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Prevalence of sexual aggression victimization and perpetration in a German university student sample
(2021)
This study examined the prevalence of sexual aggression perpetration and victimization in a sample of 1,172 students (755 female, 417 male) from four universities in Germany. All participants were asked about both victimization by, and perpetration of, sexual aggression since the age of 14 years, using the Sexual Aggression and Victimization Scale (SAV-S). Prevalence rates were established for different coercive strategies, sexual acts, and victim-perpetrator relationships. Both same-sex and opposite-sex victim-perpetrator constellations were examined. The overall victimization rate was 62.1% for women and 37.5% for men. The overall perpetration rate was 17.7% for men and 9.4% for women. Prevalence rates of both victimization and perpetration were higher for participants who had sexual contacts with both opposite-sex and same-sex partners than for participants with exclusively opposite-sex partners. Significant overlap was found between victim and perpetrator status for men and women as well as for participants with only opposite-sex and both opposite-sex and same-sex partners. A disparity between (higher) victimization and (lower) perpetration reports was found for both men and women, suggesting a general underreporting of perpetration rather than a gendered explanation in terms of social desirability or the perception of consent cues. The findings are placed in the international research literature on the prevalence of sexual aggression before and after the #metoo campaign, and their implications for prevention efforts are discussed.
RESUME Cette etude propose d'explorer et d'identifier des moments particuliers oU le changement linguistique se produit, afin de confirmer ou de rejeter l'idee d'une periode specifique designee par le terme << francais preclassique >>, avec une rupture - ou frontiere chronolectale - detectable autour de 1630 (cf. Ayres-Bennett et Caron, 2016). Afin de verifier dans quelle mesure cette chronologie peut etre confirmee, il est necessaire de multiplier des analyses fines et pointues sur des traits linguistiques qui ont subi des changements a l'epoque en question et d'interroger une gamme de textes qui refletent la variation discursive et pragmatique, au lieu de consulter le canon des traditions textuelles actuellement disponibles sur des bases numerisees, qui sont essentiellement litteraires. C'est pourquoi nous avons consulte des sources de nature differente, qui pourraient attester des usages emergents, a savoir les corpus du Reseau Corpus Francais Preclassique et Classique (RCFC). Seront presentes les resultats de deux etudes de cas (la recategorisation des formes dedans/dessous/dessus/dehors et la montee des clitiques), abondamment discutes par les remarqueurs.
This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in space applications by combining the real-time Soft Error Rate (SER) measurement with SRAM-based detector and the offline trained machine learning model. The proposed approach is intended for the self-adaptive fault-tolerant multiprocessing systems employed in space applications. With respect to the state-of-the-art, our solution allows for predicting the SER 1 h in advance and fine-grained hourly tracking of SER variations during SPEs as well as under normal conditions. Therefore, the target system can activate the appropriate mechanisms for radiation hardening before the onset of high radiation levels. Based on the comparison of five different machine learning algorithms trained with the public space flux database, the preliminary results indicate that the best prediction accuracy is achieved with the recurrent neural network (RNN) with long short-term memory (LSTM).
Foraging is risky and involves balancing the benefits of resource acquisition with costs of predation. Optimal foraging theory predicts where, when and how long to forage in a given spatiotemporal distribution of risks and resources. However, significant variation in foraging behaviour and resource exploitation remain unexplained. Using single foragers in artificial landscapes of perceived risks and resources with diminishing returns, we aimed to test whether foraging behaviour and resource exploitation are adjusted to risk level, vary with risk during different components of foraging, and (co)vary among individuals. We quantified foraging behaviour and resource exploitation for 21 common voles (Microtus arvalis). By manipulating ground cover, we created simple landscapes of two food patches varying in perceived risk during feeding in a patch and/or while travelling between patches. Foraging of individuals was variable and adjusted to risk level and type. High risk during feeding reduced feeding duration and food consumption more strongly than risk while travelling. Risk during travelling modified the risk effects of feeding for changes between patches and resulting evenness of resource exploitation. Across risk conditions individuals differed consistently in when and how long they exploited resources and exposed themselves to risk. These among-individual differences in foraging behaviour were associated with consistent patterns of resource exploitation. Thus, different strategies in foraging-under-risk ultimately lead to unequal payoffs and might affect lower trophic levels in food webs. Inter-individual differences in foraging behaviour, i.e. foraging personalities, are an integral part of foraging behaviour and need to be fully integrated into optimal foraging theory.
Advanced non-viral gene delivery experiments often require co-delivery of multiple nucleic acids. Therefore, the availability of reliable and robust co-transfection methods and defined selection criteria for their use in, e.g., expression of multimeric proteins or mixed RNA/DNA delivery is of utmost importance. Here, we investigated different co- and successive transfection approaches, with particular focus on in vitro transcribed messenger RNA (IVT-mRNA). Expression levels and patterns of two fluorescent protein reporters were determined, using different IVT-mRNA doses, carriers, and cell types. Quantitative parameters determining the efficiency of co-delivery were analyzed for IVT-mRNAs premixed before nanocarrier formation (integrated co-transfection) and when simultaneously transfecting cells with separately formed nanocarriers (parallel co-transfection), which resulted in a much higher level of expression heterogeneity for the two reporters. Successive delivery of mRNA revealed a lower transfection efficiency in the second transfection round. All these differences proved to be more pronounced for low mRNA doses. Concurrent delivery of siRNA with mRNA also indicated the highest co-transfection efficiency for integrated method. However, the maximum efficacy was shown for successive delivery, due to the kinetically different peak output for the two discretely operating entities. Our findings provide guidance for selection of the co-delivery method best suited to accommodate experimental requirements, highlighting in particular the nucleic acid dose-response dependence on co-delivery on the single-cell level.
