Multiscale Variability and the Comparison of Ground and Satellite Radar Based Measures of Peatland Surface Motion for Peatland Monitoring

Peatland surface motion is highly diagnostic of peatland condition. Interferometric Synthetic Aperture Radar (InSAR) can measure this at the landscape scale but requires ground validation. This necessitates upscaling from point to areal measures (80 × 90 m) but is hampered by a lack of data regarding the spatial variability of peat surface motion characteristics. Using a nested precise leveling approach within two areas of upland and low-lying blanket peatland within the Flow Country, Scotland, we examine the multiscale variability of peat surface motion. We then compare this with InSAR timeseries data. We find that peat surface motion varies at multiple scales within blanket peatland with decreasing dynamism with height above the water table e.g., hummocks < lawn < hollows. This trend is dependent upon a number of factors including ecohydrology, pool size/density, peat density, and slope. At the site scale motion can be grouped into central, marginal, and upland peatlands with each showing characteristic amplitude, peak timing, and response to climate events. Ground measurements which incorporate local variability show good comparability with satellite radar derived timeseries. However, current limitations of phase unwrapping in interferometry means that during an extreme drought/event InSAR readings can only qualitatively replicate peat movement in the most dynamic parts of the peatland e.g., pool systems, quaking bog.

Turbulent Superstructures in Inert Jets and Diffusion Jet Flames

An experimental study of spatially localized very large-scale motion superstructures, propagating in a jet of carbon dioxide at low Reynolds numbers, was carried out. A hot-wire anemometer and a high-speed 2D PIV with a frequency of 7 kHz were used as measuring instruments. Such a puff-type superstructure in a jet with a longitudinal dimension of up to 20–30 nozzle diameters are initially formed in the jet source—a long tube in a laminar-turbulent transition mode (without artificial disturbances). It is shown that this regime with intermittency in time, when part of the time flow is laminar and the other part of time is turbulent, exists both at the exit from the nozzle and in the near field of the jet. Thus, the structural stability of such turbulent superstructures in the near field of the jet was found. Despite the large longitudinal scale, these formations have a transverse dimension of the order of several nozzle diameters. These structures have a complex internal topology, that is, superstructures are a conglomeration of vortices of different sizes from macroscale to microscale. Using the example of diffusion combustion of methane in air, it is demonstrated that in reacting jets, the existence of such large localized perturbations is a powerful physical mechanism for a global change in the flame topology. At the same time, the presence of a cascade of vortices of different sizes in the puff composition can lead to fractal deformation of the flame front.

Numerical Simulations and Modal Analysis to Investigate the Defects in a Coating Process

This work investigates the hydrodynamics of jet wiping, a coating process in which a thin slot gas jet impinges on a coating film dragged by a moving strip; thus, reducing the coating thickness and developing a run-back flow. The interaction between the liquid film and the gas jet is highly unsteady, producing long-wavelength defects on the final product known as undulations. We perform Computational Fluid Dynamics (CFD) simulations of the process using High-Performance Computing (HPC) resources. A multi-scale modal analysis is then applied to decrypt the mechanism of wave formation. The main undulation pattern features two-dimensional waves and is correlated with a large-scale motion of the gas jet.

Local Scale Covariance and Its Radical Implications for Cosmology

Local scale covariance posits that no privileged length scales should appear in the fundamental equations of local, Minkowskian physics—why should nature have scale, but not position preferences?—yet, they clearly do. A resolution is proposed wherein scale covariance is promoted to the status of Poincaré covariance, and privileged scales emerge as a result of `scale clustering’, similarly to the way privileged positions emerge in a translation covariant theory. The implied ability of particles to `move in scale’ has recently been shown by the author to offer a possible elegant solution to the missing matter problem. For cosmology, the implications are: (a) a novel component of the cosmological redshift, due to scale-motion over cosmological times; (b) a radically different scenario for the early universe, during which the conditions for such scale clustering are absent. The former is quantitatively analyzed, resulting in a unique cosmological model, empirically coinciding with standard Einstein–de-Sitter cosmology, only in some non-physical limit. The latter implication is qualitatively discussed as part of a critique of the conceptual foundations of ΛCDM which ignores scale covariance altogether.

