A comparative study on the 3D printing process of semi-crystalline and amorphous polymers using simulation

Polymers have been widely used in the field of fused deposition modelling (FDM). The part integrity of the final printed part is affected by parameters such as processing conditions and the material properties of the polymer. Build-up of residual stresses are the main cause of shrinkage and warpage (i.e., part distortion) in the FDM parts. Among the thermoplastic polymers, semi-crystalline polymers are more prone to part distortion due to crystallisation. Therefore, it is important to understand and predict part distortion in FDM of polymers to achieve good quality prints with desirable mechanical properties. Several studies have investigated the resulting part distortion in FDM parts through empirical, analytical, and numerical approaches. In most cases, the simulation results are not quantitatively validated, mainly because the temperature dependent properties of the polymers and the crystallinity of semi-crystalline polymers are often overlooked. In this study, the thermal-mechanical properties of the polymer of study such as specific heat capacity, thermal conductivity and density and the crystallisation kinetics are invoked as a function of temperature. Furthermore, an amorphous polymer was also simulated with consideration of its respective material properties. Both the semicrystalline and the amorphous polymer models were simulated under various layer thickness (0.1 and 0.5mm), in order to investigate the effect of layer thickness on the induced thermal stress and resulting warpage. Based on the simulation results, for 0.1mm layer thickness, the amorphous polymer model exhibited a warpage drop of 77%. And for 0.5mm, the warpage noted was found to decrease by 63%, on comparison with the warpage noted from semi-crystalline polymer model. These warpage values from the simulated models were then measured against the 3D scan results of the printed samples for quantitative validation. An excellent agreement was observed between the experimental and the simulated samples.</div

Short-term Effects of Transcranial Near-Infrared Photobiomodulation on Motor Performance in Healthy Human Subjects: An Experimental Single-Blind Randomized Clinical Trial

Introduction: Transcranial near-infrared photobiomodulation (NIR-PBM) is a new noninvasive procedure which transcranially applies a near-infrared wavelength to the scalp with a laser or a light-emitting diode (LED) source. Improvement in the neurological or psychological symptoms has been reported following light irradiation. However, to our knowledge, there is no study to investigate the effects of transcranial NIR-PBM on motor performance directly. Therefore, the objective of this study was to investigate the short-term effects of transcranial NIR-PBM on motor performance in healthy human subjects.Methods: In this experimental single-blind randomized clinical trial study, 56 right-handed healthy participants, whose ages ranged from 18 to 30, were randomly assigned to (1) Real transcranial NIR-PBMC3 group (n=14), (2) Sham transcranial NIR-PBMC3 group (n=14), (3) Real transcranial NIR-PBMC4 group (n=14), and (4) Sham transcranial NIR-PBMC4 group (n=14). We applied the 808 nm laser with irradiation energy density of 60 J/cm2 and power density of 200 mw/cm2 to the C3 or C4 points of the scalp. The number of finger taps as an indicator of motor performance was assessed by the finger-tapping test (FTT) before and after irradiation of transcranial NIR-PBM on the corresponding points of the scalp for 5 minutes.Results: The results showed that the number of finger taps in both right and left hands following the use of transcranial NIR-PBM in the real transcranial NIR-PBMC3 group significantly increased (P &lt; 0.05).Conclusion: We concluded that using transcranial NIR-PBM with a laser source on C3 point of the motor cortex in right-handed healthy people can increase the number of finger taps in both hands as an indicator of motor performance improvement

Reviving Northern Ireland’s Textile Heritage

This Ulster University (UU) research project was performed in partnership with the Northern Ireland Advanced Composites and Engineering (NIACE) Centre. It complements previous works performed in conjunction with, the National Composites Centre (NCC), Bristol, the Advanced Manufacturing Research Centre (AMRC), University of Sheffield and the NIACE Centre, Belfast, to establish an Advanced Preforming Centre of Excellence in Northern Ireland. The authors are fully engaged in ongoing projects such as, the Advanced Manufacturing Innovation Centre (AMIC) Belfast Region City Deal and ‘HyTech NI’ a joint project between UU, Queen’s University Belfast, Mid and East Antrim Borough Council (MEABC) and industry underpinned by £15million in funding from the Northern Ireland Complementary Funding to support investment in the Hydrogen Economy. The economic, societal and environmental benefits of these projects and those proposed within this report, align with the Northern Ireland Executive’s Programme for Government (PfG) 2021 (1), have the required level of innovation and ambition along with scope to offer economic sustainability and inclusivity, to meet the tiered metrics for the Department for the Economy’s (DfE’s) Economic Vision of a ‘10X Economy’ (2), in addition, to aiding Northern Ireland on its trajectory to ‘Net Zero by 2050’ as part of The Climate Change Act (Northern Ireland) 2022 (3), with the latter aligning with the Northern Ireland Executive’s Energy Strategy – The Path to Net Zero Energy (4).A case for reviving Northern Ireland’s textile heritage was established by the authors following their assessment of the textiles and textile composites markets. The authors are confident in their assessment and understanding of the current market presence, along with present-day market challenges, derived from (i) the public domain such as, research articles and press releases, (ii) purchased sources such as, market research reports, (iii) focused market networking they partake in and the industry/stakeholder input they receive from primary interviews, workshops, conferencing, including industry visits and secondments to relevant organisations and, (iv) maintaining an active presence in the textiles and textile composites research and commercial spaces. <br/

