The multifaceted musculoskeletal hydatid disease.

Musculoskeletal hydatid disease is rare and can be located anywhere but most commonly the bone and muscles of the spine, pelvis, then the lower limbs. Imaging is essential for its diagnosis, performing the pre-therapeutic assessment, guiding possible percutaneous treatments, and providing post-therapeutic follow-up. Musculoskeletal hydatidosis can take several forms that may suggest other infections and tumors or pseudotumors. MRI and CT are superior for its diagnosis but ultrasound and radiography remain the most accessible examinations in developing countries where this parasitosis is endemic. In this review, we provide an overview of this disease and describe its different imaging patterns in soft tissue and bone involvement that should be sought to support the diagnosis.

Describing and Modeling Rough Composites Surfaces by Using Topological Data Analysis and Fractional Brownian Motion.

Many composite manufacturing processes employ the consolidation of pre-impregnated preforms. However, in order to obtain adequate performance of the formed part, intimate contact and molecular diffusion across the different composites' preform layers must be ensured. The latter takes place as soon as the intimate contact occurs and the temperature remains high enough during the molecular reptation characteristic time. The former, in turn, depends on the applied compression force, the temperature and the composite rheology, which, during the processing, induce the flow of asperities, promoting the intimate contact. Thus, the initial roughness and its evolution during the process, become critical factors in the composite consolidation. Processing optimization and control are needed for an adequate model, enabling it to infer the consolidation degree from the material and process features. The parameters associated with the process are easily identifiable and measurable (e.g., temperature, compression force, process time, ⋯). The ones concerning the materials are also accessible; however, describing the surface roughness remains an issue. Usual statistical descriptors are too poor and, moreover, they are too far from the involved physics. The present paper focuses on the use of advanced descriptors out-performing usual statistical descriptors, in particular those based on the use of homology persistence (at the heart of the so-called topological data analysis-TDA), and their connection with fractional Brownian surfaces. The latter constitutes a performance surface generator able to represent the surface evolution all along the consolidation process, as the present paper emphasizes.

Parametric Analysis of Thick FGM Plates Based on 3D Thermo-Elasticity Theory: A Proper Generalized Decomposition Approach.

In the present work, the general and well-known model reduction technique, PGD (Proper Generalized Decomposition), is used for parametric analysis of thermo-elasticity of FGMs (Functionally Graded Materials). The FGMs have important applications in space technologies, especially when a part undergoes an extreme thermal environment. In the present work, material gradation is considered in one, two and three directions, and 3D heat transfer and theory of elasticity equations are solved to have an accurate temperature field and be able to consider all shear deformations. A parametric analysis of FGM materials is especially useful in material design and optimization. In the PGD technique, the field variables are separated to a set of univariate functions, and the high-dimensional governing equations reduce to a set of one-dimensional problems. Due to the curse of dimensionality, solving a high-dimensional parametric problem is considerably more computationally intensive than solving a set of one-dimensional problems. Therefore, the PGD makes it possible to handle high-dimensional problems efficiently. In the present work, some sample examples in 4D and 5D computational spaces are solved, and the results are presented.

MRI of squamous cell carcinoma of the nail apparatus: report of 6 cases.

Malignant tumors of the nail apparatus are rare and dominated by squamous cell carcinomas (SCC). Routinely, their pre-therapy imaging is limited to radiography. Our purpose is to determine the MRI characteristics in the locoregional assessment of SCC of the nail apparatus through a series of 6 consecutive cases explored by MRI and operated, carried out over a period of 12 years. IRB approval was obtained. Two in situ and 4 invasive squamous cell carcinomas were found, sex ratio was 0.5, and the age was 55 ± 10 years (mean ± SD). Most tumors showed specific signal behavior different from that of the epidermis and dermis with high signal on T2wi (5/6) and complete or partial enhancement (6/6). The mean thickness was 3.4 mm. The deep margin of the tumor with the dermis was always well defined for Bowen's disease (2/2) and blurred for invasive SCC. Localization involved the nail bed epithelium in all cases. Changes of the nail plate were detectable. Extension to lateral and posterior folds, hyponychium, cul-de-sac matrix, deep dermis, and bone was determined. MRI could be proposed as preoperative imaging of squamous cell carcinoma for locoregional assessment and guide biopsy.

A parametric metamodel of the vehicle frontal structure accounting for material properties and strain-rate effect: application to full frontal rigid barrier crash test.

