Collision response and obstacle avoidance of the tethered-space net robot system with non-target objects

Purpose This paper aims to study the encounter issues of the Tethered-Space Net Robot System (TSNRS) with non-target objects on orbit during the maneuver, including the collision issues with small space debris and the obstacle avoidance from large obstacles. Design/methodology/approach For the collision of TSNRS with small debris, the available collision model of the tethered net and its limitation is discussed, and the collision detection method is improved. Then the dynamic response of TSNRS is studied and a closed-loop controller is designed. For the obstacle avoidance, the variable enveloping circle of the TSNRS has coupled with the artificial potential field (APF) method. In addition, the APF is improved with a local trajectory correction method to avoid the overbending segment of the trajectory. Findings The collision model coupled with the improved collision detection method solves the detection failure and speeds up calculation efficiency by 12 times. Collisions of TSNRS with small debris make the local thread stretch and deforms finally making the net a mess. The boundary of the disturbance is obtained by a series of collision tests, and the designed controller not only achieved the tracking control of the TSNRS but also suppressed the disturbance of the net. Practical implications This paper fills the gap in the research on the collision of the tethered net with small debris and makes the collision model more general and efficient by improving the collision detection method. And the coupled obstacle avoidance method makes the process of obstacle avoidance safer and smoother. Originality/value The work in this paper provides a reference for the on-orbit application of TSNRS in the active space debris removal mission.

Replacement of the Gamma by the Delta variant in Brazil: impact of lineage displacement on the ongoing pandemic

The COVID-19 epidemic in Brazil was driven mainly by the spread of Gamma (P.1), a locally emerged Variant of Concern (VOC) that was first detected in early January 2021. This variant was estimated to be responsible for more than 96% of cases reported between January and June 2021, being associated with increased transmissibility and disease severity, a reduction in neutralization antibodies and effectiveness of treatments or vaccines, as well as diagnostic detection failure. Here we show that, following several importations predominantly from the USA, the Delta variant rapidly replaced Gamma after July 2021. However, in contrast to what was seen in other countries, the rapid spread of Delta did not lead to a large increase in the number of cases and deaths reported in Brazil. We suggest that this was likely due to the relatively successful early vaccination campaign coupled with natural immunity acquired following prior infection with Gamma. Our data reinforces reports of the increased transmissibility of the Delta variant and, considering the increasing concern due to the recently identified Omicron variant, argues for the necessity to strengthen genomic monitoring on a national level to quickly detect and curb the emergence and spread of other VOCs that might threaten global health.

Commercial immunoassays for detection of anti-SARS-CoV-2 spike and RBD antibodies: urgent call for validation against new and highly mutated variants

Measuring the level of protection conferred by anti-SARS-CoV-2 (trimeric) spike or RBD (receptor binding domain) antibodies (especially total and IgG) is a suitable and reliable approach for predicting biological protection against the risk of infection and severe coronavirus disease 2019 (COVID-19) illness. Nonetheless, SARS-CoV-2 has undergone a broad process of recombination since the identification of the prototype lineage in 2019, introducing a huge number of mutations in its genome and generating a vast array of variants of interest (VoI) and concern (VoC). Many of such variants developed several mutations in spike protein and RBD, with the new Omicron (B.1.1.529) clade displaying over 30 changes, 15 of which concentrated in the RBD. Besides their impact on virus biology, as well as on the risk of detection failure with some molecular techniques (i.e., S gene dropout), recent evidence suggests that these mutations may also jeopardize the reliability of currently available commercial immunoassays for detecting anti-SARS-CoV-2 antibodies. The antigen (either spike or RBD) and epitopes of the prototype SARS-CoV-2 coated in some immunoassays may no longer reflect the sequence of circulating variants. On the other hand, anti-SARS-CoV-2 antibodies elicited by highly mutated SARS-CoV-2 variants may no longer be efficiently recognized by the currently available commercial immunoassays. Therefore, beside the compelling need to regularly re-evaluate and revalidate all commercially available immunoassays against live virus neutralization assays based on emerging VoCs or VoIs, diagnostic companies may also consider to redevelop their methods, replacing former SARS-CoV-2 antigens and epitopes with those of the new variants.