The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis.
Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper, we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations. Often dynamical systems feature transitions depending on some, often persistent, unobserved background regime, and different regimes may exhibit different causal relations. Here, we assume a persistent and discrete regime variable leading to a finite number of regimes within which we may assume stationary causal relations. To detect regime-dependent causal relations, we combine the conditional independence-based PCMCI method [based on a condition-selection step (PC) followed by the momentary conditional independence (MCI) test] with a regime learning optimization approach. PCMCI allows for causal discovery from high-dimensional and highly correlated time series. Our method, Regime-PCMCI, is evaluated on a number of numerical experiments demonstrating that it can distinguish regimes with different causal directions, time lags, and sign of causal links, as well as changes in the variables' autocorrelation. Furthermore, Regime-PCMCI is employed to observations of El Nino Southern Oscillation and Indian rainfall, demonstrating skill also in real-world datasets.
Global measurements of incision rate typically show a negative scaling with the timescale over which they were averaged, a phenomenon referred to as the "Sadler effect." This time dependency is thought to result from hiatus periods between incision phases, which leads to a power law scaling of incision rate with timescale. Alternatively, the "Sadler effect" has been argued to be a consequence of the mobility of the modern river bed, where the timescale dependency of incision rates arises from a bias due to the choice of the reference system. In this case, incision rates should be independent of the timescale, provided that the correct reference system is chosen. It is unclear which model best explains the "Sadler effect," and, if a timescale dependency exists, which mathematical formulation can be used to describe it. Here, we present a compilation of 581 bedrock incision rates from 34 studies, averaged over timescales ranging from single floods to millions of years. We constrain the functional relationship between incision rate and timescale and show that time-independent incision rate is inconsistent with the global data. Using a power law dependence, a single constant power is inconsistent with the distribution of observed exponents. Therefore, the scaling exponent is site dependent. Consequently, incision rates measured over contrasting timescales cannot be meaningfully compared between different field sites without properly considering the "Sadler effect." We explore the controls on the variable exponents and propose an empirical equation to correct observed incision rates for their timescale dependency.
In the absence of a stranded auxiliary or modal, VP-topicalization in most Germanic languages gives rise to the presence of a dummy verb meaning 'do'. Cross-linguistically, this is a rather uncommon strategy as comparable VP-fronting constructions in other languages, e.g. Hebrew, Polish, and Portuguese, among many others, exhibit verb doubling. A comparison of several recent approaches to verb doubling in VP-fronting reveals that it is the consequence of VP-evacuating head movement of the verb to some higher functional head, which saves the (low copy of the) verb from undergoing copy deletion as part of the low VP copy in the VP-topicalization dependency. Given that almost all Germanic languages have such V-salvaging head movement, namely V-to-C movement, but do not show verb doubling, this paper suggests that V-raising is exceptionally impossible in VP-topicalization clauses and addresses the question of why it is blocked. After discussing and rejecting some conceivable explanations for the lack of verb doubling, I propose that the blocking effect arises from a bleeding interaction between V-to-C movement and VP-to-SpecCP movement. As both operations are triggered by the same head, i.e. C, the VP is always encountered first by a downward search algorithm. Movement of VP then freezes it and its lower copies for subextraction precluding subsequent V-raising. Crucially, this implies that there is no V-to-T raising in most Germanic languages. V2 languages with V-to-T raising, e.g. Yiddish, are correctly predicted to not exhibit the blocking effect.
Earthquakes often rupture across more than one fault segment. If such rupture segmentation occurs on a significant scale, a simple point-source or one-fault model may not represent the rupture process well. As a consequence earthquake characteristics inferred, based on one-source assumptions, may become systematically wrong. This might have effects on follow-up analyses, for example regional stress field inversions and seismic hazard assessments. While rupture segmentation is evident for most M-w > 7 earthquakes, also smaller ones with 5.5 < M-w < 7 can be segmented. We investigate the sensitivity of globally available data sets to rupture segmentation and their resolution to reliably estimate the mechanisms in presence of segmentation. We focus on the sensitivity of InSAR (Interferometric Synthetic Aperture Radar) data in the static near-field and seismic waveforms in the far-field of the rupture and carry out non-linear and Bayesian optimizations of single-source and two-sources kinematic models (double-couple point sources and finite, rectangular sources) using InSAR and teleseismic waveforms separately. Our case studies comprises of four M-w 6-7 earthquakes: the 2009 L'Aquila and 2016 Amatrice (Italy) and the 2005 and 2008 Zhongba (Tibet) earthquakes. We contrast the data misfits of different source complexity by using the Akaike informational criterion (AIC). We find that the AIC method is well suited for data-driven inferences on significant rupture segmentation for the given data sets. This is based on our observation that an AIC-stated significant improvement of data fit for two-segment models over one-segment models correlates with significantly different mechanisms of the two source segments and their average compared to the single-segment mechanism. We attribute these modelled differences to a sufficient sensitivity of the data to resolve rupture segmentation. Our results show that near-field data are generally more sensitive to rupture segmentation of shallow earthquakes than far-field data but that also teleseismic data can resolve rupture segmentation in the studied magnitude range. We further conclude that a significant difference in the modelled source mechanisms for different segmentations shows that an appropriate choice of model segmentation matters for a robust estimation of source mechanisms. It reduces systematic biases and trade-off and thereby improves the knowledge on the rupture. Our study presents a strategy and method to detect significant rupture segmentation such that an appropriate model complexity can be used in the source mechanism inference. A similar, systematic investigation of earthquakes in the range of M-w 5.5-7 could provide important hazard-relevant statistics on rupture segmentation. In these cases single-source models introduce a systematic bias. Consideration of rupture segmentation therefore matters for a robust estimation of source mechanisms of the studied earthquakes.
