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This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.
In any dimension d≥2, there is no known example of a low-discrepancy sequence which possesses Poisssonian pair correlations. This is in some sense rather surprising, because low-discrepancy sequences always have β-Poissonian pair correlations for all 0<β<1/d and are therefore arbitrarily close to having Poissonian pair correlations (which corresponds to the case β=1/d). In this paper, we further elaborate on the closeness of the two notions. We show that d-dimensional Kronecker sequences for badly approximable vectors α→ with an arbitrary small uniformly distributed stochastic error term generically have β=1/d-Poissonian pair correlations.
Previous work has indicated that testing can enhance memory for subsequently studied new information by reducing proactive interference from previously studied information. Here, we examined this forward testing effect in children’s spatial memory. Kindergartners (5–6 years) and younger (7–8 years) and older (9–10 years) elementary school children studied four successively presented 3 x 3 arrays, each composed of the same 9 objects. The children were asked to memorize the locations of the objects that differed across the four arrays. Following presentation of each of the first three arrays, memory for the object locations of the respective array was tested (testing condition) or the array was re-presented for additional study (restudy condition). Results
revealed that testing Arrays 1 to 3 enhanced children’s object location memory for Array 4 relative to restudying. Moreover, children in the testing condition were less likely to confuse Array 4 locations with previous locations, suggesting that testing reduces the buildup of proactive interference. Both effects were found regardless of age. Thus, the current findings indicate that testing is an effective means to resolve proactive interference and, in this way, to enhance children’s learning and remembering of spatial information even before the time of school entry.
This article examines interweaving collective histories in different formerly colonized regions of the world: 1. Migratory movements from the territory of the former Ottoman Empire to the Americas, specifically to Brazil, and 2. transregional migration between different regions within China. On the basis of empirical data, we discuss sociological biographical research as an approach to analyzing migration and social mobility as transgenerational processes. In the case of Syrians in Brazil who have fled from the civil war, these processes are reflected in transnational family structures, transgenerational mandates, and knowledge transmission. In the case of domestic migration within China, the “mission” that families give their children is social advancement through education (e.g., Crabb 2010; Fong 2004). In the context of anti-Western discourses in China, it can be demonstrated that postcolonial discourses are functional in the effort to regain former international strength and national prosperity, and that discourses on “becoming a modern citizen” pervade family aspirations. The article is intended as a plea for i) taking a closer look at historical and contemporary South-South relations, and ii) situating current migration movements historically. It ties into global historical and sociological debates on “shared/common histories” and “intertwined histories."
Die Gesundheitswirtschaft ist im hohen Maße von Technisierung und Digitalisierung betroffen, welche neue Kompetenzen bei den verantwortlichen Akteur:innen erfordert. Die Fähigkeiten, die für den professionellen Umgang mit diesen Entwicklungspro zessen relevant sind, werden in der Literatur mit verschiedenen Begriffen umschrieben: Inhalte wie Technik, Digitalität, Medien, Informatik, Computer oder Informations- und Kommunikationstechnologien (IKT) werden mit Fähigkeiten und Fertigkeiten re präsentierenden Begriffen wie Kompetenzen, Literacy oder Skills verknüpft. Deutlich wird bisher jedoch nicht, welche spezifi schen Fähigkeiten die im Diskurs geforderten Konstrukte jeweils adressieren, worin sie sich unterscheiden und welchen Beitrag sie zum Ziel beruflicher Handlungskompetenz leisten.
Many experts project generative AI will impact the types of competencies that are valued among working professionals. This is the first known academic study to explore the views of business practitioners about the impacts of generative AI on skill sets. This survey of 692 business practitioners showed that business practitioners widely use generative AI, with the most common uses involving research and ideation, drafting of business messages and reports, and summarizing and revising text. Business practitioners report that character-based traits such as integrity and soft skills will become more important. Implications for teaching business communication are discussed.
