Medium Frequency Range Analysis in non-homogeneous slender structures

Numerous industrial processes require wave propagation characteristics in slender bodies, e.g. beams, plates or shells, especially for aircraft structures inspection. Non Destructive Inspection for critical applications and damage identification in complex structures such as composite laminates uses guided waves to assess their quality. Wave propagation in homogeneous or multi-layered plates with constant characteristics has already been reported, so the study is now extended to multilayered plates or shells with continuous damage which alters mechanical properties distribution. Wave propagation paths are then modified, inducing energy concentration depending on both kind and extend of the defect. A predictive semi-analytical tool is here developed using a specific asymptotic expansion based on W.K,B.J. approach to investigate wave propagation in heterogeneous slender structures in the medium frequency range, since wavelength has to be short compared to the characteristic size of the structure and defect. It has been implemented in a general purpose Medium Frequency Range Analysis software. Its principal aim is to simulate propagation paths for plates or shells with distributed defect, determine energy concentration zones, forbidden zones, and distribution of mode shapes and stresses in the structure for identification of damage from experiments results. Finite elements models validate the relation between damage and energy concentration

Innovation in Tigernut (Cyperus Esculentus L.) milk production: in situ hydrolysis of starch

Tigernut tubers (Cyperus esculentus) are used for the production of vegetable milk, commonly known as “Horchata de chufa” in Spain. The presence of starch in the tuber limits the yield of the milk, since this carbohydrate gelatinizes during the pasteurization of the milk and leads to the considerable solidification of this drink. The present work aims to improve the yields and extraction practice of the milk by an in situ hydrolysis of starch, using exogenous amylases of industrial or vegetable origin. The obtained results show that sprouting improves the extraction yields of tigernut milk, which goes from 50% to about 70%. This improvement in milk yield corresponds to a hydrolysis of about 35% of the starch in the tuber. The use of exogenous amylases leads to starch hydrolysis rates of 45% and 70%, respectively, for amylolytic extracts from sprouted tigernut tubers and amylase, with the corollary of a natural increase in the sweetness of milk. This technical approach makes it possible to produce a naturally sweetened tigernut milk which easily lends itself to pasteurization without a significant increase in viscosity

Analysis of the accuracy of actuation electronics in the laser interferometer space antenna pathfinder

The Laser Interferometer Space Antenna Pathfinder (LPF) main observable, labeled Δg, is the differential force per unit mass acting on the two test masses under free fall conditions after the contribution of all non-gravitational forces has been compensated. At low frequencies, the differential force is compensated by an applied electrostatic actuation force, which then must be subtracted from the measured acceleration to obtain Δg. Any inaccuracy in the actuation force contaminates the residual acceleration. This study investigates the accuracy of the electrostatic actuation system and its impact on the LPF main observable. It is shown that the inaccuracy is mainly caused by the rounding errors in the waveform processing and also by the random error caused by the analog to digital converter random noise in the control loop. Both errors are one order of magnitude smaller than the resolution of the commanded voltages. We developed a simulator based on the LPF design to compute the close-to-reality actuation voltages and, consequently, the resulting actuation forces. The simulator is applied during post-processing the LPF data

A histologic scoring system for prognosis of patients with Alcoholic hepatitis

BACKGROUND & AIMS: There is no histologic classification system to determine prognoses of patients with alcoholic hepatitis (AH). We identified histologic features associated with disease severity and created a histologic scoring system to predict short-term (90-day) mortality. METHODS: We analyzed data from 121 patients admitted to the Liver Unit (Hospital Clinic, Barcelona, Spain) from January 2000 to January 2008 with features of AH and developed a histologic scoring system to determine the risk of death using logistic regression. The system was tested and updated in a test set of 96 patients from 5 academic centers in the United States and Europe, and a semiquantitative scoring system called the Alcoholic Hepatitis Histologic Score (AHHS) was developed. The system was validated in an independent set of 109 patients. Interobserver agreement was evaluated by weighted κ statistical analysis. RESULTS: The degree of fibrosis, degree of neutrophil infiltration, type of bilirubinostasis, and presence of megamitochondria were independently associated with 90-day mortality. We used these 4 parameters to develop the AHHS to identify patients with a low (0-3 points), moderate (4-5 points), or high (6-9 points) risk of death within 90 days (3%, 19%, and 51%, respectively; P < .0001). The AHHS estimated 90-day mortality in the training and test sets with an area under the receiver operating characteristic value of 0.77 (95% confidence interval, 0.71-0.83). Interrater agreement values were 0.65 for fibrosis, 0.86 for bilirubinostasis, 0.60 for neutrophil infiltration, and 0.46 for megamitochondria. Interestingly, the type of bilirubinostasis predicted the development of bacterial infections. CONCLUSIONS: We identified histologic features associated with the severity of AH and developed a patient classification system that might be used in clinical decision making

Mental impact of Covid-19 among Spanish healthcare workers. A large longitudinal survey

Altres ajuts: Fondo Europeo de Desarrollo Regional (FEDER); Ministerio de Ciencia e Innovación; Gerencia Regional de Salud de Castilla y León (SACYL, GRS COVID 32/A/20).Aims Longitudinal data on the mental health impact of the coronavirus disease 2019 (Covid-19) pandemic in healthcare workers is limited. We estimated prevalence, incidence and persistence of probable mental disorders in a cohort of Spanish healthcare workers (Covid-19 waves 1 and 2) -and identified associated risk factors. Methods 8996 healthcare workers evaluated on 5 May-7 September 2020 (baseline) were invited to a second web-based survey (October-December 2020). Major depressive disorder (PHQ-8 ≥ 10), generalised anxiety disorder (GAD-7 ≥ 10), panic attacks, post-traumatic stress disorder (PCL-5 ≥ 7), and alcohol use disorder (CAGE-AID ≥ 2) were assessed. Distal (pre-pandemic) and proximal (pandemic) risk factors were included. We estimated the incidence of probable mental disorders (among those without disorders at baseline) and persistence (among those with disorders at baseline). Logistic regression of individual-level [odds ratios (OR)] and population-level (population attributable risk proportions) associations were estimated, adjusting by all distal risk factors, health care centre and time of baseline interview. Results 4809 healthcare workers participated at four months follow-up (cooperation rate = 65.7%; mean = 120 days s.d. = 22 days from baseline assessment). Follow-up prevalence of any disorder was 41.5%, (v. 45.4% at baseline, p < 0.001); incidence, 19.7% (s.e. = 1.6) and persistence, 67.7% (s.e. = 2.3). Proximal factors showing significant bivariate-adjusted associations with incidence included: work-related factors [prioritising Covid-19 patients (OR = 1.62)], stress factors [personal health-related stress (OR = 1.61)], interpersonal stress (OR = 1.53) and financial factors [significant income loss (OR = 1.37)]. Risk factors associated with persistence were largely similar. Conclusions Our study indicates that the prevalence of probable mental disorders among Spanish healthcare workers during the second wave of the Covid-19 pandemic was similarly high to that after the first wave. This was in good part due to the persistence of mental disorders detected at the baseline, but with a relevant incidence of about 1 in 5 of HCWs without mental disorders during the first wave of the Covid-19 pandemic. Health-related factors, work-related factors and interpersonal stress are important risks of persistence of mental disorders and of incidence of mental disorders. Adequately addressing these factors might have prevented a considerable amount of mental health impact of the pandemic among this vulnerable population. Addressing health-related stress, work-related factors and interpersonal stress might reduce the prevalence of these disorders substantially. Study registration number: NCT0455656