Typical Time Research Articles

Calibrating lane-changing models: Two data-related issues and a general method to extract appropriate data

Lane-changing is a routine but difficult driving task with important implications on traffic flow characteristics. Despite significant progresses in lane-changing decision modeling, lane-changing models are often improperly calibrated due to two issues related to trajectory data processing. First, the time of insertion (i.e., the time instant where a vehicle crossed the lane marking) is incorrectly considered as the lane-changing decision point, since the lane-changing decision is typically made earlier. Secondly, there is an imbalance between the number of non-lane-changing and lane-changing events, where non-lane-changing events typically dominate trajectory data. These issues can overestimate model performance and biased parameters. In this paper, we propose a method that combines (i) the wavelet transform method to pinpoint the correct lane-changing decision point, and (ii) a case–control design to systematically neutralize the dominance of non-lane-changing events in the data. The proposed method is applied to two NGSIM datasets to assess the performance of four representative lane-changing models. Results uncover that (i) lane-changing models are sensitive to various degrees of data imbalance, (ii) regardless of a driver’s decision time window (e.g., 1 s, 2 s, or 3 s), an analysis time window of 6 s will work reasonably well for evaluating the performance of a lane-changing model, while the optimal control-to-case ratio is 1:1; and (iii) when possible, a temporal discretization interval (i.e., an approximation of a driver’s typical decision time window) of 2 s should be preferred, while 3 s should be avoided. The proposed method also enabled us to outline a performance range for the selected lane-changing models.

CGAN-rIRN: a data-augmented deep learning approach to accurate classification of mental tasks for a fNIRS-based brain-computer interface.

Functional near-infrared spectroscopy (fNIRS) is increasingly used to investigate different mental tasks for brain-computer interface (BCI) control due to its excellent environmental and motion robustness. Feature extraction and classification strategy for fNIRS signal are essential to enhance the classification accuracy of voluntarily controlled BCI systems. The limitation of traditional machine learning classifiers (MLCs) lies in manual feature engineering, which is considered as one of the drawbacks that reduce accuracy. Since the fNIRS signal is a typical multivariate time series with multi-dimensionality and complexity, it makes the deep learning classifier (DLC) ideal for classifying neural activation patterns. However, the inherent bottleneck of DLCs is the requirement of substantial-scale, high-quality labeled training data and expensive computational resources to train deep networks. The existing DLCs for classifying mental tasks do not fully consider the temporal and spatial properties of fNIRS signals. Therefore, a specifically-designed DLC is desired to classify multi-tasks with high accuracy in fNIRS-BCI. To this end, we herein propose a novel data-augmented DLC to accurately classify mental tasks, which employs a convolution-based conditional generative adversarial network (CGAN) for data augmentation and a revised Inception-ResNet (rIRN) based DLC. The CGAN is utilized to generate class-specific synthetic fNIRS signals to augment the training dataset. The network architecture of rIRN is elaborately designed in accordance with the characteristics of the fNIRS signal, with serial multiple spatial and temporal feature extraction modules (FEMs), where each FEM performs deep and multi-scale feature extraction and fusion. The results of the paradigm experiments show that the proposed CGAN-rIRN approach improves the single-trial accuracy for mental arithmetic and mental singing tasks in both the data augmentation and classifier, as compared to the traditional MLCs and the commonly used DLCs. The proposed fully data-driven hybrid deep learning approach paves a promising way to improve the classification performance of volitional control fNIRS-BCI.

Fast Substructure Search in Combinatorial Library Spaces.

We present an efficient algorithm for substructure search in combinatorial libraries defined by synthons, i.e., substructures with connection points. Our method improves on existing approaches by introducing powerful heuristics and fast fingerprint screening to quickly eliminate branches of nonmatching combinations of synthons. With this, we achieve typical response times of a few seconds on a standard desktop computer for searches in large combinatorial libraries like the Enamine REAL Space. We published the Java source as part of the OpenChemLib under the BSD license, and we implemented tools to enable substructure search in custom combinatorial libraries.

