A comparison of technicques to disimpact the fetal head on a second stage caesearean simulator

Background The rate of second stage caesarean section (CS) is rising with associated increases in maternal and neonatal morbidity, which may be related to impaction of the fetal head in the maternal pelvis. In the last 10 years, two devices have been developed to aid disimpaction and reduce these risks: the Fetal Pillow (FP) and the Tydeman Tube (TT). The aim of this study was to determine the distance of upward fetal head elevation achieved on a simulator for second stage CS using these two devices, compared to the established technique of per vaginum digital disimpaction by an assistant. Methods We measured elevation of the fetal head achieved with the two devices (TT and FP), compared to digital elevation, on a second stage Caesearean simulator (Desperate Debra ™ set at three levels of severity. Elevation was measured by both a single operator experienced with use of the TT and FP and also multiple assistants with no previous experience of using either device. All measurements were blinded Results The trained user achieved greater elevation of the fetal head at both moderate and high levels of severity with the TT (moderate: 30mm vs 12.5mm p<0.001; most severe: 25mm vs 10mm p<0.001) compared to digital elevation. The FP provided comparable elevation to digital at both settings (moderate: 10 vs 12.5mm p=0.149; severe 10 vs 10mm p=0.44). With untrained users, elevation was also significantly greater with the TT compared to digital elevation (20mm vs 10mm p<0.01). However digital disimpaction was significantly greater than the FP (10mm vs 0mm p<0.0001). Conclusion On a simulator, with trained operators, the TT provided greater fetal head elevation than digital elevation and the FP. The FP achieved similar elevation to the digital technique, especially when the user was trained in the procedure.

A High-Resolution Gravimetric Geoid Model for Kuwait Using the Least-Squares Collocation

A new gravimetric geoid model, the KW-FLGM2021, is developed for Kuwait in this study. This new geoid model is driven by a combination of the XGM2019e-combined global geopotential model (GGM), terrestrial gravity, and the SRTM 3 global digital elevation model with a spatial resolution of three arc seconds. The KW-FLGM2021 has been computed by using the technique of Least Squares Collocation (LSC) with Remove-Compute-Restore (RCR) procedure. To evaluate the external accuracy of the KW-FLGM2021 gravimetric geoid model, GPS/leveling data were used. As a result of this evaluation, the residual of geoid heights obtained from the KW-FLGM2021 geoid model is 2.2 cm. The KW-FLGM2021 is possible to be recommended as the first accurate geoid model for Kuwait.

Demystifying the Differences between Structure-from-Motion Software Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows.

Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning.

The 1513 Monte Crenone rock avalanche: numerical model and geomorphological analysis

As a contribution to the knowledge of historical rockslides, this research focuses on the historical reconstruction, field mapping, and simulation of the expansion, through numerical modelling, of the 30 September 1513 Monte Crenone rock avalanche. Earth observation in 2-D and 3-D, as well as direct in situ field mapping, allowed the detachment zone and the perimeter and volume of the accumulation to be determined. Thanks to the reconstruction of the post-event digital elevation model based on historical topographic maps and the numerical modelling with the RAMMS::DEBRISFLOW software, the dynamics and runout of the rock avalanche were calibrated and reconstructed. The reconstruction of the runout model allowed confirmation of the historical data concerning this event, particularly the damming of the valley floor and the lake formation up to an elevation of 390 m a.s.l., which generated an enormous flood by dam breaching on 20 May 1515, known as the “Buzza di Biasca”.