Landslides and geophysical investigations: advantages and limitations

This special issue is dedicated to the geophysical methods applied to investigate, characterize, and monitor landslides. Over the years, both the advantages and limitations of these techniques have been highlighted, and some drawbacks are still open. Some papers were submitted to this special issue, and, after a thorough peer review process, only five articles were selected to be included in this special issue. This relatively small number is probably caused by the difficulty in applying geophysical techniques on slope movements given hard-operating conditions (e.g., high slopes, distance from access roads, and lack of security for the technical operator) and not because the methods limitations are greater than the advantages

A rotorcraft in-flight ice detection framework using computational aeroacoustics and Bayesian neural networks

This work develops a novel ice detection framework specifically suitable for rotorcraft using computational aeroacoustics and Bayesian neural networks. In an offline phase of the work, the acoustic signature of glaze and rime ice shapes on an oscillating wing are computed. In addition, the aerodynamic performance indicators corresponding to the ice shapes are also monitored. These performance indicators include the lift, drag, and moment coefficients. A Bayesian neural network is subsequently trained using projected Stein variational gradient descent to create a mapping from the acoustic signature generated by the iced wings to predict their performance indicators along with quantified uncertainty that is highly important for time- and safety-critical decision-making scenarios. While the training is carried out fully offline, usage of the Bayesian neural network to make predictions can be conducted rapidly online allowing for an ice detection system that can be used in real time and in-flight

PLoS Genet.

Our understanding of basic evolutionary processes in bacteria is still very limited. For example, multiple recent dating estimates are based on a universal inter-species molecular clock rate, but that rate was calibrated using estimates of geological dates that are no longer accepted. We therefore estimated the short-term rates of mutation and recombination in Helicobacter pylori by sequencing an average of 39,300 bp in 78 gene fragments from 97 isolates. These isolates included 34 pairs of sequential samples, which were sampled at intervals of 0.25 to 10.2 years. They also included single isolates from 29 individuals (average age: 45 years) from 10 families. The accumulation of sequence diversity increased with time of separation in a clock-like manner in the sequential isolates. We used Approximate Bayesian Computation to estimate the rates of mutation, recombination, mean length of recombination tracts, and average diversity in those tracts. The estimates indicate that the short-term mutation rate is 1.4x10(-6) (serial isolates) to 4.5x10(-6) (family isolates) per nucleotide per year and that three times as many substitutions are introduced by recombination as by mutation. The long-term mutation rate over millennia is 5-17-fold lower, partly due to the removal of non-synonymous mutations due to purifying selection. Comparisons with the recent literature show that short-term mutation rates vary dramatically in different bacterial species and can span a range of several orders of magnitude

Structured cost analysis of robotic TME resection for rectal cancer:a comparison between the da Vinci Si and Xi in a single surgeon's experience

Background: Robotic-assisted surgery by the da Vinci Si appears to benefit rectal cancer surgery in selected patients, but still has some limitations, one of which is its high costs. Preliminary studies have indicated that the use of the new da Vinci Xi provides some added advantages, but their impact on cost is unknown. The aim of the present study is to compare surgical outcomes and costs of rectal cancer resection by the two platforms, in a single surgeon’s experience. Methods: From April 2010 to April 2017, 90 robotic rectal resections were performed, with either the da Vinci Si (Si-RobTME) or the da Vinci Xi (Xi-RobTME). Based on CUSUM analysis, two comparable groups of 40 consecutive Si-RobTME and 40 consecutive Xi-RobTME were obtained from the prospectively collected database and used for the present retrospective comparative study. Data costs were analysed based on the level of experience on the proficiency–gain curve (p–g curve) by the surgeon with each platform. Results: In both groups, two homogeneous phases of the p–g curve were identified: Si1 and Xi1: cases 1–19, Si2 and Xi2: cases 20–40. A significantly higher number of full RAS operations were achieved in the Xi-RobTME group (p < 0.001). A statistically significant reduction in operating time (OT) during Si2 and Xi2 phase was observed (p < 0.001), accompanied by reduced overall variable costs (OVC), personnel costs (PC) and consumable costs (CC) (p < 0.001). All costs were lower in the Xi2 phase compared to Si2 phase: OT 265 versus 290 min (p = 0.052); OVC 7983 versus 10231.9 (p = 0.009); PC 1151.6 versus 1260.2 (p = 0.052), CC 3464.4 versus 3869.7 (p < 0.001). Conclusions: Our experience confirms a significant reduction of costs with increasing surgeon’s experience with both platforms. However, the economic gain was higher with the Xi with shorter OT, reduced PC and CC, in addition to a significantly larger number of cases performed by the fully robotic approach

