There are many instances in computer science where computational operations must be performed on matrices of different sizes. In the field of machine learning, particularly when interpreting images, it is often necessary to resize and re-scale images to achieve higher resolutions. While there are various methods for this, they typically involve fitting a simple linear or cubic model to scale the image. The singular value decomposition (SVD) is a powerful tool for dimension reduction and projection. Our proposed state-of-the-art method leverages the capabilities of SVD to create a technique for re-scaling images. This method is primarily based on modifications of the eigenvectors derived from SVD. Previous work has shown that by editing only these Eigenvectors, it is possible to minimize error propagation through the images. When applied to several well-known image processing tasks, it is possible to scale an image with reduced error compared to the current state-of-the-art methods. Additionally, we show that our method can improve the results of machine learning approaches.

Side-scan sonar (SSS) data presents a significant challenge to analysts due to the size of the material that requires processing. Automatic recognition systems can help in the interpretation and filtration of the data, reducing the time needed for analysis. Deep learning architectures have been employed for computer vision tasks in the underwater domain. The objective of this work is to review current deep learning methods for automatic image classification, object detection, semantic segmentation, and instance segmentation through statistical analysis of published studies. Further, this work includes an inventory of publicly available SSS data followed by a discussion on augmentation methods applied to SSS imagery. The study discusses current challenges in the automatic underwater target detection field, highlights main gaps within the domain and identifies potential research directions. Our proposal includes the creation of a standardised benchmark and the use of a common set of metrics allowing for consistent evaluation of future research works.

This study aims to provide valuable insights into the impact of porous structures on particle dynamics in non-Brownian, non-colloidal suspension flows at very low Reynolds numbers. Two experimental approaches, Particle Image Velocimetry (PIV) with refractive index matching and Optical Flow Tracking Velocimetry (OFTV) were employed to analyze very dilute suspensions over various porous media models. The study considered three different porous structures with permeabilities ranging from 0.7 to 0.9 and three different thicknesses ranging from 0.2 cm to 0.5 cm, while the suspension bulk volume fraction was maintained at 3%. In the PIV analysis, we observed that decreasing the porous permeability resulted in the maximum velocity location within the free flow region moving closer towards the interface between the flow and the porous media. We further quantified the effect of the porous structure on the suspension by characterizing interface properties, such as dimensionless slip velocity, shear rate, and slip length. These interface properties were found to be influenced by both the thickness and permeability of the porous media. Next, we analyzed particle migration due to the presence of porous structures using OFTV for very dilute suspensions of 1%, 2%, and 3%, considering a porous medium with known physical properties and thickness. The study revealed two local concentration maxima: one within the free flow region on top of the rod arrays used to create the porous structure and a second along the rods' centerline inside the porous media model.

Background: Current evidence linking ambient PM concentrations and paediatric asthma ED visits is based on varying methodologies, often using modelled data. In 2019, modelled government data showed that Liverpool (UK) exceeded the WHO recommended limit for PM in every local authority. Liverpool persistently experiences higher than national average paediatric hospitalisation for asthma. Aim: To identify children in Liverpool at risk of pollution driven asthma attacks by linking real time business intelligence data for ED visits and city-wide PM sensor data. Methods: The University of Liverpool9s air quality network is equipped with 37 optical particle counters to measure PM concentrations city-wide, hourly. Accuracy is ensured by calibration against reference stations and a machine learning algorithm is employed to generate contour plots. The same methodology is applied to interpolate data on asthma ED visits (ICD-10 codes and post code of residence), analysed using the gliding box algorithm to determine the density of ED visits. Results: We present data for a 150-day period (Figure 1). There is a clear relationship between average PM concentrations around a child's residence and ED visits with asthma. Discussion: Ongoing work includes interrogation of temporal and spatial relationships and linking to prescribing data to further elucidate associations between PM concentrations and asthma morbidity.

Non-Thermal Plasma (NTP) is a promising state of matter for carrying out chemical reactions. NTP offers high densities of reactive species, without the need for a catalyst, while operating at atmospheric pressure and remaining at moderate temperature. Despite its potential, NTP cannot be used comprehensively in reactions until we understand more about the complex interactions of NTP and liquids. To achieve this, NTP reactors that can overcome challenges with solvent evaporation, enable inline data collection, and achieve high selectivity, high yield, and high throughput are required. Here, we detail the construction of i) a microfluidic reactor for chemical reactions using NTP in organic solvents and ii) a corresponding batch setup for control studies and scale-up. The use of microfluidics enables controlled generation of NTP and subsequent mixing with reaction media without loss of solvent. The construction of a low-cost custom mount enables inline optical emission spectroscopy using a fibre optic probe at points along the fluidic pathway.

