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This case study offers an overview of the impact and reach of the Springer Nature/Jisc Transformative Agreement in the UK over a six-year period. Through interviews and data analysis, this case study demonstrates the evolution of the UK TA to the end of 2021, discussing the benefits such partnerships bring to authors, institutions and funders of an agreement, and how TAs enable a sustainable transition to an open research future.

In 2021, Springer Nature commissioned an analysis of 60,567 articles published in 1,262 of its hybrid journals, measuring reach by the number of downloads and the attention reflected in the Altmetric Attention Score, and impact through the number of citations.This white paper presents the findings from that analysis, which show a clear advantage in reach and impact for articles published OA in hybrid journals compared to non-OA articles in the same journals, and for the first time also an advantage for full (Gold) OA articles compared to subscription articles where an earlier article version (such as an accepted manuscript (AM) or preprint) has been self-archived in an OA repository. Data set: https://figshare.com/articles/dataset/Going_for_gold_exploring_the_reach_and_impact_of_Gold_open_access_articles_in_hybrid_journals_data_set_/16860238 Infographic:https://figshare.com/articles/figure/Going_for_gold_exploring_the_reach_and_impact_of_Gold_open_access_articles_in_hybrid_journals_infographic_/16924417

Data cleaning is widely acknowledged as an important yet tedious task when dealing with large amounts of data. Thus, there is always a cost-benefit trade-off to consider. In particular, it is important to assess this trade-off when not every data point and data error is equally important for a task. This is often the case when statistical analysis or machine learning (ML) models derive knowledge about data. If we only care about maximizing the utility score of the applications, such as accuracy or F1 scores, many tasks can afford some degree of data quality problems. Recent studies analyzed the impact of various data error types on vanilla ML tasks, showing that missing values and outliers significantly impact the outcome of such models. In this paper, we expand the setting to one where data cleaning is not considered in isolation but as an equal parameter among many other hyper-parameters that influence feature selection, regularization, and model selection. In particular, we use state-of-the-art AutoML frameworks to automatically learn the parameters that benefit a particular ML binary classification task. In our study, we see that specific cleaning routines still play a significant role but can also be entirely avoided if the choice of a specific model or the filtering of specific features diminishes the overall impact.

Mitigation of pressure pulsations in the exhaust of a pulse detonation combustor is crucial for operation with a downstream turbine. For this purpose, a device termed the shock divider is designed and investigated. The intention of the divider is to split the leading shock wave into two weaker waves that propagate along separated ducts with different cross sections, allowing the shock waves to travel with different velocities along different paths. The separated shock waves redistribute the energy of the incident shock wave. The shock dynamics inside the divider are investigated using numerical simulations. A second-order dimensional split finite volume MUSCL-scheme is used to solve the compressible Euler equations. Furthermore, low-cost simulations are performed using geometrical shock dynamics to predict the shock wave propagation inside the divider. The numerical simulations are compared to high-speed schlieren images and time-resolved total pressure recording. For the latter, a high-frequency pressure probe is placed at the divider outlet, which is shown to resolve the transient total pressure during the shock passage. Moreover, the separation of the shock waves is investigated and found to grow as the divider duct width ratio increases. The numerical and experimental results allow for a better understanding of the dynamic evolution of the flow inside the divider and inform its capability to reduce the pressure pulsations at the exhaust of the pulse detonation combustor.

One possibility for reducing fuel consumption is to fly in the upwind field of the wake vortex generated by an aircraft that is flying ahead. Migratory birds use this principle. Manually flying an aircraft at the point of optimal fuel reduction is not suited for routine flight operations as the pilot workload is excessively high. Hence, an autopilot function has to carry out this task. For designing the autopilot, a flight mechanical simulation with a wake vortex velocity model is required that has the ability to calculate the vortex-induced velocity fields. This paper contributes to the choice of a real-time simulation method for modelling vortex-induced velocities that the wake vortex of a leading aircraft generates and that the trailing aircraft shall use during fuel-saving formation flight.Two different wake vortex velocity models are introduced and compared during steady, horizontal flight. One model is based on the Lifting Line Method (LLM) and the other on the unsteady Vortex Lattice Method (VLM). Both models are able to calculate the wake vortex roll-up phase for arbitrary lift distributions, whereas the commonly used Single Horseshoe Vortex Model (SHVM) ignores the near-field roll up. The differences in the induced upwind distribution and vortex filament position are analysed for coarse spatial and temporal discretisation that the real-time constraint requires. Despite the more stringent simplifications of LLM, both methods yield similar filament positions and similar velocity fields for the same discretisation of the lifting surfaces. Finally, the influence of the discretisation parameters is discussed and parameter values are recommended for using VLM and LLM in real-time flight simulations.

Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10–200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene–graphene dynamics induced by nuclear quantum effects and their strong contributions to the hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.

We introduce the class of "smooth rough paths" and study their main properties. Working in a smooth setting allows us to discard sewing arguments and focus on algebraic and geometric aspects. Specifically, a Maurer-Cartan perspective is the key to a purely algebraic form of Lyons extension theorem, the renormalization of rough paths in the spirit of [Bruned, Chevyrev, Friz, Prei{\ss}, A rough path perspective on renormalization, J. Funct. Anal. 277(11), 2019] as well as a related notion of "sum of rough paths". We first develop our ideas in a geometric rough path setting, as this best resonates with recent works on signature varieties, as well the renormalization of geometric rough paths. We then explore extensions to the quasi-geometric and the more general Hopf algebraic setting., Comment: 47 pages

ZusammenfassungVirtual Reality Lernumgebungen bieten besonderes Potenzial für die Vermittlung komplexer Inhalte und Fähigkeiten im beruflichen Kontext. In solchen Lernumgebungen lassen sich authentische Arbeitsaufgaben umsetzen anhand derer anwendungsnah Kompetenzen erlernt werden können. In der VR Lernumgebung MARLA wird die Fehlerdiagnose in den Bereichen Elektro- und Metalltechnik in Form eines achtstufigen Prozesses anhand eines konkreten Beispiels auf einer Offshore-Windenergieanlage trainiert. Auszubildende bekommen dafür in Anlehnung an den Cognitive Apprenticeship Ansatz zunächst von einem Non-Player Character innerhalb der Anwendung die einzelnen Schritte erklärt und vorgemacht, bevor sie anschließend schrittweise selbständig den Prozess durchlaufen. Der vorliegende Beitrag stellt die Anwendung und ihre Entwicklung vor und berichtet Ergebnisse der formativen Evaluation, welche wichtige Erkenntnisse für die Ausgestaltung der Umgebung geliefert hat.Praktische Relevanz Im vorliegenden Beitrag wird zum einen auf theoretische Erkenntnisse hinsichtlich des Potentials von Virtual Reality Anwendungen für die berufliche Bildung eingegangen. Zum anderen werden Erfahrungen aus dem Bereich der konzeptionellen Entwicklung der VR Anwendung und der empirischen Evaluation der Anwendung präsentiert. Beide Punkte haben zum Ziel einen Beitrag zur Weiterentwicklung der Weiterbildung im Arbeitskontext zu leisten.

