Semi-automated Detection of the Timing of Respiratory Muscle Activity: Validation and First Application

Background: Respiratory muscle electromyography (EMG) can identify whether a muscle is activated, its activation amplitude, and timing. Most studies have focused on the activation amplitude, while differences in timing and duration of activity have been less investigated. Detection of the timing of respiratory muscle activity is typically based on the visual inspection of the EMG signal. This method is time-consuming and prone to subjective interpretation.Aims: Our main objective was to develop and validate a method to assess the respective timing of different respiratory muscle activity in an objective and semi-automated manner.Method: Seven healthy adults performed an inspiratory threshold loading (ITL) test at 50% of their maximum inspiratory pressure until task failure. Surface EMG recordings of the costal diaphragm/intercostals, scalene, parasternal intercostals, and sternocleidomastoid were obtained during ITL. We developed a semi-automated algorithm to detect the onset (EMG, onset) and offset (EMG, offset) of each muscle’s EMG activity breath-by-breath with millisecond accuracy and compared its performance with manual evaluations from two independent assessors. For each muscle, the Intraclass Coefficient correlation (ICC) of the EMG, onset detection was determined between the two assessors and between the algorithm and each assessor. Additionally, we explored muscle differences in the EMG, onset, and EMG, offset timing, and duration of activity throughout the ITL.Results: More than 2000 EMG, onset s were analyzed for algorithm validation. ICCs ranged from 0.75–0.90 between assessor 1 and 2, 0.68–0.96 between assessor 1 and the algorithm, and 0.75–0.91 between assessor 2 and the algorithm (p < 0.01 for all). The lowest ICC was shown for the diaphragm/intercostal and the highest for the parasternal intercostal (0.68 and 0.96, respectively). During ITL, diaphragm/intercostal EMG, onset occurred later during the inspiratory cycle and its activity duration was shorter than the scalene, parasternal intercostal, and sternocleidomastoid (p < 0.01). EMG, offset occurred synchronously across all muscles (p ≥ 0.98). EMG, onset, and EMG, offset timing, and activity duration was consistent throughout the ITL for all muscles (p > 0.63).Conclusion: We developed an algorithm to detect EMG, onset of several respiratory muscles with millisecond accuracy that is time-efficient and validated against manual measures. Compared to the inherent bias of manual measures, the algorithm enhances objectivity and provides a strong standard for determining the respiratory muscle EMG, onset.

Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide.

An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)

We propose an explicit GPU-based solver within the material point method (MPM) framework using graphics processing units (GPUs) to resolve elastoplastic problems under two- and three-dimensional configurations (i.e. granular collapses and slumping mechanics). Modern GPU architectures, including Ampere, Turing and Volta, provide a computational framework that is well suited to the locality of the material point method in view of high-performance computing. For intense and non-local computational aspects (i.e. the back-and-forth mapping between the nodes of the background mesh and the material points), we use straightforward atomic operations (the scattering paradigm). We select the generalized interpolation material point method (GIMPM) to resolve the cell-crossing error, which typically arises in the original MPM, because of the C0 continuity of the linear basis function. We validate our GPU-based in-house solver by comparing numerical results for granular collapses with the available experimental data sets. Good agreement is found between the numerical results and experimental results for the free surface and failure surface. We further evaluate the performance of our GPU-based implementation for the three-dimensional elastoplastic slumping mechanics problem. We report (i) a maximum 200-fold performance gain between a CPU- and a single-GPU-based implementation, provided that (ii) the hardware limit (i.e. the peak memory bandwidth) of the device is reached. Furthermore, our multi-GPU implementation can resolve models with nearly a billion material points. We finally showcase an application to slumping mechanics and demonstrate the importance of a three-dimensional configuration coupled with heterogeneous properties to resolve complex material behaviour.

Detecting Long-Term Deformation of a Loess Landslide from the Phase and Amplitude of Satellite SAR Images: A Retrospective Analysis for the Closure of a Tunnel Event

Information about the long-term spatiotemporal evolution of landslides can improve the understanding of landslides. However, since landslide deformation characteristics differ it is difficult to monitor the entire movement of a landslide using a single method. The Interferometric Synthetic Aperture Radar (InSAR) and pixel offset tracking (POT) method can complement each other when monitoring deformation at different landslide stages. Therefore, the InSAR and improved POT method were adapted to study the pre- and post-failure surface deformation characteristics of the Gaojiawan landslide to deepen understanding of the long-term spatiotemporal evolution characteristics of landslides. The results show that the deformation displacement gradient of the Gaojiawan landslide exhibited rapid movement that exceeded the measurable limit of InSAR during the first disaster. Moreover, the Gaojiawan landslide has experienced long-term creep, and while studying the post-second landslide’s failure stability, the acceleration trend was identified via time series analysis, which can be used as a precursor signal for landslide disaster warning. Our study aims to provide scientific reference for local governments to help prevent and mitigate geological disasters in this region.

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar using Artificial Neural Networking

Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.

Analysis of Flexural Toppling Failure in Rock Slopes Using Discrete Element Method

Flexural toppling failure is a common failure mode of natural and artificial rock slopes, which has caused serious damage to human life and property. In this work, an advanced numerical method called the Universal Distinct Element Code (UDEC) was used to study the mechanism of flexural toppling failure. In total, more than twenty slope models were built and analyzed. Two new parameters (displacement discontinuity and transition coefficient of failure surface) were introduced to present a further understanding of flexural toppling. The results show the failure zone of rock slopes subjected to flexural toppling includes two parts: the first-order instability part (FOIP) and the independent toppling zone (ITZ). The FOIP can be further divided into two subzones: the sliding zone (SZ) and the superimposed toppling zone (STZ). The occurrence of surface deformation discontinuities is the precursor to flexural toppling failure. The first displacement discontinuity occurs on the boundary between the FOIP and the ITZ. The angle, spacing, and angle of the joints, the angle of the slope has a significant influence on the stability of anti-dip bedding rock slopes. However, they do not affect the deformation and failure pattern of the slope.