Lightning at Jupiter pulsates with a similar rhythm as in-cloud lightning at Earth

Our knowledge about the fine structure of lightning processes at Jupiter was substantially limited by the time resolution of previous measurements. Recent observations of the Juno mission revealed electromagnetic signals of Jovian rapid whistlers at a cadence of a few lightning discharges per second, comparable to observations of return strokes at Earth. The duration of these discharges was below a few milliseconds and below one millisecond in the case of Jovian dispersed pulses, which were also discovered by Juno. However, it was still uncertain if Jovian lightning processes have the fine structure of steps corresponding to phenomena known from thunderstorms at Earth. Here we show results collected by the Juno Waves instrument during 5 years of measurements at 125-microsecond resolution. We identify radio pulses with typical time separations of one millisecond, which suggest step-like extensions of lightning channels and indicate that Jovian lightning initiation processes are similar to the initiation of intracloud lightning at Earth.

Leaking from the phase space of the Riemann–Liouville fractional standard map

In this work we characterize the escape of orbits from the phase space of the Riemann–Liouville (RL) fractional standard map (fSM). The RL-fSM, given in action–angle variables, is derived from the equation of motion of the kicked rotor when the second order derivative is substituted by a RL derivative of fractional order α. Thus, the RL-fSM is parameterized by K and α∈(1,2] which control the strength of nonlinearity and the fractional order of the RL derivative, respectively. Indeed, for α=2 and given initial conditions, the RL-fSM reproduces Chirikov’s standard map. By computing the survival probability PS(n) and the frequency of escape PE(n), for a hole of hight h placed in the action axis, we observe two scenarios: When the phase space is ergodic, both scattering functions are scale invariant with the typical escape time ntyp=exp〈lnn〉∝(h/K)2. In contrast, when the phase space is not ergodic, the scattering functions show a clear non-universal and parameter-dependent behavior.

Terahertz EPR spectroscopy using a 36-tesla high-homogeneity series-connected hybrid magnet

Electron Paramagnetic Resonance (EPR) is a powerful technique to study materials and biological samples on an atomic scale. High-field EPR in particular enables extracting very small g-anisotropies in organic radicals and half-filled 3d and 4f metal ions such as MnII (3d5) or GdIII (4f7), and resolving EPR signals from unpaired spins with very close g-values, both of which provide high-resolution details of the local atomic environment. Before the recent commissioning of the high-homogeneity Series Connected Hybrid magnet (SCH, superconducting + resistive) at the National High Magnetic Field Laboratory (NHMFL), the highest-field, high-resolution EPR spectrometer available was limited to 25 T using a purely resistive “Keck” magnet at the NHMFL. Herein, we report the first EPR experiments performed using the SCH magnet capable of reaching the field of 36 T, corresponding to an EPR frequency of 1 THz for g = 2. The magnet’s intrinsic homogeneity (25 ppm, that is 0.9 mT at 36 T over 1 cm diameter, 1 cm length cylinder) was previously established by NMR. We characterized the magnet’s temporal stability (5 ppm, which is 0.2 mT at 36 T over one-minute, the typical acquisition time) using 2,2-diphenyl-1-picrylhydrazyl (DPPH). This high resolution enables resolving the weak g-anisotropy of 1,3-bis(diphenylene)-2-phenylallyl (BDPA), Δg = 2.5 × 10–4 obtained from measurements at 932 GHz and 33 T. Subsequently, we recorded EPR spectra at multiple frequencies for two GdIII complexes with potential applications as spin labels. We demonstrated a significant reduction in line broadening in Gd[DTPA], attributed to second order zero field splitting, and a resolution enhancement of g-tensor anisotropy for Gd[sTPATCN]-SL.

Quench dynamics of Fano-like resonances in the presence of the on-dot superconducting pairing

We explore the electron dynamics of a system composed of double quantum dot embedded between metallic and superconducting leads in a “T-shape” geometry. In nanoscopic systems, where electron transfer between electrodes can be realized via different paths, interference effects play an important role. For double quantum dot system in the chosen geometry, interference of electrons transferred between electrodes via the interfacial quantum dot and electrons scattered on the side dot gives rise to Fano-like interference. If such a system is additionally coupled to a superconducting electrode, together with the well-understood Fano resonance an additional resonance appears on the opposite side of the Fermi level. In the recent work (Barański et al. in Sci Rep 10:2881, 2020), we showed that this resonance occurs solely as a result of the local pairing of non-scattered electrons with scattered ones. In this work, considering the quench dynamics, we explore how much time is required for formation of each of these resonances. In particular, (i) we analyze the charge oscillations between subsystems; (ii) we estimate the time required for each resonance to achieve stable equilibrium upon an abrupt change of interdot connection; (iii) we discuss a typical energy and time scales for experiments on similar architectures.