Through their lifetime sunspots undergo a change in their area and shape and, as they decay, they fragment into smaller structures. Here, for the first time we analyze the spatial structure of magnetohydrodynamic (MHD) slow body and fast surface modes in observed umbrae as their cross-sectional shape changes. The Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) techniques were used to analyze 3 and 6 hours SDO/HMI time series of Doppler velocities at the photospheric level of approximately circular and elliptically shaped sunspots. Each time series were divided equally into time intervals, to evidence the change of the sunspots' shape. To identify physical wave modes, the POD/DMD modes were cross-correlated with a slow body mode model using the exact shape of the umbra, whereas the shape obtained by applying a threshold level of the mean intensity for every time interval. Our results show that the spatial structure of MHD modes are affected, even by apparently small changes of the umbral shape, especially in the case of the higher-order modes. For the datasets used in our study, the optimal time intervals to consider the influence of the change in the shape on the observed MHD modes is 37 - 60 minutes. The choice of these intervals is crucial to properly quantify the energy contribution of each wave mode to the power spectrum.

This work marks the third in a series of experiments that were in a semi-circular, gas-fluidized bed with side jets. In this work, the particles are 1 mm ceramic beads. The bed is operated just at and slightly above and below the minimum fluidization velocity and additional fluidization is provided by two high-speed gas located on the sides of the bed near the flat, front face of the unit. Two primary measurements are taken: high-speed video recording of the front of the bed and bed pressure drop from a tap in the back of the bed. PIV is used to determine particle motion, characterized as a mean Froude number, from the high-speed video. A CFD-DEM model of the bed is presented using the recently released MFIX-Exa code. Four model subvariants are considered using two methods of representing the jets and two drag models, both of which are calibrated to exactly match the experimentally measured minimum fluidization velocity. Although it is more difficult to determine the jet penetration depths in a straightforward manner as in the previous works using Froude number contours, the CFD-DEM results compare quite well to the PIV measurements. Unfortunately, the good agreement of the solids-phase is overshadowed by significant disagreement in the gas-phase data. Specifically, the predicted time-averaged standard deviation of the pressure drop is found to be over an order of magnitude larger than measured. Due to the low value of the measurements, just 1% of the mean bed pressure drop, it seems possible that the data is in error. On the other hand, the model may not be accurately capturing pressure attenuation through an under-fluidized region in the back of the bed. Without the possibility additional experiments to test the validity of the data, this work is simply being reported as is without being able to indicate which, either the simulation or the experiment, is more correct.

Through their lifetime, sunspots undergo a change in their area and shape and, as they decay, they fragment into smaller structures. Here, for the first time we analyze the spatial structure of the magnetohydrodynamic (MHD) slow-body and fast-surface modes in the observed umbrae as their cross-sectional shape changes. The proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) techniques were used to analyze 3 and 6 hr Solar Dynamics Observatory/Helioseismic and Magnetic Imager time series of Doppler velocities at the photospheric level of approximately circular and elliptically shaped sunspots. Each time series was divided into equal time intervals to evidence the change in the shape of the sunspots. To identify the physical wave modes, the POD/DMD modes were cross-correlated with a slow-body mode model using the exact shape of the umbra, whereas the shape obtained by applying a threshold level of the mean intensity for every time interval. Our results show that the spatial structure of MHD modes are affected, even by apparently small changes in the umbral shape, especially in the case of the higher-order modes. For the data sets used in our study, the optimal time intervals to consider the influence of the change in the shape on the observed MHD modes is 37–60 minutes. The choice of these intervals is crucial to properly quantify the energy contribution of each wave mode to the power spectrum.

Coherent structures are generally three-dimensional (3D), and while this information may be readily available from numerical simulations, obtaining 3D time resolved measurements might be technically challenging. We present a methodology to produce 3D kinematic tomographies of spatially localized coherent structures from two-dimensional sequences of snapshots. The method operates by synchronizing modal decompositions of multiple independent Particle Image Velocimetry (PIV) scans of a given flow volume. The volume should be scanned in pairs of simultaneous planes, one of which is taken as sync reference. The case study is a rectangular channel partially obstructed by a permeable media showing a linear instability producing periodic waves. The volume of interest is the downstream-facing permeable step generated by the end of the permeable media. The method is first verified using 3D numerical simulation results produced with a lattice Boltzmann scheme, comparing the kinematic tomography against the complete 3D dataset. Then the method is applied to actual experimental measurements with excellent result, which reveals non-trivial 3D features of the dominating coherent structures.