Computational Analysis of Mapping Catheter Geometry and Contact Quality Effects on Rotor Detection in Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and catheter mapping has been proved to be an effective approach for detecting AF drivers to be targeted by ablation. Among drivers, the so-called rotors have gained the most attention: their identification and spatial location could help to understand which patient-specific mechanisms are acting, and thus to guide the ablation execution. Since rotor detection by multi-electrode catheters may be influenced by several structural parameters including inter-electrode spacing, catheter coverage, and endocardium-catheter distance, in this study we proposed a tool for testing the ability of different catheter shapes to detect rotors in different conditions. An approach based on the solution of the monodomain equations coupled with a modified Courtemanche ionic atrial model, that considers an electrical remodeling, was applied to simulate spiral wave dynamics on a 2D model for 7.75 s. The developed framework allowed the acquisition of unipolar signals at 2 KHz. Two high-density multipolar catheters were simulated (Advisor™ HD Grid and PentaRay®) and placed in a 2D region in which the simulated spiral wave persists longer. The configuration of the catheters was then modified by changing the number of electrodes, inter-electrodes distance, position, and atrial-wall distance for assessing how they would affect the rotor detection. In contact with the wall and at 1 mm distance from it, all the configurations detected the rotor correctly, irrespective of geometry, coverage, and inter-electrode distance. In the HDGrid-like geometry, the increase of the inter-electrode distance from 3 to 6 mm caused rotor detection failure at 2 mm distance from the LA wall. In the PentaRay-like configuration, regardless of inter-electrode distance, rotor detection failed at 3 mm endocardium-catheter distance. The asymmetry of this catheter resulted in rotation-dependent rotor detection. To conclude, the computational framework we developed is based on realistic catheter shapes designed with parameter configurations which resemble clinical settings. Results showed it is well suited to investigate how mapping catheter geometry and location affect AF driver detection, therefore it is a reliable tool to design and test new mapping catheters.

Evaluating and Optimizing Analytic Signals

Component failures in complex systems are often expensive. The loss of operation time is compounded by the costs of emergency repairs, excess labor, and compensation to aggrieved customers. Prognostic health management presents a viable option when the failure onset is observable and the mitigation plan actionable. As data-driven approaches become more favorable, success has been measured in many ways, from the basic outcomes, i.e. costs justify the prognostic, to the more nuanced detection tests. Prognostic models, likewise, run the gamut from purely physics-based to statistically inferred. Preserving some physics has merit as that is the source of justification for removing a fully functioning component. However, the method for evaluating competing strategies and optimizing for performance has been inconsistent. One common approach relies on the binary classifier construct, which compares two prediction states (alert or no alert) with two actual states (failure or no failure). A model alert is a positive; true positives are followed by actual failures and false positives are not. False negatives are when failures occur without any alert, and true negatives complete the table, indicating no alert and no failure. Derivatives of the binary classifier include concepts like precision, i.e. the ratio of alerts which are true positives, and recall, the ratio of events which are preceded by an alert. Both precision and recall are useful in determining whether an alert can be trusted (precision) or how many failures it can catch (recall). Other analyses recognize the fact that the underlying sensor signal is continuous, so the alerts will change along with the threshold. For instance, a threshold that is more extreme will result in fewer alerts and therefore more precision at the cost of some recall. These types of tradeoff studies have produced the receiver operating characteristic (ROC) curve. A few ambiguities persist when we apply the binary classifier construct to continuous signals. First, there is no time axis. When does an alert transition from prescriptive to low-value or nuisance? Second, there is no consideration of the nascent information contained in the underlying continuous signal. Instead, it is reduced to alerts via a discriminate threshold. Fundamentally, prognostic health management is the detection of precursors. Failures which can be prognosticated are necessarily a result of wear-out modes. Whether the wear out is detectable and trackable is a system observability issue. Observability in signals is a concept rooted in signal processing and controls. A system is considered observable if the internal state of the system can be estimated using only the sensor information. In a prognostic application, sensor signals intended to detect wear will also contain some amount of noise. This case, noise is anything that is not the wear-out mode. It encompasses everything from random variations of the signal, to situations where the detection is intermittent or inconsistent. Hence, processing the raw sensor signal to maximize the wear-out precursors and minimize noise will provide an overall benefit to the detection before thresholds are applied. The proposed solution is a filter tuned to maximize detection of the wear-out mode. The evaluation of the filter is crucial, because that is also the evaluation of the entire prognostic. The problem statement transforms from a binary classifier to a discrete event detection using a continuous signal. Now, we can incorporate the time dimension and require a minimum lead time between a prognostic alert and the event. Filter evaluation is fundamentally performance evaluation for the prognostic detection. First, we aggregate the filtered values in a prescribed lead interval n samples before each event. Each lead trace is averaged so that there is one characteristic averaged behavior before an event. In this characteristic trace, we can consider the value at some critical actionable time, tac, before the event, after which there is insufficient time to act on the alert. The filtered signal value at this critical time should be anomalous, i.e. it should be far from its mean value. Further, the filtered value in the interval preceding tac should transition from near-average to anomalous. Both the signal value at tac­ as well as the filtered signal behavior up to that point present independent evaluation metrics. These frame the prognostic detection problem as it should be stated, as a continuous signal detecting a discrete event, rather than a binary classifier. A strong anomaly in the signal that precedes events on an aggregated basis is the alternate performance metric. If only a subset of events show an anomaly, that means the detection failure mode is unique to those events, and the performance can be evaluated accordingly. Thresholding is the final step, once the detection is optimized. The threshold need not be ambiguous at this step. The aggregated trace will indicate clearly which threshold will provide the most value.