Satellites of Milky Way- and M31-like galaxies with TNG50: quenched fractions, gas content, and star formation histories

ABSTRACT We analyse the quenched fractions, gas content, and star formation histories of ∼1200 satellite galaxies with M* ≥ 5 × 106 M⊙ around 198 Milky Way- (MW) and Andromeda-like (M31) hosts in TNG50, the highest-resolution simulation of IllustrisTNG. Satellite quenched fractions are larger for smaller masses, for smaller distances to their host galaxy, and in the more massive M31-like compared to MW-like hosts. As satellites cross their host’s virial radius, their gas content drops: Most satellites within 300 kpc lack detectable gas reservoirs at z = 0, unless they are massive like the Magellanic Clouds and M32. Nevertheless, their stellar assembly exhibits a large degree of diversity. On average, the cumulative star formation histories are more extended for brighter, more massive satellites with a later infall, and for those in less massive hosts. Based on these relationships, we can even infer infall periods for observed MW and M31 dwarfs, e.g. 0–4 Gyr ago for the Magellanic Clouds and Leo I, and 4–8 and 0–2 Gyr ago for M32 and IC 10, respectively. Ram pressure stripping (in combination with tidal stripping) deprives TNG50 satellites of their gas reservoirs and ultimately quenches their star formation, even though only a few per cent of the present-day satellites around the 198 TNG50 MW/M31-like hosts appear as jellyfish. The typical time since quenching for currently quenched TNG50 satellites is $6.9\substack{+2.5\\-3.3}$ Gyr ago. The TNG50 results are consistent with the quenched fractions and stellar assembly of observed MW and M31 satellites, however, satellites of the SAGA survey with M* ∼ 108–109 M⊙ exhibit lower quenched fractions than TNG50 and other, observed analogues.

Observing Spin-Squeezed States under Spin-Exchange Collisions for a Second

Using the platform of a trapped-atom clock on a chip, we observe the time evolution of spin-squeezed hyperfine clock states in ultracold rubidium atoms on previously inaccessible timescales up to 1 s. The spin degree-of-freedom remains squeezed after 0.6 s, which is consistent with the limit imposed by particle loss and is compatible with typical Ramsey times in state-of-the-art microwave clocks. The results also reveal a surprising spin-exchange interaction effect that amplifies the cavity-based spin measurement via a correlation between spin and external degrees of freedom. These results open up perspectives for squeezing-enhanced atomic clocks in a metrologically relevant regime and highlight the importance of spin interactions in real-life applications of spin squeezing.

Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection.

In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD.Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields.To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig.1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.

Electrocatalysis at Individual Colloidal Nanoparticles: A Quantitative Survey of Four Geometries via Electrochemical Cell Microscopy

Colloidal nanoparticles are inherently heterogeneous, exhibiting variations in size, shape, or composition that will impact their catalytic behavior. Understanding these particle-to-particle variations in catalytic behavior will be critical to realizing more stable, selective, and efficient catalyst systems, but it remains difficult to generate this understanding using conventional characterization techniques. Here, we demonstrate how targeted electrochemical cell microscopy (TECCM) can be utilized to rigorously evaluate the electrocatalytic behavior of hundreds of individual metal nanoparticle catalysts, enabling statistically meaningful insights into these systems to be generated. The electrocatalytic oxidation of hydrazine was studied in a series of Au nanoparticle systems (nanorods, nanospheres, triangular nanoprisms, and nanocubes), directly revealing particle-to-particle variations in key kinetic parameters and catalyst stability. On average, “sharper” nanoparticle geometries were found to exhibit higher initial activities but quickly degraded upon potential cycling. Interestingly, our single particle studies also reveal that while the smoother nanorod and nanosphere geometries exhibit stable behavior in an ensemble sense, this is in fact due to a complicated balance of populations which exhibit increasing or decreasing catalytic behavior over typical experimental time scales. Results from correlated optical spectroscopy and electron microscopy experiments suggest that these observed changes in catalytic behavior are not associated with significant changes in particle structure. Together, these results demonstrate the extensive heterogeneity present in common colloidal nanoparticle systems and the utility of single particle analytical techniques for studying these systems.