During the two years following the first case of COVID-19 in the United Kingdom, cycles of social restrictions were imposed to control the spread of the virus. These measures curtailed social contact and halted commercial and recreational activities affecting levels of air pollutants. As society adapted, restrictions eased and pollution gradually returned to baseline levels. However, resurgence in COVID-19 cases from new variants created a protracted and challenging path back to 'normality'. In this study, we retrospectively look back at the two years of COVID-19 and its prevalent variants, and examine the government response and its impact on mobility and air pollution. Results from a peak detection algorithm show peak events in mobility and COVID-19 deaths during variants periods decreased significantly from the wildtype COVID-19, despite the high contagiousness of these variants. Pollution levels remained below baseline with periods of significant increase for O3, while NO2 levels remained depleted, likely as a result of reduced traffic congestion as home office schemes have been maintained. Our findings suggest mobility and pollution return to baseline levels as immunity to COVID-19 increases.

Efforts to prevent the spread of the coronavirus disease 2019 (COVID-19) pandemic have had profound positive and negative impacts on social and environmental indicators worldwide. For the first time, a scenario of a partial economic shutdown could be measured, and large tech companies published wide-coverage mobility reports to quantify the impacts on social change with anonymized location data. During the COVID-19 pandemic, the UK government has employed some of the strictest lockdown periods in the world, causing an immediate halt to travel and business activities. From these repeated lockdown periods, we have gained a snapshot of life without excessive human-made pollution; this has allowed us to interrogate the interaction between meteorology and air quality with minimal anthropogenic input. Our findings show a warmer 2020 increased the UK's ozone levels by 9%, while reductions in human-mobility reduced UK-wide nitrogen dioxide levels by 25% in 2020, which have remained low during the first months of 2021 despite curtailing/ending of restrictions; and a decrease in particulate matter created by meteorological and human drivers. Regionally, London records the highest NO2 and O3 changes, -31% and 35%, respectively, linked to mobility reductions and meteorology.

In this paper, we provide clear direct evidence of multiple concurrent higher-order magnetohydrodynamic (MHD) modes in circular and elliptical sunspots by applying both proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) techniques on solar observational data. These techniques are well documented and validated in the areas of fluid mechanics, hydraulics, and granular flows but are relatively new to the field of solar physics. While POD identifies modes based on orthogonality in space and provides a clear ranking of modes in terms of their contribution to the variance of the signal, DMD resolves modes that are orthogonal in time. The clear presence of the fundamental slow sausage and kink body modes, as well as higher-order slow sausage and kink body modes, have been identified using POD and DMD analysis of the chromospheric H α line at 6562.808 Å for both the circular and elliptical sunspots. Additionally, for the various slow body modes, evidence for the presence of the fast surface kink mode was found in the circular sunspot. All of the MHD mode patterns were cross-correlated with their theoretically predicted counterparts, and we demonstrated that ellipticity cannot be neglected when interpreting MHD wave modes. The higher-order MHD wave modes are even more sensitive to irregularities in umbral cross-sectional shapes; hence, this must be taken into account for more accurate modeling of the modes in sunspots and pores.

To better understand the complex dynamics and physics associated with the rapid expansion of the detonation product fireball following an explosion, it is imperative to have a full description of its associated velocity field. Typical experimental techniques rely on simple single-point measurements captured from pressure transducers or Hopkinson pressure bars. In this technical design note, we aim to improve the current state-of-the-art by introducing a means to determine full velocity fields from high-speed video using optical flow tracking velocimetry. We demonstrate the significance of this method from our results by comparing velocity fields derived from high-speed video and a validated numerical model of the same case. A wider use of this technique will allow researchers to elucidate spatial and temporal features of explosive detonations, which could not be obtained thus far using single-point measurements.

To fully examine the underlying processes of coastal change we need to determine the largest underlying driving mechanisms (hydrodynamics and geomorphological change). The associated mechanics of these mechanisms are based on a vast spectrum of spatial and temporal scales, all of which are important. While independent methods can monitor and model these different scales at different locations obtaining time-synchronized real-world view across all these scales is beyond the scope of current technologies. In this study, we present a novel method and early findings of our machine learning, Eigen decomposition-based data assimilation technique "Eigenshores". Using this method, we assimilate spatially and temporally different datasets. Applying these machine learning-based techniques we create a probabilistic view of short-term coastal changes i.e. with good accuracy we can forecast changes in hydrodynamics and geomorphic change at a vessel level scale creating an early warning system for low carbon and safe port navigation.

