Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models

Sea ice ridging presents a great challenge to ships navigating thearctic. In this paper, we examine the capabilities of various machinelearning methods in predicting regions of high ridge density fromSAR imagery of Hudson Strait. Our results showed that althoughridging in Hudson Strait may be difficult to distinguish even with thehuman eye, machine learning can give some insight into potentiallydangerous regions of Hudson Strait

Ultrafast Laser Inscription of Buried Waveguides in W-TCP Bioactive Eutectic Glasses

Since the first report of Davis in 1996, ultrafast laser inscription (ULI) has been widely used to fabricate buried optical devices such as active and passive waveguides inside dielectric materials. In this technique, ultra-short and ultra-intense laser pulses are tightly focused inside transparent materials leading to laser-induced nonlinear processes in the focal volume. The energy density deposited into the submicron focal volume can reach several of MJcm−3 and hence, may trigger dramatic changes in a strongly localized region, whereas the surrounding bulk material remains unchanged. This technique can be used from void formation to weak refractive index modification, which is the key feature to create buried optical waveguides. In this chapter, firstly, we review the fundamentals of the ultrafast laser inscription technique to produce optical waveguides inside dielectric materials such as crystals and glasses. Next, as an example, we revise the application of this technique to create buried waveguides inside bioactive glasses and specifically, inside W-TCP eutectic glasses

River Ice Segmentation under a Limited Compute and Annotation Budget

River ice segmentation, used to differentiate ice and water, can give valuable information regarding ice cover and ice distribution. These are important factors when evaluating flooding risks caused by ice jams that may harm local ecosystems and infrastructure. Furthermore, discriminating specifically between anchor ice and frazil ice is important in understanding sediment transport and release events that can affect geomorphology and cause landslide risks. Modern deep learning techniques have proved to deliver promising segmentation results; however, they can require hours of expensive manual image labelling, can show poor generalization ability, and can be inefficient when hardware and computing power are limited. As river ice images are often collected in remote locations by unmanned aerial vehicles with limited computation power, we explore the performance-latency trade-offs for river ice segmentation. We propose a novel convolution block inspired by both depthwise separable convolutions and local binary convolutions giving additional efficiency, parameter savings, and generalization ability to river ice segmentation networks. Our novel convolution block is used in a shallow architecture that has 99.9% fewer trainable parameters, 99% fewer multiply-add operations, and 69.8% less memory usage than a UNet, while achieving virtually the same segmentation performance. We find that this network trains fast and is able to achieve high segmentation performance early in training due to an emphasis on both pixel intensity and texture. When compared to very efficient segmentation networks such as LR-ASPP with a MobileNetV3 backbone, we achieve good performance (mIoU of 64) 91% faster during training on a CPU and and an overall mIoU that is 7.7% higher. We also find that our novel convolution block is able to generalize better to new domains such as snowy environments or datasets with varying illumination. Diving deeper into river ice segmentation with resource constraints, we take on a separate task of training a segmentation model when labelling time is limited. As the ice type, environment, and image quality can vary drastically between rivers of interest, training new segmentation models for new environments can be infeasible due to the laborious task of pixel-wise annotation. We explore a point labelling method leveraging object proposals and a post processing technique that delivers a 14.6% increase in mIoU as compared to a fully supervised UNet with the same labelling budget. Our point labelling method also achieves a mIoU that is only 6.3% lower than a fully supervised model with a annotation budget that is 23x larger

The Role of Thermal Accumulation on the Fabrication of Diffraction Gratings in Ophthalmic PHEMA by Ultrashort Laser Direct Writing

The fabrication of diffraction gratings by ultrashort direct laser writing in poly-hydroxyethyl-methacrylate (PHEMA) polymers used as soft contact lenses is reported. Diffraction gratings were inscribed by focusing laser radiation 100 µm underneath the surface of the samples. Low- and high-repetition rate Ti:sapphire lasers with 120 fs pulsewidth working at 1 kHz and 80 MHz respectively were used to assess the role of thermal accumulation on microstructural and optical characteristics. Periodic patterns were produced for different values of repetition rate, pulse energy, laser wavelength, distance between tracks, and scanning speed. Compositional and structural modifications of the processed areas were studied by micro-Raman spectroscopy showing that under certain parameters, thermal accumulation may result in local densification. Far-field diffraction patterns were recorded for the produced gratings to assess the refractive index change induced in the processed areasThis research was funded by the PIT2 program of the University of Murcia’s own research plan. Fundación Seneca grant No 20647/JLI/18, Junta de Castilla y León (project SA287P18), MINECO (project FIS2017-87970-R) and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie IF No 795630 are also acknowledged