A natural fibre supply chain in Northern Ireland

This research project was performed by Ulster University (UU) in partnership with the Northern Ireland Advanced Composites and Engineering (NIACE) Centre. It complements two other research projects running in parallel with each another, (1) Reviving Northern Ireland’s Textile Heritage and (2) A Green Carbon Fibre Opportunity in Northern Ireland. The projects have their own specific agenda but equally complement one another due to sharing common market streams and therefore, being challenged by the same market difficulties. Individually and in combination there is alignment to the Northern Ireland Executive’s Programme for Government (PfG) 2021 (1).The economic, societal and environmental benefits of these projects and those proposed within this report, have (1) the required level of innovation and ambition along with scope to offer economic sustainability and inclusivity, to meet the tiered metrics for the Department for the Economy’s (DfE’s) Economic Vision of a ‘10X Economy’ (2), in addition to, aiding Northern Ireland on its trajectory to ‘Net Zero by 2050’ as part of The Climate Change Act (Northern Ireland) 2022 (3), with the latter aligning with the Northern Ireland Energy Strategy – The Path to Net Zero Energy (4). This project supports the Department of Agriculture, Environment and Rural Affairs’ (DAERA's) Green Growth Strategy (5) and the Northern Ireland Energy Strategy’s vision to decarbonise, create new jobs and grow a green skills base (4).A case for a natural fibre opportunity in Northern Ireland was established by the authors following their assessment of different fibre supply markets, and for their products too. Given the expertise at UU/NIACE for researching and commercialising high performance fibre-based composites, the report focuses on opportunities for utilising natural fibres in composites to create a more sustainable composite offering than is currently the case with traditional composites of carbon or glass fibre. The authors are confident in their assessment and understanding of the markets, along with present-day market challenges, derived from (i) the public domain such as, research articles and press releases, (ii) purchased sources such as, market research reports, (iii) focused market networking, (iv) industry input, industry visits and secondments to relevant organisations and, (v) maintaining an active presence in the supply markets for fibres and their products, at both research and commercial levels.<br/

Neural-based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions. We chose NASA's SDO mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of $\sim0.1$ bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.Comment: Accepted for publication in IEEE Transactions on Aerospace and Electronic Systems (TAES). arXiv admin note: text overlap with arXiv:2210.0647

Interfering with DNA Decondensation as a Strategy Against Mycobacteria

Tuberculosis is once again a major global threat, leading to more than 1 million deaths each year. Treatment options for tuberculosis patients are limited, expensive and characterized by severe side effects, especially in the case of multidrug-resistant forms. Uncovering novel vulnerabilities of the pathogen is crucial to generate new therapeutic strategies. Using high resolution microscopy techniques, we discovered one such vulnerability of Mycobacterium tuberculosis. We demonstrate that the DNA of M. tuberculosis can condense under stressful conditions such as starvation and antibiotic treatment. The DNA condensation is reversible and specific for viable bacteria. Based on these observations, we hypothesized that blocking the recovery from the condensed state could weaken the bacteria. We showed that after inducing DNA condensation, and subsequent blocking of acetylation of DNA binding proteins, the DNA localization in the bacteria is altered. Importantly under these conditions, Mycobacterium smegmatis did not replicate and its survival was significantly reduced. Our work demonstrates that agents that block recovery from the condensed state of the nucleoid can be exploited as antibiotic. The combination of fusidic acid and inhibition of acetylation of DNA binding proteins, via the Eis enzyme, potentiate the efficacy of fusidic acid by 10 and the Eis inhibitor to 1,000-fold. Hence, we propose that successive treatment with antibiotics and drugs interfering with recovery from DNA condensation constitutes a novel approach for treatment of tuberculosis and related bacterial infections