Investigation of gating in outer membrane porins provides new perspectives on antibiotic resistance mechanisms

Gram-negative bacteria pose a serious public health concern, primarily due to a higher frequency of antibiotic resistance conferred to them as a result of low permeability of their outer membrane (OM). Antibiotics capable of traversing the OM typically permeate through OM porins; thus, understanding the permeation properties of these porins is instrumental to the development of new antibiotics. A common macroscopic feature of many OM porins is their ability to transition between functionally distinct open and closed states that regulate transport properties and rate. To obtain a molecular basis for these processes, we performed tens of microseconds of molecular dynamics simulations of E. coli OM porin, OmpF. We observed that large-scale motion of the internal loop, L3, leads to widening and narrowing of the pore, suggesting its potential role in gating. Furthermore, Markov state analysis revealed multiple energetically stable conformations of L3 corresponding to open and closed states of the porin. Dynamics between these functional states occurs on the time scale of tens of microseconds and are mediated by the movement of highly conserved acidic residues of L3 to form H-bonds with opposing sides of the barrel wall of the pore. To validate our mechanism, we mutated key residues involved in the gating process that alter the H-bond pattern in the open/closed states and performed additional simulations. These mutations shifted the dynamic equilibrium of the pore towards open or closed states. Complementarily, the mutations favoring the open/closed states lead to increased/decreased accumulation of multiple antibiotics in our whole-cell accumulation assays. Notably, porins containing one of the mutations favoring the closed state has previously been found in antibiotic resistant bacterial strains. Overall, our 180 µs of simulation data (wild type and mutants) with concerted experiments suggests that regulation of the dynamic equilibrium between open and closed states of OM porins could be a mechanism by which Gram-negative bacteria acquire antibiotic resistance.

A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors

Research on pre-impact fall detection with wearable inertial sensors (detecting fall accidents prior to body-ground impacts) has grown rapidly in the past decade due to its great potential for developing an on-demand fall-related injury prevention system. However, most researchers use their own datasets to develop fall detection algorithms and rarely make these datasets publicly available, which poses a challenge to fairly evaluate the performance of different algorithms on a common basis. Even though some open datasets have been established recently, most of them are impractical for pre-impact fall detection due to the lack of temporal labels for fall time and limited types of motions. In order to overcome these limitations, in this study, we proposed and publicly provided a large-scale motion dataset called “KFall,” which was developed from 32 Korean participants while wearing an inertial sensor on the low back and performing 21 types of activities of daily living and 15 types of simulated falls. In addition, ready-to-use temporal labels of the fall time based on synchronized motion videos were published along with the dataset. Those enhancements make KFall the first public dataset suitable for pre-impact fall detection, not just for post-fall detection. Importantly, we have also developed three different types of latest algorithms (threshold based, support-vector machine, and deep learning), using the KFall dataset for pre-impact fall detection so that researchers and practitioners can flexibly choose the corresponding algorithm. Deep learning algorithm achieved both high overall accuracy and balanced sensitivity (99.32%) and specificity (99.01%) for pre-impact fall detection. Support vector machine also demonstrated a good performance with a sensitivity of 99.77% and specificity of 94.87%. However, the threshold-based algorithm showed relatively poor results, especially the specificity (83.43%) was much lower than the sensitivity (95.50%). The performance of these algorithms could be regarded as a benchmark for further development of better algorithms with this new dataset. This large-scale motion dataset and benchmark algorithms could provide researchers and practitioners with valuable data and references to develop new technologies and strategies for pre-impact fall detection and proactive injury prevention for the elderly.

PIV Study on Grid-Generated Turbulence in a Free Surface Flow

To investigate the feature of turbulence developing behind the filter device in a current flow, the flow fields at intermediate downstream distance of an immersed grid in an open water channel are recorded using a two-dimensional (2D) Particle Image Velocimetry (PIV) system. The measurements on a series of vertical and horizontal sections are conducted to reveal the stream-wise evolution and depth diversity of grid turbulence in the free surface flow. Unlike the previous experiments by Laser Doppler Velocimetry (LDV) and Hot-Wire Anemometry (HWA), the integral scales and space-time correlations are estimated without using the Taylor hypothesis in this paper. The distributions of mean velocity, turbulence intensity and integral scale show the transition behavior of grid-generated flow from perturbations to fully merged homogenous turbulence. The distributions of velocity and turbulence intensity become more uniform with increasing distance. While the spatial divergence of integral scale becomes more pronounced as the flow structures develop downstream. The vertical distributions of flow parameters reveal the diversity of flow characteristics in the water depth direction influenced by free surface and the outer part of turbulence boundary layer (TBL) from the channel bottom. The applicability of the newly proposed two-order elliptic approximation model for the space-time correlations of the decaying grid turbulence in channel flow is verified at different positions. The calculated convection velocity for large-scale motion and sweep velocity for small-scale motion based on this model bring a new insight into the dynamic pattern of this type of flow.