The bond between polymer fibers and the surrounding cementitious matrix is essential for the development of concrete reinforcement. The single fiber pull-out test (SFPT) is the standard characterization technique for testing the bond strength. However, the different phases of debonding cannot be distinguished by the SFPT. This study investigates the debonding of different polymer fibers from the surrounding cementitious matrix with a modified SFPT and proposes methods to change the SFPT setup to generate more valuable information on the debonding mechanism. The SFPT was equipped with linear variable differential transformers (LVDT), digital image correlation (DIC) and acoustic emission (AE) analysis. The results demonstrate that the modified SFPT allows a better understanding of the different phases of debonding during fiber pull-out. Furthermore, bond strength values calculated by different methods reveal that the chemical bond of the investigated polymers is not different as reported by previous studies. Deformation measurements performed using LVDTs and DIC are suitable measuring techniques to characterize the debonding mechanism in SFPT. A correlation between recorded AE and debonding phases was not found.
The necessity for resource-efficient manufacturing technologies requires new developments within the field of plastic processing. Lightweight design using wood fibers as sustainable reinforcement for thermoplastics might be one solution. The processing of wood fibers requires special attention to the applied thermal load. Even at low processing temperatures, the influence of the dwell time, temperature and shear force is critical to ensure the structural integrity of fibers. Therefore, this article compares different compounding rates for polypropylene with wood fibers and highlights their effects on the olfactory, visual and mechanical properties of the injection-molded part. The study compares one-step processing, using an injection-molding compounder (IMC), with two-step processing, using a twin-scew-extruder (TSE), a heating/cooling mixer (HCM) and an internal mixer (IM) with subsequent injection molding. Although the highest fiber length was achieved by using the IMC, the best mechanical properties were achieved by the HCM and IM. The measured oxidation induction time and volatile organic compound content indicate that the lowest amount of thermal damage occurred when using the HCM and IM. The advantage of one-time melting was evened out by the dwell time. The reinforcement of thermoplastics by wood fibers depends more strongly on the structural integrity of the fibers compared to their length and homogeneity
Die Reinraumtechnik zielt darauf ab, mit gerichteten partikelfreien Luftströmungen die im Reinraum stattfindenden Prozesse vor Fremdeinflüssen zu schützen. Die Spritzgussproduktion stellt die Reinraumtechnik dabei vor besondere Herausforderungen. Im Bereich des Spritzgusswerkzeugs können aufgrund des relativ hohen Temperaturunterschieds zur Reinraumluft Kamineffekte auftreten, die der Reinraumströmung entgegen wirken. Im Folgenden wird deshalb der thermische Einfluss eines Spritzgusswerkzeugs exemplarisch bei unterschiedlichen Temperaturen untersucht. Dazu werden Strömungsvisualisierungen mittels Nebel und Schlierenfotografie sowie Strömungssimulationen genutzt. Bereits relativ
niedrige Werkzeugtemperaturen von 40 °C reichten aus, um einen Kamineffekt im
Werkzeugbereich entstehen zu lassen, der die Reinraumströmung ungünstig beeinflusst. Erst durch den Einsatz einer Filter-Fan-Unit über dem kritischen Werkzeugbereich konnte die Luftströmung auch bei höheren Werkzeugtemperaturen kontrolliert werden.
This study presents an approach to collect and classify usage data of public charging infrastructure in order to predict usage based on socio-demographic data within a city. The approach comprises data acquisition and a two-step machine learning approach, classifying and predicting usage behavior. Data is acquired by gathering information on charging points from publicly available sources. The first machine learning step identifies four relevant usage patterns from the gathered data using an agglomerative clustering approach. The second step utilizes a Random Forest Classification to predict usage patterns from socio-demographic factors in a spatial context. This approach allows to predict usage behavior at locations for potential new charging points. Applying the presented approach to Munich, a large city in Germany, results confirm the adaptability in complex urban environments. Visualizing the spatial distribution of the predicted usage patterns shows the prevalence of different patterns throughout the city. The presented approach helps municipalities and charging infrastructure operators to identify areas with certain usage patterns and, hence different technical requirements, to optimize the charging infrastructure in order to help meeting the increasing demand of electric mobility.