Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.

Data reduction for a calorimetrically measured $$^{163}\mathrm {Ho}$$ spectrum of the ECHo-1k experiment

The electron capture in $$^{163}\mathrm {Ho}$$ 163 Ho experiment (ECHo) is designed to directly measure the effective electron neutrino mass by analysing the endpoint region of the $$^{163}\mathrm {Ho}$$ 163 Ho electron capture spectrum. We present a data reduction scheme for the analysis of high statistics data acquired with the first phase of the ECHo experiment, ECHo-1k, to reliably infer the energy of $$^{163}\mathrm {Ho}$$ 163 Ho events and discard triggered noise or pile-up events. On a first level, the raw data is filtered purely based on the trigger time information of the acquired signals. On a second level, the time profile of each triggered event is analysed to identify the signals corresponding to a single energy deposition in the detector. We demonstrate that events not belonging to this category are discarded with an efficiency above 99.8%, with a minimal loss of $$^{163}\mathrm {Ho}$$ 163 Ho events of about 0.7%. While the filter using the trigger time information is completely energy independent, a slight energy dependence of the filter based on the time profile is precisely characterised. This data reduction protocol will be important to minimise systematic errors in the analysis of the $$^{163}\mathrm {Ho}$$ 163 Ho spectrum for the determination of the effective electron neutrino mass.

Simulation study of local thermal runaway of 18650 lithium battery module under multi-point thermal trigger

The thermal runaway of lithium power battery is the key problem of battery safety, according to the standard SAE J2464–2009 single point heating key position, the proposed multi-point trigger based thermal runaway of lithium power battery module simulation method and battery module thermal runaway battery monomer ratio PN, thermal runaway trigger time tn two indicators, the thermal runaway rule of 18650 lithium power battery module under different trigger position, trigger points. The results show that considering the external thermal insulation conditions of the power lithium battery module, the geometric angular position in the module is the most dangerous position, and the number of thermal trigger points is positively correlated with PN.

Confidence in predicted position error explains saccadic decisions during pursuit

A fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade frequency and trigger time remain poorly understood and this decision process remains imprecisely quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioural observations on the frequency and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions that saccade decisions are highly sensitive to uncertainty for small predicted position errors, but this influence diminishes as the magnitude of predicted position error increases. We suggest that this predictive, confidence-based decision making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.

Corrosion Modeling and Prognosis of the Al-Fe Self-Pierce Riveting Joints

Self-pierce riveting (SPR) is one of the commonly used joining technologies, which is useful to assemble dissimilar materials. However, the galvanic / crevice corrosion between joining metals and metal-rivet interfaces plays an important role on the mechanical properties of the joint. It is critical to have a compressive understanding on the corrosion phenomena of the joint. In this study, a hybrid model that combines the stochastic corrosion nucleation method and physics-based finite element (FE) modeling is proposed. The corrosion nucleation variables, including the positions of the nucleation sites and their corrosion trigger time, are firstly obtained based upon statistical analysis on experiments and then imported in the physics-based FE model as initial conditions. Afterwards, the corrosion propagation process is evaluated via the FE model. Various corrosion scenarios with different nucleation variables are generated and statistically analyzed. Experimental results are used to validate the model. It is found that, the proposed hybrid model can reasonably predict the localized corrosion results of the SPR joints, compared to experimental observations. Moreover, the corrosion area and material loss in the SPR joint are predicted.

The African golden cat Caracal aurata in Tanzania: first record and vulnerability assessment

We report on the first population found in Tanzania of the Vulnerable African golden cat Caracal aurata, extending its documented range c. 200 km to the south and south-east. This is one of the least-known and truly forest-dependent felines in Africa, ranging across the Guinea–Congolian forest block. We recorded the new population in Minziro Nature Forest Reserve, north-west Tanzania, during a 3-month survey in 2018. We deployed 70 camera traps on a regular grid and obtained 33 detection events of the golden cat at 26% of sites, with a minimum of 10 individuals across 257 km2. We estimated occupancy and detection probability and modelled these in relation to the distance of sampling sites to the forest edge, which coincides with both the Reserve boundary and proximity to human settlements surrounding the Reserve. Mean estimated occupancy was 0.41 ± SE 0.12 (mean detectability = 0.13 ± SE 0.05), with occupancy increasing significantly with distance from the forest edge. Detectability did not vary significantly with distance from the forest edge, but was higher for camera models that had a shorter trigger time. Our findings add to the scant data available for this species. It appears threatened by human activity, which we recorded both outside and within the Reserve, and the presence of the species indicates Minziro Forest is an important site for its conservation.