In the automotive industry, building parametric surrogate models is a fundamental tool to evaluate, in real time, the performance of newly designed car components. Such models allow to compute any Quantity of Interest -QoI-, such as a specific safety protocol index, for any choice of material and/or geometrical parameters characterizing the component, within the stringent real time constraint. For instance, they can be exploited to guarantee safer designs (e.g., maximizing energy absorption by the crash boxes) or to reduce manufacturing costs (e.g., minimizing the mass of a specific structure under some safety protocol constraints). In general, these parametric simulation tools allow a significant gain in terms of manufacturing costs and time delays during the investigation phase. In this study, we focus on the vehicle frontal structure system considering its performance in a full-frontal crash scenario. In the front structure system we parameterize the crash boxes (left and right) and the inner/outer side front members (left and right, front and rear) with respect to the part thickness and the material parameters characterizing the Krupkowski plasticity curve. Moreover, Strain Rate Effect is also taken into account via Neural Network based regressions, whose training dataset comes from experimental data. The parametric metamodel is built via Non-Intrusive PGD -NI-PGD- strategies, relying on a sparse sampling of the parametric space, and allowing a quite reduced number of High Fidelity -HiFi- simulations. A novel strategy based on clustering and classification, known as Multi-PGD, is also applied and numerically verified.

Introduction for the Special Issue: Statistical Fluid Dynamics.

Characterizing complex material consists in establishing the relationship between flow rheology during forming processes and the induced micro-structural state that affects directly the final mechanical properties of the formed parts [...].

Transient modifications of the olfactory bulb on MR follow-up of COVID-19 patients with related olfactory dysfunction.

BACKGROUND: Olfactory dysfunction (OD) has been reported with a high prevalence on mild to moderate COVID-19 patients. Previous reports suggest that volume and signal intensity of olfactory bulbs (OB) have been reported as abnormal on acute phase of COVID-19 anosmia, but a prospective MRI and clinical follow-up study of COVID-19 patients presenting with OD was missing, aiming at understanding the modification of OB during patients'follow-up. METHODS: A prospective multicenter study was conducted including 11 COVID-19 patients with OD. Patients underwent MRI and psychophysical olfactory assessments at baseline and 6-month post-COVID-19. T2 FLAIR-Signal intensity ratio (SIR) was measured between the average signal of the OB and the average signal of white matter. OB volumes and obstruction of olfactory clefts (OC) were evaluated at both evaluation times. RESULTS: The psychophysical evaluations demonstrated a 6-month recovery in 10/11 patients (90.9%). The mean values of OB-SIR significantly decreased from baseline (1.66±0.24) to 6-month follow-up (1.35±0.27), reporting a mean variation of -17.82±15.20 % (p<0.001). The mean values of OB volumes significantly decreased from baseline (49.22±10.46 mm3) to 6-month follow-up (43.70±9.88 mm3), (p=0.006). CONCLUSION: Patients with demonstrated anosmia reported abnormalities in OB imaging that may be objectively evaluated with the measurement of SIR and OB volumes. SIR and OB volumes significantly normalized when patient recovered smell. This supports the underlying mechanism of a transient inflammation of the OB as a cause of Olfactory Dysfunction in COVID-19 patients.

Data-Driven Modelling of Polyethylene Recycling under High-Temperature Extrusion.

Two main problems are studied in this article. The first one is the use of the extrusion process for controlled thermo-mechanical degradation of polyethylene for recycling applications. The second is the data-based modelling of such reactive extrusion processes. Polyethylenes (high density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE)) were extruded in a corotating twin-screw extruder under high temperatures (350 °C < T < 420 °C) for various process conditions (flow rate and screw rotation speed). These process conditions involved a decrease in the molecular weight due to degradation reactions. A numerical method based on the Carreau-Yasuda model was developed to predict the rheological behaviour (variation of the viscosity versus shear rate) from the in-line measurement of the die pressure. The results were successfully compared to the viscosity measured from offline measurement assuming the Cox-Merz law. Weight average molecular weights were estimated from the resulting zero-shear rate viscosity. Furthermore, the linear viscoelastic behaviours (Frequency dependence of the complex shear modulus) were also used to predict the molecular weight distributions of final products by an inverse rheological method. Size exclusion chromatography (SEC) was performed on five samples, and the resulting molecular weight distributions were compared to the values obtained with the two aforementioned techniques. The values of weight average molecular weights were similar for the three techniques. The complete molecular weight distributions obtained by inverse rheology were similar to the SEC ones for extruded HDPE samples, but some inaccuracies were observed for extruded UHMWPE samples. The Ludovic® (SC-Consultants, Saint-Etienne, France) corotating twin-screw extrusion simulation software was used as a classical process simulation. However, as the rheo-kinetic laws of this process were unknown, the software could not predict all the flow characteristics successfully. Finally, machine learning techniques, able to operate in the low-data limit, were tested to build predicting models of the process outputs and material characteristics. Support Vector Machine Regression (SVR) and sparsed Proper Generalized Decomposition (sPGD) techniques were chosen to predict the process outputs successfully. These methods were also applied to material characteristics data, and both were found to be effective in predicting molecular weights. More precisely, the sPGD gave better results than the SVR for the zero-shear viscosity prediction. Stochastic methods were also tested on some of the data and showed promising results.