Fabricación mediante fusión zonal con láser, caracterización microestructural, mecánica, térmica y óptica y estudio de bioactividad del compuesto (CaxMg(1-x))3 Al2Si3O12

El objetivo del presente proyecto ha sido la fabricación de vidrios de los compuestos cerámicos Ca3Al2Si3O12, Mg3Al2Si3O12 y Ca1.5Mg1.5Al2Si3O12 mediante fusión zonal asistida con láser, su caracterización microestructural, mecánica, térmica y óptica y la realización de un estudio de bioactividad. La elección de estas composiciones se debe a dos razones. Por una parte, es conocido que a altas temperaturas y presiones estos materiales adquieren estructura tipo granate, lo que les permitiría poder ser utilizados como matriz láser.Por otra, dada su composición química podrían ser materiales bioactivos. De esta manera, la variación de las propiedades ópticas del material podría utilizarse como sonda luminiscente para conocer la evolución de la microestructura del implante. Los vidrios se obtuvieron mediante una técnica de solidificación direccional, en concreto, se utilizó fusión zonal asistida con láser (LFZ). Los precursores necesarios se fabricaron mezclando polvos comerciales de Al2O3, CaO, MgO y SiO2 con la composición deseada y utilizando compactación isostática en frío y sinterización. Se realizó la caracterización microestructural mediante SEM comprobando la ausencia de fases cristalinas y se determinó la composición real de los vidrios mediante análisis EDX. Los ensayos de dureza Vickers, de nanoindentación y de flexión por tres puntos permitieron determinar las propiedades mecánicas de los vidrios. Con la caracterización térmica se obtuvieron las temperaturas de recristalización y de transición vítrea. Mediante espectroscopia láser se caracterizaron, a temperatura ambiente, vidrios dopados con un 1% en peso de Nd2O3 obteniendo los espectros de emisión, de excitación y los tiempos de vida media, así como la densidad óptica. Para determinar la bioactividad de estos materiales se realizaron ensayos in vitro. Las muestras se sumergieron en suero biológico artificial (SBF) y se observaron los cambios producidos en la superficie del material transcurridas 3, 7 y 8 semanas. El proyecto se ha realizado en el Área de Ciencia de Materiales e Ingeniería Metalúrgica del Departamento de Ciencia y Tecnología de Materiales y Fluidos

Canonical and Noncanonical Autophagy as Potential Targets for COVID-19

The SARS-CoV-2 pandemic necessitates a review of the molecular mechanisms underlying cellular infection by coronaviruses, in order to identify potential therapeutic targets against the associated new disease (COVID-19). Previous studies on its counterparts prove a complex and concomitant interaction between coronaviruses and autophagy. The precise manipulation of this pathway allows these viruses to exploit the autophagy molecular machinery while avoiding its protective apoptotic drift and cellular innate immune responses. In turn, the maneuverability margins of such hijacking appear to be so narrow that the modulation of the autophagy, regardless of whether using inducers or inhibitors (many of which are FDA-approved for the treatment of other diseases), is usually detrimental to viral replication, including SARS-CoV-2. Recent discoveries indicate that these interactions stretch into the still poorly explored noncanonical autophagy pathway, which might play a substantial role in coronavirus replication. Still, some potential therapeutic targets within this pathway, such as RAB9 and its interacting proteins, look promising considering current knowledge. Thus, the combinatory treatment of COVID-19 with drugs affecting both canonical and noncanonical autophagy pathways may be a turning point in the fight against this and other viral infections, which may also imply beneficial prospects of long-term protectionThis research was funded by the FEDER/Spanish Ministry of Science and Innovation – State Agency of Research, grant number RTI2018-101969-J-I00CSIC and Ministry of Science and Innovation of Spain (BIO2016-75549-R AEI/FEDER, UE; PIE Ref. 202020E079; PIE CSIC Ref. 202020E043),European Commission, H2020-SC1-2019 (Improved Vaccination Strategies for Older Adults, ISOLDA_Ref. 848166)and U.S. National Institutes of Health (NIH) (2P01AI060699, 0258-3413/HHSN266200700010C and GM131919). M.B.-P. received a contract from NIH