The Ionospheric Effects of the 2022 Hunga Tonga Volcano Eruption and the Associated Impacts on GPS Precise Point Positioning Across the Australian Region

AbstractThe Hunga Tonga Volcano eruption launched a myriad of atmospheric waves that have been observed to travel around the world several times. These waves generated traveling ionospheric disturbances (TIDs) in the ionosphere, which are known to adversely impact radio applications such as Global Navigation Satellite Systems (GNSS). One such GNSS application is Precise Point Positioning (PPP), which can achieve cm‐level accuracy using a single receiver, following a typical convergence time of 30 min to 1 hr. A network of ionosondes located throughout the Australian region were used in combination with GNSS receivers to explore the impacts of the Hunga Tonga Volcano eruption on the ionosphere and what subsequent impacts they had on PPP. It is shown that PPP accuracy was not significantly impacted by the arrival of the TIDs and Spread‐F, provided that PPP convergence had already been achieved. However, when the PPP algorithm was initiated from a cold start either shortly before or after the TID arrivals, the convergence times were significantly longer. GNSS stations in northeastern Australia experienced increases in convergence time of more than 5 hr. Further analysis reveals increased convergence times to be caused by a super equatorial plasma bubble (EPB), the largest observed over Australia to date. The EPB structure was found to be ∼42 TECU deep and ∼300 km across, traveling eastwards at 30 m/s. The Hunga Tonga Volcano eruption serves as an excellent example of how ionospheric variability can impact real‐world applications and the challenges associated with modeling the ionosphere to support GNSS.

Observations of the space/time scales of Beaufort sea acoustic duct variability and their impact on transmission loss via the mode interaction parameter.

The Beaufort duct (BD) is a subsurface sound channel in the western Arctic Ocean formed by cold Pacific Winter Water (PWW) sandwiched between warmer Pacific Summer Water (PSW) and Atlantic Water (AW). Sound waves can be trapped in this duct and travel long distances without experiencing lossy surface/ice interactions. This study analyzes BD vertical and temporal variability using moored oceanographic measurements from two yearlong acoustic transmission experiments (2016-2017 and 2019-2020). The focus is on BD normal mode propagation through observed ocean features, such as eddies and spicy intrusions, where direct numerical simulations and the mode interaction parameter (MIP) are used to quantify ducted mode coupling strength. The observations show strong PSW sound speed variability, weak variability in the PWW, and moderate variability in the AW, with typical time scales from days to weeks. For several hundreds Hertz propagation, the BD modes are relatively stable, except for rare episodes of strong sound speed perturbations. The MIP identifies a resonance condition such that the likelihood of coupling is greatest when there is significant sound speed variability in the horizontal wave number band 1/11<kh<1/5 km-1. MITgcm ocean model results are used to estimate sound speed fluctuations in this resonance regime.

Development of Machine Learning Based Flight Price Prediction System for Indian Market

Abstract: Airlines usually keep their price strategies as commercial secrets and information is always asymmetric, it is difficult for ordinary customers to estimate future flight price changes. However, a reasonable prediction can help customers make decisions when to buy air tickets for a lower price. Flight price prediction can be regarded as a typical time series prediction problem. When you give customers a device that can help them save some money, they will pay you back with loyalty, which is priceless. Considering the features such as departure time, the number of days left for departure and time of the day it will give the best time to buy the ticket. Features are extracted from the collected data to apply Random Forest Machine Learning (ML) model. Then using this information, we are intended to build a system that can help buyers whether to buy a ticket or not. We have used Random Forest Algorithm which is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model. With that said, random forests are a strong modelling technique and much more robust than a single decision tree. They aggregate many decision trees to limit over fitting as well as error due to bias and therefore yield useful results. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them.