Reliability and limits of transport-ventilators to safely ventilate severe patients in special surge situations

BackgroundSeveral Intensive Care Units (ICU) have been overwhelmed by the surge of COVID-19 patients thus necessitating to extend ventilation capacity outside the ICU where air and oxygen pressure are not always available. Transport ventilators requiring only O2 source may be used to deliver volume-controlled ventilation.ObjectiveTo evaluate the performances of four transport ventilators compared to an ICU ventilator simulating severe respiratory conditions.Materials and methodsTwo pneumatic transport ventilators, (Oxylog 3000, Draeger; Osiris 3, Air Liquide Medical Systems) and two turbine transport ventilators (Elisee 350, ResMed; Monnal T60, Air Liquide Medical Systems) were compared to an ICU ventilator (Engström Carestation – GE Healthcare) using a Michigan training test lung. We tested each ventilator with different set volumes Vtset (350, 450, 550 ml) and different compliances (20 or 50 ml/cmH2O) and a resistance of 15 cmH2 0/L/sec based on values recently described in COVID-19 Acute Respiratory Distress Syndrome. Volume error was measured, as well as the trigger time delay during assist-control ventilation simulating spontaneous breathing activity with a P0.1 of 4 cmH20.ResultsGrouping all conditions, the volume error was 2.9 ± 2.2 % for Engström Carestation; 3.6 ± 3.9 % for Osiris 3; 2.5 ± 2.1 % for Oxylog 3000; 5.4 ± 2.7 % for Monnal T60 and 8.8 ± 4.8 % for Elisee 350. Grouping all conditions, trigger delay was 42 ± 4 ms, 65 ± 5 ms, 151 ± 14 ms, 51 ± 6 and 64 ± 5 ms for Engström Carestation, Osiris 3, Oxylog 3000, Monnal T60 and Elisee 350, respectively.ConclusionsIn special surge situations such as COVID-19 pandemic, most transport ventilators may be used to safely deliver volume-controlled ventilation in locations where only oxygen pressure supply is available with acceptable volume accuracy. Performances regarding triggering function are generally acceptable but vary across ventilators.

GRANDMA observations of advanced LIGO’s and advanced Virgo’s third observational campaign

ABSTRACT GRANDMA (Global Rapid Advanced Network Devoted to the Multi-messenger Addicts) is a network of 25 telescopes of different sizes, including both photometric and spectroscopic facilities. The network aims to coordinate follow-up observations of gravitational-wave (GW) candidate alerts, especially those with large localization uncertainties, to reduce the delay between the initial detection and the optical confirmation. In this paper, we detail GRANDMA’s observational performance during Advanced LIGO/Advanced Virgo Observing Run 3 (O3), focusing on the second part of O3; this includes summary statistics pertaining to coverage and possible astrophysical origin of the candidates. To do so, we quantify our observation efficiency in terms of delay between GW candidate trigger time, observations, and the total coverage. Using an optimized and robust coordination system, GRANDMA followed-up about 90 per cent of the GW candidate alerts, that is 49 out of 56 candidates. This led to coverage of over 9000 deg2 during O3. The delay between the GW candidate trigger and the first observation was below 1.5 h for 50 per cent of the alerts. We did not detect any electromagnetic counterparts to the GW candidates during O3, likely due to the very large localization areas (on average thousands of degrees squares) and relatively large distance of the candidates (above 200 Mpc for 60 per cent of binary neutron star, BNS candidates). We derive constraints on potential kilonova properties for two potential BNS coalescences (GW190425 and S200213t), assuming that the events’ locations were imaged.