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Eine evidenzbasierte Gestaltung von Studium und Lehre, wie sie heute normativ eingefordert wird, bedarf des integrierten Zusammenwirkens von Qualitätsmanagement und Hochschuldidaktik – aber gibt es dieses in der Praxis? Mit Blick auf die allgemeine Befundlage, aber auch anhand einer eigenen empirischen Untersuchung zeigt der Beitrag diesbezüglich auf, dass Qualitätsmanagement und Hochschuldidaktik als weitgehend desintegrierte Funktionsbereiche wahrgenommen werden und Evidenzbasierung in der Praxis folglich keinen sehr hohen Stellenwert genießt. Ausgehend von einer Ursachenanalyse wird auf die dysfunktionalen, aber auch auf die funktionalen Auswirkungen dieser Separierung aufmerksam gemacht.
Rats are a reservoir of human- and livestock-associated methicillin-resistant Staphylococcus aureus (MRSA). However, the composition of the natural S. aureus population in wild and laboratory rats is largely unknown. Here, 144 nasal S. aureus isolates from free-living wild rats, captive wild rats and laboratory rats were genotyped and profiled for antibiotic resistances and human-specific virulence genes. The nasal S. aureus carriage rate was higher among wild rats (23.4%) than laboratory rats (12.3%). Free-living wild rats were primarily colonized with isolates of clonal complex (CC) 49 and CC130 and maintained these strains even in husbandry. Moreover, upon livestock contact, CC398 isolates were acquired. In contrast, laboratory rats were colonized with many different S. aureus lineages—many of which are commonly found in humans. Five captive wild rats were colonized with CC398-MRSA. Moreover, a single CC30-MRSA and two CC130-MRSA were detected in free-living or captive wild rats. Rat-derived S. aureus isolates rarely harbored the phage-carried immune evasion gene cluster or superantigen genes, suggesting long-term adaptation to their host. Taken together, our study revealed a natural S. aureus population in wild rats, as well as a colonization pressure on wild and laboratory rats by exposure to livestock- and human-associated S. aureus, respectively.
In a multisource, lagged design field study of 66 consulting teams, we investigated the role of leader mood in unlocking the performance potential of functionally diverse teams. In line with our hypotheses, we found that, given high levels of leader positive mood, functional diversity was positively related to collective team identification. In contrast, given high levels of leader negative mood, functional diversity was positively associated with information elaboration in teams. Furthermore, results showed that functional diversity was most strongly related to team performance when both leader positive mood and leader negative mood were high. This study highlights the value of examining seemingly contradictory leadership aspects in the effort to gain a fuller understanding of how to foster performance in diverse teams.
Practitioner points
To effectively lead diverse teams, leaders need to navigate between the need to promote unique ideas (i.e., information elaboration) and the simultaneous need to pull together diverse members towards a common identity.
Leader mood addresses both of these needs. When the team leader exhibited a positive mood, team functional diversity was positively related to members' identification with the team. By contrast, when the team leader displayed a negative mood, team functional diversity was positively related to information elaboration.
Over a 12-day period, diverse teams performed best when the leader showed both positive and negative mood.
Leaders of diverse teams are required to be sensitive to the affective tone of their team and aware of how their emotional displays influence team members' moods and behaviours as well as team processes.
We consider rough metrics on smooth manifolds and corresponding Laplacians induced by such metrics. We demonstrate that globally continuous heat kernels exist and are Holder continuous locally in space and time. This is done via local parabolic Harnack estimates for weak solutions of operators in divergence form with bounded measurable coefficients in weighted Sobolev spaces.