Proper Generalized Decomposition for Parametric Study and Material Distribution Design of Multi-Directional Functionally Graded Plates Based on 3D Elasticity Solution.

The use of mesh-based numerical methods for a 3D elasticity solution of thick plates involves high computational costs. This particularly limits parametric studies and material distribution design problems because they need a large number of independent simulations to evaluate the effects of material distribution and optimization. In this context, in the current work, the Proper Generalized Decomposition (PGD) technique is adopted to overcome this difficulty and solve the 3D elasticity problems in a high-dimensional parametric space. PGD is an a priori model order reduction technique that reduces the solution of 3D partial differential equations into a set of 1D ordinary differential equations, which can be solved easily. Moreover, PGD makes it possible to perform parametric solutions in a unified and efficient manner. In the present work, some examples of a parametric elasticity solution and material distribution design of multi-directional FGM composite thick plates are presented after some validation case studies to show the applicability of PGD in such problems.

Effect of Shear Flow on Nanoparticles Migration near Liquid Interfaces.

The effect of shear flow on spherical nanoparticles (NPs) migration near a liquid-liquid interface is studied by numerical simulation. We have implemented a compact model through which we use the diffuse interface method for modeling the two fluids and the molecular dynamics method for the simulation of the motion of NPs. Two different cases regarding the state of the two fluids when introducing the NPs are investigated. First, we introduce the NPs randomly into the medium of the two immiscible liquids that are already separated, and the interface is formed between them. For this case, it is shown that before applying any shear flow, 30% of NPs are driven to the interface under the effect of the drag force resulting from the composition gradient between the two fluids at the interface. However, this percentage is increased to reach 66% under the effect of shear defined by a Péclet number Pe = 0.316. In this study, different shear rates are investigated in addition to different shearing times, and we show that both factors have a crucial effect regarding the migration of the NPs toward the interfacial region. In particular, a small shear rate applied for a long time will have approximately the same effect as a greater shear rate applied for a shorter time. In the second studied case, we introduce the NPs into the mixture of two fluids that are already mixed and before phase separation so that the NPs are introduced into the homogenous medium of the two fluids. For this case, we show that in the absence of shear, almost all NPs migrate to the interface during phase separation, whereas shearing has a negative result, mainly because it affects the phase separation.

Viscosity and Rheological Properties of Graphene Nanopowders Nanofluids.

The dynamic viscosity and rheological properties of two different non-aqueous graphene nano-plates-based nanofluids are experimentally investigated in this paper, focusing on the effects of solid volume fraction and shear rate. For each nanofluid, four solid volume fractions have been considered ranging from 0.1% to 1%. The rheological characterization of the suspensions was performed at 20 ∘C, with shear rates ranging from 10-1s-1 to 103s-1, using a cone-plate rheometer. The Carreau-Yasuda model has been successfully applied to fit most of the rheological measurements. Although it is very common to observe an increase of the viscosity with the solid volume fraction, we still found here that the addition of nanoparticles produces lubrication effects in some cases. Such a result could be very helpful in the domain of heat extraction applications. The dependence of dynamic viscosity with graphene volume fraction was analyzed using the model of Vallejo et al.

Reduction of the chemical master equation for gene regulatory networks using proper generalized decompositions.

The numerical solution of the chemical master equation (CME) governing gene regulatory networks and cell signaling processes remains a challenging task owing to its complexity, exponentially growing with the number of species involved. Although most of the existing techniques rely on the use of Monte Carlo-like techniques, we present here a new technique based on the approximation of the unknown variable (the probability of having a particular chemical state) in terms of a finite sum of separable functions. In this framework, the complexity of the CME grows only linearly with the number of state space dimensions. This technique generalizes the so-called Hartree approximation, by using terms as needed in the finite sums decomposition for ensuring convergence. But noteworthy, the ease of the approximation allows for an easy treatment of unknown parameters (as is frequently the case when modeling gene regulatory networks, for instance). These unknown parameters can be considered as new space dimensions. In this way, the proposed method provides solutions for any value of the unknown parameters (within some interval of arbitrary size) in one execution of the program.