Airborne environmental DNA for terrestrial vertebrate community monitoring

Assessing and studying the distribution, ecology, diversity and movements of species is key in understanding environmental and anthropogenic effects on natural ecosystems. Although environmental DNA is rapidly becoming the tool of choice to assess biodiversity there are few eDNA sample types that effectively capture terrestrial vertebrate diversity and those that do can be laborious to collect, require special permits and contain PCR inhibitory substances, which can lead to detection failure. Thus there is an urgent need for novel environmental DNA approaches for efficient and cost-effective large-scale routine monitoring of terrestrial vertebrate diversity. Here we show that DNA metabarcoding of airborne environmental DNA filtered from air can be used to detect a wide range of local vertebrate taxa. We filtered air at three localities in Copenhagen Zoo, detecting mammal, bird, amphibian and reptile species present in the zoo or its immediate surroundings. Our study demonstrates that airDNA has the capacity to complement and extend existing terrestrial vertebrate monitoring methods and could form the cornerstone of programs to assess and monitor terrestrial communities, for example in future global next generation biomonitoring frameworks.

Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)

House mice communicate through ultrasonic vocalizations (USVs), which are above the range of human hearing (>20 kHz), and several automated methods have been developed for USV detection and classification. Here we evaluate their advantages and disadvantages in a full, systematic comparison. We compared the performance of four detection methods, DeepSqueak (DSQ), MUPET, USVSEG, and the Automatic Mouse Ultrasound Detector (A-MUD). Moreover, we compared these to human-based manual detection (considered as ground truth), and evaluated the inter-observer reliability. All four methods had comparable rates of detection failure, though A-MUD outperformed the others in terms of true positive rates for recordings with low or high signal-to-noise ratios. We also did a systematic comparison of existing classification algorithms, where we found the need to develop a new method for automating the classification of USVs using supervised classification, bootstrapping on Gammatone Spectrograms, and Convolutional Neural Networks algorithms with Snapshot ensemble learning (BootSnap). It successfully classified calls into 12 types, including a new class of false positives used for detection refinement. BootSnap provides enhanced performance compared to state-of-the-art tools, it has an improved generalizability, and it is freely available for scientific use.

Introspective False Negative Prediction for Black-Box Object Detectors in Autonomous Driving

Object detection plays a critical role in autonomous driving, but current state-of-the-art object detectors will inevitably fail in many driving scenes, which is unacceptable for safety-critical automated vehicles. Given the complexity of the real traffic scenarios, it is impractical to guarantee zero detection failure; thus, online failure prediction is of crucial importance to mitigate the risk of traffic accidents. Of all the failure cases, False Negative (FN) objects are most likely to cause catastrophic consequences, but little attention has been paid to the online FN prediction. In this paper, we propose a general introspection framework that can make online prediction of FN objects for black-box object detectors. In contrast to existing methods which rely on empirical assumptions or handcrafted features, we facilitate the FN feature extraction by an introspective FN predictor we designed in this framework. For this purpose, we extend the original concept of introspection to object-wise FN predictions, and propose a multi-branch cooperation mechanism to address the distinct foreground-background imbalance problem of FN objects. The effectiveness of the proposed framework is verified through extensive experiments and analysis, and the results show that our method successfully predicts the FN objects with 81.95% precision for 88.10% recall on the challenging KITTI Benchmark, and effectively improves object detection performance by taking FN predictions into consideration.