The United Nations (UN) policy agenda on Preventing and Countering Violent Extremism (P/CVE) promotes a “holistic” approach to counterterrorism, which includes elements traditionally found in security and development programs. Advocates of the agenda increasingly emphasize the importance of gender mainstreaming for counterterrorism goals. In this article, I scrutinize the merging of the goals of gender equality, security, and development into a global agenda for counterterrorism. A critical feminist discourse-analytical reading of gender representations in P/CVE shows how problematic imageries of women as victims, economic entrepreneurs, and peacemakers from both the UN’s Sustainable Development Goals and the Women, Peace and Security agenda are reproduced in core UN documents advocating for a “holistic” P/CVE approach. By highlighting the tensions that are produced by efforts to merge the different gender discourses across the UN’s security and development institutions, the article underlines the relevance of considering the particular position of P/CVE at the security–development nexus for further gender-sensitive analysis and policies of counterterrorism.
This study aimed to investigate the relationship between the acute to chronic workload ratio (ACWR), based upon participant session rating of perceived exertion (sRPE), using two models [(1) rolling averages (ACWRRA); and (2) exponentially weighted moving averages (ACWREWMA)] and the injury rate in young male team soccer players aged 17.1 ± 0.7 years during a competitive mesocycle. Twenty-two players were enrolled in this study and performed four training sessions per week with 2 days of recovery and 1 match day per week. During each training session and each weekly match, training time and sRPE were recorded. In addition, training impulse (TRIMP), monotony, and strain were subsequently calculated. The rate of injury was recorded for each soccer player over a period of 4 weeks (i.e., 28 days) using a daily questionnaire. The results showed that over the course of the study, the number of non-contact injuries was significantly higher than that for contact injuries (2.5 vs. 0.5, p = 0.01). There were also significant positive correlations between sRPE and training time (r = 0.411, p = 0.039), ACWRRA (r = 0.47, p = 0.049), and ACWREWMA (r = 0.51, p = 0.038). In addition, small-to-medium correlations were detected between ACWR and non-contact injury occurrence (ACWRRA, r = 0.31, p = 0.05; ACWREWMA, r = 0.53, p = 0.03). Explained variance (r²) for non-contact injury was significantly greater using the ACWREWMA model (ranging between 21 and 52%) compared with ACWRRA (ranging between 17 and 39%). In conclusion, the results of this study showed that the ACWREWMA model is more sensitive than ACWRRA to identify non-contact injury occurrence in male team soccer players during a short period in the competitive season.
RainNet v1.0
(2020)
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900km × 900km and has a resolution of 1km in space and 5min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1h, a recursive approach was implemented by using RainNet predictions at 5min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events.
RainNet significantly outperforms the benchmark models at all lead times up to 60min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5mm h⁻¹. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15mm h⁻¹). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.
Carrion plays an essential role in shaping the structure and functioning of ecosystems and has far‐reaching implications for biodiversity conservation. The change in availability and type of carcasses throughout ecosystems can involve negative effects for scavenging communities. To address this issue, there have been recent conservation management measures of carrion provision in natural systems. However, the optimal conditions under which exposing carcasses to optimize conservation outcomes are still limited. Here, we used camera traps throughout elevational and vegetational gradients to monitor the consumption of 48 deer carcasses over a study period of six years by evaluating 270,279 photographs resulting out of 15,373 trap nights. We detected 17 species visiting carcass deployments, including five endangered species. Our results show that large carcasses, the winter season, and a heterogeneous surrounding habitat enhanced the frequency of carcass visits and the species richness of scavenger assemblages. Contrary to our expectations, carcass species, condition (fresh/frozen), and provision schedule (continuous vs single exposure) did not influence scavenging frequency or diversity. The carcass visitation frequency increased with carcass mass and lower temperatures. The effect of large carcasses was especially pronounced for mesopredators and the Eurasian lynx (Lynx lynx ). Lynx were not too influenced in its carrion acquisition by the season, but exclusively preferred remote habitats containing higher forest cover. Birds of prey, mesopredators, and top predators were also positively influenced by the visiting rate of ravens (Corvus corax ), whereas no biotic or abiotic preferences were found for wild boars (Sus scrofa ). This study provides evidence that any ungulate species of carrion, either in a fresh or in previously frozen condition, attracts a high diversity of scavengers especially during winter, thereby supporting earlier work that carcass provisions may support scavenger communities and endangered species.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.