An empirical investigation of the relationship between government revenue, expenditure, and economic growth in selected EMEs

Purpose ─ This article explores the relationship between government revenue, government expenditure, and economic growth for nine emerging market economies using annual data from 1991-92 to 2019-20. Method ─ This paper distinguishes itself from the existing literature through the application of co-integration tests, vector error correction, DOLS and FMOLS for an empirical investigation of a unique panel data set of select emerging economies across Asia, Africa, Europe and Latin America. A bi-directional causal long-run relationship between economic growth and government expenditure, as well as between government expenditure and government revenue, was found using standard panel co-integration tests. Findings ─ The long-run elasticities computed using VECM were confirmed from DOLS as well as FMOLS estimates. A one per cent increase in expenditure and revenue, in the long run, would result in an increase in GDP by 0.94 and 0.90 per cent, respectively. Similarly, an increase in GDP by one per cent would lead to an increase in government expenditure by 1.1 per cent. On the other hand, an increase in government revenue by one per cent would cause a corresponding increase in government expenditure by nearly one per cent. The findings of this research point to a positive association between government revenue, expenditure, and economic growth, which will be valuable to policymakers. Contribution ─ Our combination of country selection covering economies from different continents is a first of its kind to the best of our knowledge. Another contribution is the application of panel cointegration and panel error correction techniques to fully use the panel data set, while most previous studies utilised the typical time series modelling with individual time series data.

Self-enhancement lateral flow immunoassay for COVID-19 diagnosis

Equipment-free colorimetric-based lateral flow immunoassay (LFIA) is the most convenient and popular tool for various applications, including diagnostic tools requiring high sensitivity for the detection of pathogens. Thus, improvements and developments of LFIA are constantly being reported. Herein, we enriched the sensitivity of LFIA using the gold enhancement principle, emphasizing needlessly complicated apparatus, only one step for the strip test operation, and typical time incubation (15 min) process. Self-enhanced LFIA was then executed for subsequent flows by overlapping the additionally enhanced pad composed of gold ions and reducing agent on the conjugate pad and the sample pad. Self-enhanced LFIA was performed to detect SARS-CoV-2 antigens in saliva. The obtained result depicted that the achieved sensitivity was up to tenfold compared with that of conventional LFIA by visual measurements. The detection limits of self-enhanced LFIA detecting nucleocapsid protein antigens in the saliva sample was 0.50 and 0.10 ng/mL employed by naked eye detection and calibration curve-based calculation, respectively. When the proposed device was applied to 207 human saliva samples, the diagnostic performance presented a 96.10 % sensitivity and 99.23 % specificity. This self-enhanced LFIA could be implemented in large-scale production and demonstrates higher sensitivity with effortless use, which meets the requirements for point-of-care testing and on-field mass screening.

A New Digital Health Program for Patients With Inflammatory Bowel Disease: Preliminary Program Evaluation

Inflammatory bowel disease (IBD) causes chronic inflammation of the gastrointestinal tract. IBD is characterized by an unpredictable disease course that varies greatly between individuals and alternates between the periods of relapse and remission. A low energy level (fatigue) is a common symptom, whereas stress and reduced sleep quality may be the triggering factors. Therapeutic guidelines call for effective disease assessment, early intervention, and personalized care using a treat-to-target approach, which may be difficult to achieve through typical time- and resource-constrained standard care. Providing patients with a digital health program that incorporates helpful self-management features and patient support to complement standard care may be optimal for improving the disease course. This study aimed to perform a preliminary program evaluation, analyzing engagement and preliminary effectiveness and the effect on participants' energy levels (fatigue), stress, and sleep quality, of a newly developed 16-week digital health program (SK-311 and SK-321) for patients with IBD. Adults with IBD were recruited to participate in a real-world, live, digital health program via Finnish IBD patient association websites and social media. No inclusion or exclusion criteria were applied for this study. Baseline characteristics were entered by the participants upon sign-up. Platform engagement was measured by tracking the participants' event logs. The outcome measures of stress, energy levels (fatigue), and quality of sleep were reported by participants through the platform. Of the 444 adults who registered for the digital health program, 205 (46.2%) were included in the intention-to-treat sample. The intention-to-treat participants logged events on average 41 times per week (5.9 times per day) during the weeks in which they were active on the digital platform. More women than men participated in the intervention (126/205, 88.7%). The mean age of the participants was 40.3 (SD 11.5) years, and their mean BMI was 27.9 (SD 6.0) kg/m2. In total, 80 people provided the required outcome measures during weeks 12 to 16 (completers). Treatment completion was strongly predicted by the number of active days in week 1. Analysis of the completers (80/205, 39%) showed significant improvements for stress (t79=4.57; P<.001; percentage change=-23.26%) and energy levels (t79=-2.44; P=.017; percentage change=9.48%); however, no significant improvements were observed for quality of sleep (t79=-1.32; P=.19). These results support the feasibility of a digital health program for patients with IBD (SK-311 and SK-321) and suggest that treatment completion might have a substantial positive effect on patient-reported stress and energy levels in a real-world setting. These findings are promising and provide initial support for using the Sidekick Health digital health program to supplement standard care for patients with IBD.