Evaluating Learning-based Tie Point Matching for Geometric Processing of Off-Track Satellite Stereo

Tie-point matching of off-track stereo images is a very challenging task, which can impact bias compensation and digital surface model (DSM) generation. Compared to in-track stereo images, off-track stereo images are more complex primarily due to the radiometric differences caused by sun illumination, sensor responses, atmospheric conditions, and seasonal land cover variations, and secondly due to the longer baseline and larger intersection angle. These challenges significantly limit the use of the vast number of images in satellite archives for automated geometric processing and mapping. Recent advances in deep learning (DL) based matching show promising results against images with diverse illuminations, viewing angles and scales through learning examples. This paper evaluates the potentials of addressing the tie point matching problems in off-track satellite stereo images. Specifically, we focus on stereo pairs that failed or underperformed in classic matching algorithms (i.e., SIFT (scale invariant feature transform)), and evaluate the DL-based tie points matchers by its resulting geometric accuracy in relative orientation, and the generated DSM. The experiments are carried out using 40 off-track satellite stereo pairs from four different regions around the world. We conclude that DL-based methods provide a significant higher success rate in matching challenging multi-temporal stereo pairs, even if their matching accuracy is slightly lower than classic algorithms

A time-domain control signal detection technique for OFDM

Transmission of system-critical control information plays a key role in efficient management of limited wireless network resources and successful reception of payload data information. This paper uses an orthogonal frequency division multiplexing (OFDM) architecture to investigate the detection performance of a time-domain approach used to detect deterministic control signalling information. It considers a type of control information chosen from a finite set of information, which is known at both transmitting and receiving wireless terminals. Unlike the maximum likelihood (ML) estimation method, which is often used, the time-domain detection technique requires no channel estimation and no pilots as it uses a form of time-domain correlation as the means of detection. Results show that when compared with the ML method, the time-domain approach improves detection performance even in the presence of synchronisation error caused by carrier frequency offset

Frequency tracking by method of least squares combined with channel estimation for OFDM over mobile wireless channels

[[abstract]]To track frequency offset and time-varying channel in orthogonal frequency division multiplexing (OFDM) systems over mobile wireless channels, a common technique is, based on one OFDM training block sample, to apply the maximum-likelihood (ML) algorithm to perform joint frequency tracking and channel estimation employing some adaptive iteration processes. The major drawback of such joint estimation techniques is the local extrema problem arising from the highly nonlinear nature of the log-likelihood function. This makes the joint estimation process very difficult and complicated, and many a time the results are not very satisfactory if the algorithm is not well designed. In this study, rather than using the ML algorithm, we shall apply the method of least squares (LS) for frequency tracking utilizing repeated OFDM training blocks. As will be seen, by using such an LS approach, the frequency offset estimation requires no channel knowledge. The channel state can be estimated separately after the LS frequency offset correction. This not only circumvents the local extrema complication, but also obviates the need for the lengthy adaptive iteration process of joint estimation thus greatly simplifies the entire estimation process. Most importantly, our technique can achieve excellent estimation performance as compared to the usual ML algorithms.[[incitationindex]]SCI[[booktype]]紙