The effect of the process on mechanical properties of polylactic acid - date palm leaf fibers composite films produced by extrusion blowing

Biocomposite films prepared with melt compounding and film blowing have become a new trend in plastic research to deliver more eco-friendly packages. Polylactic acid (PLA) was melt compounded with minimally processed date palm leaf fiber (DPLF) and converted into films by blown film extrusion. The compounding was done in order to enhance the film mechanical properties in one hand, and to decrease the film production cost in the other hand. In this present study, a reference PLA film and films with 1%, 2%, and 5% of DPLF (weight %) were produced with different process parameters. The spatial variations in films thickness and lay flat width indicate that the addition of DPLF up to 2% enhances the bubble stability for the tested process parameters. However, the composite with 5% DPLF shows nearly the same processability window as the neat PLA. The structural and mechanical characterizations of films suggest a reinforcing effect of the PLA matrix up to 2% of fiber (with an optimum at 1%). Larger DPLF loading leads to depressed and more anisotropic mechanical properties, related to an increased density of defects at the fiber-PLA fragile interface and to a DPLF-induced enhanced PLA thermal degradation and amorphous phase orientatio

Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework

International audienceIdentifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool

Sleep Quality and Physical Activity as Predictors of Mental Wellbeing Variance in Older Adults during COVID-19 Lockdown:ECLB COVID-19 International Online Survey

Background. The COVID-19 lockdown could engender disruption to lifestyle behaviors, thus impairing mental wellbeing in the general population. This study investigated whether sociodemographic variables, changes in physical activity, and sleep quality from pre- to during lockdown were predictors of change in mental wellbeing in quarantined older adults. Methods. A 12-week international online survey was launched in 14 languages on 6 April 2020. Forty-one research institutions from Europe, Western-Asia, North-Africa, and the Americas, promoted the survey. The survey was presented in a differential format with questions related to responses "pre" and "during" the lockdown period. Participants responded to the Short Warwick-Edinburgh Mental Wellbeing Scale, the Pittsburgh Sleep Quality Index (PSQI) questionnaire, and the short form of the International Physical Activity Questionnaire. Results. Replies from older adults (aged &gt;55 years, n = 517), mainly from Europe (50.1%), Western-Asia (6.8%), America (30%), and North-Africa (9.3%) were analyzed. The COVID-19 lockdown led to significantly decreased mental wellbeing, sleep quality, and total physical activity energy expenditure levels (all p &lt; 0.001). Regression analysis showed that the change in total PSQI score and total physical activity energy expenditure (F-(2,F- 514) = 66.41 p &lt; 0.001) were significant predictors of the decrease in mental wellbeing from pre- to during lockdown (p &lt; 0.001, R-2: 0.20). Conclusion. COVID-19 lockdown deleteriously affected physical activity and sleep patterns. Furthermore, change in the total PSQI score and total physical activity energy expenditure were significant predictors for the decrease in mental wellbeing.</p

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

First detailed data on metazoan parasites of the rare species short beaked garfish Belone svetovidovi (Teleostei: Belonidae) from Tunisian coast, Central Mediterranean Sea

Forty five specimens of the short beaked garfish Belone svetovidovi, a rare belonid species largely confused with the garfish Belone belone from Tunisian coast Sea were examined for metazoan parasite. Nine metazoan parasites species were identified: one monogenean (Axine sp.), 4 digeneans (Lecithostaphylus retroflexus, Tergestia acanthocephala, Aponurus laguncula and Condylocotyla pilodora metacercaria), one copepod (Bomolochus bellones), one isopod (Irona nana), one acanthocephalan (Telosentis exiguus) and one nematod Hysterotylacium sp. Most of parasite species were new records for B. svetovidovi in Tunisia. In the parasite fauna of B. svetovidovi, digenean C. pilodora metacercaria was the most prevalent species (42%) followed by Monogenea Axine sp. (36%). The total length of the host did not influence parasitic infection in B. svetovidovi. The metazoan parasite composition of B. svetovidovi revealed great similarity than those of B. belone from Tunisia supporting same ecological behavior of both hosts

Porous material effect on gearbox vibration and acoustic behavior

In this paper, we define a resolution method to study the effect of a porous material on vibro-acoustic behavior of a geared transmission. A porous plate is coupled with the gearbox housing cover. The developed model depends on the gearbox characteristic and poroelastic parameters of the porous material. To study the acoustic effect of the housing cover, the acoustic transmission loss is computed by simulating numerically the elastic-porous coupled plate model, and the numerical implementation is performed by directly programming the mixed displacement-pressure formulation. To study the vibration effect, the bearing displacement is computed using a two-stage gear system dynamical model and used as the gearbox cover excitation. Numerical implementation is performed by direct programming of the Leclaire formulation

Porous material effect on gearbox vibration and acoustic behavior

In this paper, we define a resolution method to study the effect of a porous material on vibro-acoustic behavior of a geared transmission. A porous plate is coupled with the gearbox housing cover. The developed model depends on the gearbox characteristic and poroelastic parameters of the porous material. To study the acoustic effect of the housing cover, the acoustic transmission loss is computed by simulating numerically the elastic-porous coupled plate model, and the numerical implementation is performed by directly programming the mixed displacement-pressure formulation. To study the vibration effect, the bearing displacement is computed using a two-stage gear system dynamical model and used as the gearbox cover excitation. Numerical implementation is performed by direct programming of the Leclaire formulation

Bayesian sparse regularization for parallel MRI reconstruction using Complex Bernoulli-Laplace mixture priors

International audienceParallel imaging technique using several receiver coils provides a fast acquisition of magnetic resonance imaging (MRI) images with high temporal and/or spatial resolutions. Against this background, the most difficult task is the full field of view images reconstruction without noise, distortions and artifacts. In this context, SENSitivity Encoding is considered the most often used parallel MRI (pMRI) reconstruction method in the clinical application. On the one side, solving the inherent reconstruction problems has known significant progress during the last decade. On the other side, the sparse Bayesian regularization for signal/image recovery has generated a great research interest especially when large volumes of data are processed. The purpose of this paper is to develop a novel Bayesian regularization technique for sparse pMRI reconstruction. The new technique is based on a hierarchical Bayesian model using a complex Bernoulli–Laplace mixture in order to promote two sparsity levels for the target image. The inference is conducted using a Markov chain Monte Carlo sampling scheme. Simulation results obtained with both synthetic and real datasets are showing the outperformance of the proposed sparse Bayesian technique compared to other existing regularization techniques