Tropical Arenosols may be challenging for agricultural use, particularly in semi-arid regions. The aim of this study was to evaluate the impact of the addition of increasing shares of biochar and clay on the nutrient sorption capacity of a tropical Arenosol. In batch equilibrium experiments, the sorption of ammonium-N ($$\hbox {NH}_{4}^{+}{\text{-N}}$$ NH 4 + -N ), nitrate-N ($$\text {NO}_{3}^{-}{\text{-N}}$$ NO 3 - -N ), potassium ($$\text {K}^{+}$$ K + ), and phosphate-P ($$\text {PO}_{4}^{3-}{\text{-P}}$$ PO 4 3 - -P ) was quantified for mixtures of an Arenosol with increasing shares of biochar and clay (1%, 2.5%, 5%, 10%, 100%) and the unmixed Arenosol, biochar, and clay. The mid-temperature biochar was produced from Prosopis juliflora feedstock; the clayey material was taken from the sedimentary parent material of a temporarily dry lake. Only the Arenosol–biochar mixture with 10% biochar addition and the biochar increased the $$\text {NH}_{4}^{+}{\text{-N}}$$ NH 4 + -N maximum sorption capacity ($$q_{max}$$ q max ) of the Arenosol, by 34% and 130%, respectively. The $$q_{max}$$ q max of $$\text {PO}_{4}^{3-}{\text{-P}}$$ PO 4 3 - -P slightly increased with ascending biochar shares (1–10%) by 14%, 30%, 26%, and 42%, whereas the undiluted biochar released $$\text {PO}_{4}^{3-}{\text{-P}}$$ PO 4 3 - -P . Biochar addition slightly reduced $$\text {NO}_{3}^{-}{\text{-N}}$$ NO 3 - -N release from the Arenosol but strongly induced $$\text {K}^{+}$$ K + release. On the other hand, clay addition of 10% and clay itself augmented $$q_{max}$$ q max of $$\text {NH}_{4}^{+}{\text{-N}}$$ NH 4 + -N by 30% and 162%; ascending clay rates (1–100%) increased $$q_{max}$$ q max for $$\text {PO}_{4}^{3-}{\text{-P}}$$ PO 4 3 - -P by 78%, 130%, 180%, 268%, and 712%. Clay rates above 5% improved $$\text {K}^{+}$$ K + sorption; however, no $$q_{max}$$ q max values could be derived. Sorption of $$\text {NO}_{3}^{-}{\text{-N}}$$ NO 3 - -N remained unaffected by clay amendment. Overall, clay addition proved to enhance the nutrient sorption capacity of the Arenosol more effectively than biochar; nonetheless, both materials may be promising amendments to meliorate sandy soils for agricultural use in the semi-arid tropics.

Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires significant insights into expected job runtimes and scaling behavior, resource characteristics, input data distributions, and other factors. Unable to estimate performance accurately, users frequently overprovision resources for their jobs, leading to low resource utilization and high costs.In this paper, we present major building blocks towards a collaborative approach for optimization of data processing cluster configurations based on runtime data and performance models. We believe that runtime data can be shared and used for performance models across different execution contexts, significantly reducing the reliance on the recurrence of individual processing jobs or, else, dedicated job profiling. For this, we describe how the similarity of processing jobs and cluster infrastructures can be employed to combine suitable data points from local and global job executions into accurate performance models. Furthermore, we outline approaches to performance prediction via more context-aware and reusable models. Finally, we lay out how metrics from previous executions can be combined with runtime monitoring to effectively re-configure models and clusters dynamically.

Die Rolle von 3D-Modellen hat sich für Unternehmen, die sich auf die Wertschöpfung im Bereich der physischen und virtuellen Konsumgüter konzentrieren, in den vergangenen Jahren erheblich verändert. Für herstellende und Handelsunternehmen stellen virtuelle Produkte heute für die Entwicklung und den Vertrieb von physischen Gütern ein essenzielles Medium dar, wobei vermehrt auf virtuelle Umgebungen basierend auf Augmented und Virtual Reality zurückgegriffen wird. Eine Vielzahl an Spieleentwicklern hingegen generiert einen maßgeblichen Teil ihrer Einnahmen nicht mehr durch den Vertrieb ihrer Software, sondern den Verkauf von virtuellen Gütern in ihren Spieleumgebungen. Ziel der Studie ist es, basierend auf einer Literaturanalyse, die Rolle von 3D-Modellen in den Wertschöpfungsprozessen beider Domänen zu beschreiben sowie die durch die beschriebenen Trends entstandenen Ähnlichkeiten und Unterschiede in den Prozessen zu identifizieren, die Möglichkeiten zur Kooperation und dem Austausch von Wissen bieten. Die Ergebnisse implizieren, dass durch den Einsatz von 3D-Modellen über den gesamten Wertschöpfungsprozess neue 3D-Modell-Arten entstanden sind, die mit ihren jeweiligen Abhängigkeiten in einem Wertschöpfungsprozessmodell festgehalten sind. Zudem zeigen die Ergebnisse, dass nicht-funktionale Attribute von 3D-Modellen von zunehmender Bedeutung sind, die Nutzer eine tragende Rolle in den Wertschöpfungsprozessen spielen und sich Möglichkeiten zur Kooperation hinsichtlich der Erstellung von Augmented und Virtual Reality Umgebungen, der Produktion voneinander abhängiger Güter und dem Austausch von 3D-Modellen basierend auf virtuellen Assets ergeben. The role of 3D models has changed significantly in recent years for companies focused on physical and virtual consumer goods. For manufacturing and retail firms, the use of virtual products and technologies such as augmented and virtual reality are essential for the development and distribution of physical goods. Many game developers, on the other hand, no longer generate the significant part of their revenues through the distribution of their software, but through the sale of virtual goods in their game environments. Based on a literature analysis, the aim of the study is to describe the role of 3D models in the value creation processes of both domains and to identify similarities and differences in the processes which offer opportunities for cooperation and the exchange of knowledge. The results imply that the use of 3D models across the value creation process has created new 3D model types that are described with their respective dependencies in a value creation process model. Furthermore, the results show that non-functional attributes of 3D models are of increasing importance, as are the users for the value creation processes. Opportunities for cooperation arise with regard to the creation of augmented and virtual reality environments, the production of interdependent goods and the exchange of 3D models based on virtual assets.

