Evaluating the exit pressure method for measurements of normal stress difference at high shear rates

A challenge for polymer rheology is the reliable determination of shear dependent first normal stress difference (N-1 values) at high shear rates (>10 s(-1)). Here, we evaluate the correctness of the commonly applied exit pressure method focusing on polypropylene and high and low density polyethylene melts at 200 degrees C. It is demonstrated that the linear extrapolation of pressure values toward the die exit, which is a key step in the application of the exit pressure method, is affordable to determine N-1 values despite that these extrapolated exit pressure values are characterized by a relative deviation of 25%-40%. The validity of the exit pressure method is further supported by an excellent match with rheological data from the Laun rule (exponent close to 0.7) and a representative simulation of extrudate swelling data in the width and height direction, considering tuned parameters for the Phan-Thien-Tanner constitutive model. Also, the absence of a significant viscous heating effect near the die exit is highlighted based on numerical analysis. (c) 2020 The Society of Rheology

Bio-material polylactic acid/poly(butylene adipate-co-terephthalate) blend developed for extrusion- based additive manufacturing

Bio-material polylactic acid and poly(butylene adipate-co-terephthalate) were blended to achieve increased ductility of the blend. Cloisite was added to improve the stiffness of the blend. The blends were made into filament suitable for extrusion-based additive manufacturing. Melt flow index of the filament and mechanical properties of the printed bars were tested. Preliminary results showed that the melt flow index increases significantly with cloisite and the modulus of polylactic acid/poly(butylene adipate-co-terephthalate) improved slightly. The notched impact strength of the blend increased with increasing content of cloisite, and it increased significantly after annealing, especially for blends without cloisite

Three-dimensional flow simulations for polymer extrudate swell out of slit dies from low to high aspect ratios

The impact of the slit die geometry and the polymer melt flow characteristics on the extrudate swell behavior, which is a key extrusion operating parameter, is highlighted. Three-dimensional (3D) numerical simulations based on the finite element method are compared with their conventional two-dimensional (2D) counterparts at the same apparent shear rates using ANSYS Polyflow software. The rheological behavior is described by the differential multimode Phan-Thien-Tanner constitutive model, with polypropylene as a reference. It is shown that increasing the aspect ratio of the die geometry (width/height ratio variation from 1 to 20) contributes to a significant change in the 3D extrudate deformation (relative changes of 10% in several directions; absolute changes up to 30%) and delays the equilibrium axial position (up to a factor 10). High aspect ratios induce a switch to contract flow (swell ratio <1) for the edge height swell. The 3D extrudate swell strongly deviates from the 2D simplified case due to the die effect near the wall, even for higher aspect ratios. Also a different relation with the material parameters is recorded. The initially large swell behavior is followed by a small shrinkage flow in the middle height direction which cannot be captured by the 2D counterpart. The findings are supported by a comprehensive analysis of the velocity and stress fields in and out of the slit dies

Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings

The aims of this work were to create a robust automatic software tool for measurement of the levator hiatal area on transperineal ultrasound (TPUS) volumes and to measure the potential reduction in variability and time taken for analysis in a clinical setting. The proposed tool automatically detects the C-plane (i.e., the plane of minimal hiatal dimensions) from a 3-D TPUS volume and subsequently uses the extracted plane to automatically segment the levator hiatus, using a convolutional neural network. The automatic pipeline was tested using 73 representative TPUS volumes. Reference hiatal outlines were obtained manually by two experts and compared with the pipeline's automated outlines. The Hausdorff distance, area, a clinical quality score, C-plane angle and C-plane Euclidean distance were used to evaluate C-plane detection and quantify levator hiatus segmentation accuracy. A visual Turing test was created to compare the performance of the software with that of the expert, based on the visual assessment of C-plane and hiatal segmentation quality. The overall time taken to extract the hiatal area with both measurement methods (i.e., manual and automatic) was measured. Each metric was calculated both for computer–observer differences and for inter-and intra-observer differences. The automatic method gave results similar to those of the expert when determining the hiatal outline from a TPUS volume. Indeed, the hiatal area measured by the algorithm and by an expert were within the intra-observer variability. Similarly, the method identified the C-plane with an accuracy of 5.76 ± 5.06° and 6.46 ± 5.18 mm in comparison to the inter-observer variability of 9.39 ± 6.21° and 8.48 ± 6.62 mm. The visual Turing test suggested that the automatic method identified the C-plane position within the TPUS volume visually as well as the expert. The average time taken to identify the C-plane and segment the hiatal area manually was 2 min and 35 ± 17 s, compared with 35 ± 4 s for the automatic result. This study presents a method for automatically measuring the levator hiatal area using artificial intelligence-based methodologies whereby the C-plane within a TPUS volume is detected and subsequently traced for the levator hiatal outline. The proposed solution was determined to be accurate, relatively quick, robust and reliable and, importantly, to reduce time and expertise required for pelvic floor disorder assessment

Unravelling the wavelength dependency of anthracene photoinitiated reactions

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DMRN+18: Digital Music Research Network One-day Workshop 2023