Fast Reconstruction of 3D Density Distribution around the Sun Based on the MAS by Deep Learning

This study is the first attempt to generate a three-dimensional (3D) coronal electron density distribution based on the pix2pixHD model, whose computing time is much shorter than that of the magnetohydrodynamic (MHD) simulation. For this, we consider photospheric solar magnetic fields as input, and electron density distribution simulated with the MHD Algorithm outside a Sphere (MAS) at a given solar radius is taken as output. We consider 155 pairs of Carrington rotations as inputs and outputs from 2010 June to 2022 April for training and testing. We train 152 deep-learning models for 152 solar radii, which are taken up to 30 solar radii. The artificial intelligence (AI) generated 3D electron densities from this study are quite consistent with the simulated ones from lower radii to higher radii, with an average correlation coefficient 0.97. The computing time of testing data sets up to 30 solar radii of 152 deep-learning models is about 45.2 s using the NVIDIA TITAN XP graphics-processing unit, which is much less than the typical simulation time of MAS. We find that the synthetic coronagraphic images estimated from the deep-learning models are similar to the Solar Heliospheric Observatory (SOHO)/Large Angle and Spectroscopic Coronagraph C3 coronagraph data, especially during the solar minimum period. The AI-generated coronal density distribution from this study can be used for space weather models on a near-real-time basis.

Retirement and Volunteering in Germany - Historical Changes and Social Inequalities.

The first years of retirement have often been seen as a typical time window to take up (or intensify) voluntary work. Due to the changing context of retirement and historical differences in resources, the role of retirement for volunteering may have changed with historical time. We compared individuals aged 60-70 in five cross-sectional waves (1999, 2004, 2009, 2014, 2019) of the German Survey on Volunteering (Deutscher Freiwilligensurvey: FWS) to investigate how the association of retirement status and volunteering has changed. We found a negative association of retirement and volunteering, which was not significant once controlling for age, education, gender and region. While engagement levels seem to have increased with historical time, voluntary work seems to have become less time consuming. There were no historical differences in the association of retirement and volunteering. Our results highlight the role of historical time and contextual factors when investigating volunteering and post-retirement activities.

How Are Tornadic Supercell Soundings Significantly Different From Nearby Baseline Environments?

AbstractThis study explores how tornadic supercell soundings significantly differ from the same‐location and same‐hour baseline environment soundings, sampled from the days prior to or following the event. Permutation testing is used to identify whether sounding‐derived parameters mixed‐layer convective available potential energy and 0–1 km storm‐relative helicity are significantly different between the tornadic and baseline environment. Typically, in an environment with marginal values of certain key environmental parameters, anomalous values of those environmental parameters are more strongly associated with supercell tornadoes. Furthermore, many tornadic events already exhibit environmental conditions favorable for tornadic supercells a day prior to the event itself. Generally, supercell tornadoes that occur during typical peak tornadic activity time frames are easier to distinguish from baseline (non‐tornadic) environments compared to those occurring in other time frames. Spatiotemporal variations of distinguishability between tornadic and baseline environmental parameters add complexity to traditional parameter‐based fixed threshold forecasting.