Advanced engineering materials 23(11), 2001502 (2021). doi:10.1002/adem.202001502 special issue: "Neutrons and Synchrotron Radiation - Unique Tools for the Characterization of Materials", Laser powder bed fusion (LPBF) is a metal additive manufacturing technology,which enables the manufacturing of complex geometries for various metals andalloys. Herein, parts made from commercially pure titanium are studied usingin situ synchrotron radiation diffraction experiments. Both the phase transformationand the internal stress buildup are evaluated depending on the processingparameters. For this purpose, evaluation approaches for both temperatureand internal stresses from in situ diffraction patterns are presented. Four differentparameter sets with varying energy inputs and laser scanning strategiesare investigated. A combination of a low laser power and scanning speed leads toa more homogeneous stress distribution in the observed gauge volumes. Theresults show that the phase transformation is triggered during the primarymelting and solidification of the powder and subsurface layers. Furthermore, thestress buildup as a function of the part height during the manufacturing processis clarified. A stress maximum is formed below the part surface, extending intodeeper layers with increasing laser power. A temperature evaluation approach forabsolute internal stresses shows that directional stresses decrease sharply duringlaser impact and reach their previous magnitude again during cooling., Published by Deutsche Gesellschaft fu��r Materialkunde, Frankfurt, M.

We present for the first time the climatology of medium-scale traveling ionospheric disturbances (MSTIDs) by using Global Positioning System (GPS) receiver networks on geomagnetically quiet days (Kp ≤ 3) over the North African region during 2008-2016. The ionospheric Total Electron Content (TEC) were estimated from the dual-frequency GPS measurements, and the TEC perturbations (dTEC) data were derived from the estimated TEC data. We focused on the TEC perturbations (dTEC) associated with medium-scale traveling ionospheric disturbances (MSTIDs) and statistically analyzed the MSTIDs characteristics, occurrence rate, diurnal and seasonal behavior as well as the interannual dependence. The oscillating wave-like pattern of MSTIDs showed a local and seasonal dependence of nighttime and daytime. The results showed that MSTIDs propagation direction is predominantly towards the South (equatorward), MSTIDs event period is (12 ≤ period ≤ 53 mins), and dominant amplitude (0.08 ≤ amp ≤ ~1.5 TECU), with a propagation velocity higher at daytime than nighttime. The amplitudes of the MSTIDs increase with solar activity. The study reveals that the spatio-temporal variation of MSTIDs depends on local time and solar activity. The study also shows that the disturbance occurrence time is dominant within the hours of (1200–1600 LT), and (1000–1400 LT) in December solstice at daytime for stations located in the Northwest (NW) and Northeast (NE) part of the African region, respectively. While at the nighttime, the MSTIDs exhibits variability in disturbance occurrence time around (NW: 2100–0200 LT) and (NE: 1900–0200 LT) in June solstice, but get extended to March equinox during solar maximum (2014). The mean phase velocity in daytime MSTIDs is higher than the nighttime in every season, except during June solstice. The study revealed that atmospheric gravity waves (AGWs) control the daytime MSTIDs occurrence for a selected day.