DMRN+18: Digital Music Research Network One-day Workshop 2023 Queen Mary University of London Tuesday 19th December 2023 • Keynote speaker: Stefan Bilbao The Digital Music Research Network (DMRN) aims to promote research in the area of digital music, by bringing together researchers from UK and overseas universities, as well as industry, for its annual workshop. The workshop will include invited and contributed talks and posters. The workshop will be an ideal opportunity for networking with other people working in the area. Keynote speakers: Stefan Bilbao Tittle: Physics-based Audio: Sound Synthesis and Virtual Acoustics. Abstract: Any acoustically-produced sound produced must be the result of physical laws that describe the dynamics of a given system---always at least partly mechanical, and sometimes with an electronic element as well. One approach to the synthesis of natural acoustic timbres, thus, is through simulation, often referred to in this context as physical modelling, or physics-based audio. In this talk, the principles of physics-based audio, and the various different approaches to simulation are described, followed by a set of examples covering: various musical instrument types; the important related problem of the emulation of room acoustics or “virtual acoustics”; the embedding of instruments in a 3D virtual space; electromechanical effects; and also new modular instrument designs based on physical laws, but without a counterpart in the real world. Some more technical details follow, including the strengths, weaknesses and limitations of such methods, and pointers to some links to data-centred black-box approaches to sound generation and effects processing. The talk concludes with some musical examples and recent work on moving such algorithms to a real-time setting.. Bio: Stefan is a Professor (full) at Reid School of Music, University of Edinburgh, he is the Personal Chair of Acoustics and Audio Signal Processing, Music. He currently works on computational acoustics, for applications in sound synthesis and virtual acoustics. Special topics of interest include: Finite difference time domain methods, distributed nonlinear systems such as strings and plates, architectural acoustics, spatial audio in simulation, multichannel sound synthesis, and hardware and software realizations. More information on: https://www.acoustics.ed.ac.uk/group-members/dr-stefan-bilbao/ DMRN+18 is sponsored by The UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM); a leading PhD research programme aimed at the Music/Audio Technology and Creative Industries, based at Queen Mary University of London

Reducing the number of accepted species in Aspergillus series Nigri

The Aspergillus series Nigri contains biotechnologically and medically important species. They can produce hazardous mycotoxins, which is relevant due to the frequent occurrence of these species on foodstuffs and in the indoor environment. The taxonomy of the series has undergone numerous rearrangements, and currently, there are 14 species accepted in the series, most of which are considered cryptic. Species-level identifications are, however, problematic or impossible for many isolates even when using DNA sequencing or MALDI-TOF mass spectrometry, indicating a possible problem in the definition of species limits or the presence of undescribed species diversity. To re-examine the species boundaries, we collected DNA sequences from three phylogenetic markers (benA, CaM and RPB2) for 276 strains from series Nigri and generated 18 new whole-genome sequences. With the three-gene dataset, we employed phylogenetic methods based on the multispecies coalescence model, including four single-locus methods (GMYC, bGMYC, PTP and bPTP) and one multilocus method (STACEY). From a total of 15 methods and their various settings, 11 supported the recognition of only three species corresponding to the three main phylogenetic lineages: A. niger, A. tubingensis and A. brasiliensis. Similarly, recognition of these three species was supported by the GCPSR approach (Genealogical Concordance Phylogenetic Species Recognition) and analysis in DELINEATE software. We also showed that the phylogeny based on benA, CaM and RPB2 is suboptimal and displays significant differences from a phylogeny constructed using 5 752 single-copy orthologous proteins; therefore, the results of the delimitation methods may be subject to a higher than usual level of uncertainty. To overcome this, we randomly selected 200 genes from these genomes and performed ten independent STACEY analyses, each with 20 genes. All analyses supported the recognition of only one species in the A. niger and A. brasiliensis lineages, while one to four species were inconsistently delimited in the A. tubingensis lineage. After considering all of these results and their practical implications, we propose that the revised series Nigri includes six species: A. brasiliensis, A. eucalypticola, A. luchuensis (syn. A. piperis), A. niger (syn. A. vinaceus and A. welwitschiae), A. tubingensis (syn. A. chiangmaiensis, A. costaricensis, A. neoniger and A. pseudopiperis) and A. vadensis. We also showed that the intraspecific genetic variability in the redefined A. niger and A. tubingensis does not deviate from that commonly found in other aspergilli. We supplemented the study with a list of accepted species, synonyms and unresolved names, some of which may threaten the stability of the current taxonomy.The Czech Ministry of Health, the Charles University Research Centre program, the Czech Academy of Sciences Long-term Research Development Project, the project of Charles University Grant Agency, JST SPRING, the Foundational Biodiversity Information Programme (FBIP) of the National Research Foundation of South Africa, the Japan Society for the Promotion of Science - Postdoctoral Fellowships for Research in Japan and Grant-in-aid for a JSPS research fellow.https://www.journals.elsevier.com/studies-in-mycologyam2023BiochemistryGeneticsMicrobiology and Plant Patholog

Progressive leukoencephalopathy impairs neurobehavioral development in sialin-deficient mice

Slc17a5−/− mice represent an animal model for the infantile form of sialic acid storage disease (SASD). We analyzed genetic and histological time-course expression of myelin and oligodendrocyte (OL) lineage markers in different parts of the CNS, and related this to postnatal neurobehavioral development in these mice. Sialin-deficient mice display a distinct spatiotemporal pattern of sialic acid storage, CNS hypomyelination and leukoencephalopathy. Whereas few genes are differentially expressed in the perinatal stage (p0), microarray analysis revealed increased differential gene expression in later postnatal stages (p10–p18). This included progressive upregulation of neuroinflammatory genes, as well as continuous down-regulation of genes that encode myelin constituents and typical OL lineage markers. Age-related histopathological analysis indicates that initial myelination occurs normally in hindbrain regions, but progression to more frontal areas is affected in Slc17a5−/− mice. This course of progressive leukoencephalopathy and CNS hypomyelination delays neurobehavioral development in sialin-deficient mice. Slc17a5−/− mice successfully achieve early neurobehavioral milestones, but exhibit progressive delay of later-stage sensory and motor milestones. The present findings may contribute to further understanding of the processes of CNS myelination as well as help to develop therapeutic strategies for SASD and other myelination disorders