As a result of the outbreak and diffusion of SARS-CoV-2, there has been a directive to advance medical working conditions. In dentistry, airborne particles are produced through aerosolization facilitated by dental instruments. To develop methods for reducing the risks of infection in a confined environment, understanding the nature and dynamics of these droplets is imperative and timely. This study provides the first evidence of aerosol droplet formation from an ultrasonic scalar under simulated oral conditions. State-of-the-art optical flow tracking velocimetry and shadowgraphy measurements are employed to quantitatively measure the flow velocity, trajectories and size distribution of droplets produced during a dental scaling process. The droplet sizes are found to vary from 5 µm to 300 µm; these correspond to droplet nuclei that could carry viruses. The droplet velocities also vary between 1.3 m s −1 and 2.6 m s −1 . These observations confirm the critical role of aerosols in the transmission of disease during dental procedures, and provide invaluable knowledge for developing protocols and procedures to ensure the safety of both dentists and patients.

This study is focused on the motion of a dilute suspension containing rigid, spherical, non-Brownian, noncolloidal particles flowing over and through porous media models. The flow is confined to very low Reynolds numbers. To examine the velocity distribution particle image velocimetry (PIV) was applied in conjunction with refractive index matching (RIM) techniques. This study is the first of its kind analyzing the interaction between two common engineering systems: suspension fluid and porous media.

A low-cost “Internet of Things” (IoT) tide gauge network was developed to provide real-time and “delayed mode” sea-level data to support monitoring of spatial and temporal coastal morphological changes. It is based on the Arduino Sigfox MKR 1200 micro-controller platform with a Measurement Specialties pressure sensor (MS5837). Experiments at two sites colocated with established tide gauges show that these inexpensive pressure sensors can make accurate sea-level measurements. While these pressure sensors are capable of ~1 cm accuracy, as with other comparable gauges, the effect of significant wave activity can distort the overall sea-level measurements. Various off-the-shelf hardware and software configurations were tested to provide complementary data as part of a localized network and to overcome operational constraints, such as lack of suitable infrastructure for mounting the tide gauges and for exposed beach locations.

The increasing volume and spatio-temporal resolution of satellite-derived ice velocity data has created new exploratory opportunities for the quantitative analysis of glacier dynamics. One potential technique, Proper Orthogonal Decomposition (POD), also known as Empirical Orthogonal Functions, has proven to be a powerful and flexible technique for revealing coherent structures in a wide variety of environmental flows: mapping hydraulic vortex shedding patterns, the dynamics of fluidised granular beds, and the magnetohydrodynamics of sunspots.POD exactly describes a series of snapshots from a flow field with the product of ranked spatially orthogonal Eigenfunctions, or “modes” of spatial weighting, and one-dimensional “temporal” coefficients (Eigenvectors). In many cases the variance of the flow field is well described by just a few dominant modes. The orthogonal nature of each mode, by definition, means that the relative contribution of independent forcing mechanisms on the flow can, in theory, be separated.In this study we investigate the applicability of POD to freely available TanDEM-X/TerraSAR-X derived ice velocity datasets of Sermeq Kujalleq (Jakobshavn Glacier), Greenland. We outline the POD procedure using the singular value decomposition of a rearranged and resampled velocity matrix and investigate the factors responsible for the dominant modes. We find dominant modes interpreted as relating to the stress-reconfiguration at the glacier terminus and the development of the glacier hydrological system, but also find that the POD is sensitive to data resampling and quality. With the proliferation of publicly available optical and radar derived velocity products (e.g. MEaSUREs/ESA CCI) we suggest POD, and potentially other modal decomposition techniques, will become increasingly useful in future studies of ice dynamics.

Abstract. The increasing volume and spatio-temporal resolution of satellite-derived ice velocity data has created new exploratory opportunities for the quantitative analysis of glacier dynamics. One potential technique, Proper Orthogonal Decomposition (POD), also known as Empirical Orthogonal Functions, has proven to be a powerful and flexible technique for revealing coherent structures in a wide variety of environmental flows. In this study we investigate the applicability of POD to an openly available TanDEM-X/TerraSAR-X derived ice velocity dataset from Sermeq Kujalleq (Jakobshavn Isbræ), Greenland. We find three dominant modes with annual periodicity that we argue are explained by glaciological processes. Mode 1 is interpreted as relating to the stress-reconfiguration at the glacier terminus, known to be an important control on the glacier’s dynamics. Modes 2 and 3 together relate to the development of the spatially heterogenous glacier hydrological system and are primarily driven by the pressurisation and efficiency of the subglacial hydrological system. During the melt season, variations in the velocity shown in Modes 2 and 3 are explained by the drainage of nearby supraglacial melt ponds, as identified with a Google Earth Engine MODIS dynamic thresholding technique. By isolating statistical structures within velocity datasets, and through their comparison to glaciological theory and complementary datasets POD indicates which glaciological processes are responsible for the changing bulk velocity signal, as observed from space. With the proliferation of optical and radar derived velocity products (e.g. MEaSUREs/ESA CCI/PROMICE) we suggest POD, and potentially other modal decomposition techniques, will become increasingly useful in future studies of ice dynamics.