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Recent research has shown an indirect effect of generativity on fear of death through ego-integrity in older adults. The present paper aims at demonstrating that the indirect effect is valid even when controlling for social desirability. For that purpose, participants (N = 260 German adults) in study 1 provided self-reports on generativity, ego-integrity, fear of death, and social desirability. Analyses confirmed the indirect effect when the tendency for socially desirable responding was statistically controlled. In study 2, participants (N = 133 German adults) also reported on their generativity and ego-integrity. Fear of death, however, was assessed with a reaction time-based measure (i.e., the Implicit Associations Test). Again, the indirect effect could be confirmed. Taken together, the studies lend further credibility to the extant findings on the indirect effect of generativity on fear of death through ego-integrity. [ABSTRACT FROM AUTHOR]

Grammatical knowledge has often been investigated in wug tests, in which participants inflect pseudo-words. It was argued that in inflecting these pseudo-words, speakers apply their knowledge of word formation. However, it remains unclear what exactly this knowledge is and how it is learned. According to one theory, the knowledge is best characterised as abstractions that specify how units are combined. Another theory maintains that it is best characterised by memory-based analogy. In both cases the knowledge is learned by association based on positive evidence alone. In this paper, we model the classification of pseudo-words to Maltese plurals using a shallow neural network trained with an error-driven learning algorithm. We demonstrate that the classifications mirror those of Maltese speakers in a wug test. Our results indicate that speakers rely on gradient knowledge of a relation between the phonetics of whole words and plural classes, which is learned in an error-driven way. [ABSTRACT FROM AUTHOR]

Assessing the impact of dysarthria on a patient's ability to communicate should be an integral part of patient management. However, due to the high demands on reliable quantification of communication limitations, hardly any formal clinical tests with approved psychometric properties have been developed so far. This study investigates a web-based assessment of communication impairment in dysarthria, named KommPaS. The test comprises measures of intelligibility, naturalness, perceived listener effort and communication efficiency, as well as a total score that integrates these parameters. The approach is characterized by a quasi-random access to a large inventory of test materials and to a large group of naïve listeners, recruited via crowdsourcing. As part of a larger research program to establish the clinical applicability of this new approach, the present paper focuses on two psychometric issues, namely specificity and sensitivity (study 1) and retest-reliability (study 2). Study 1: KommPaS was administered to 54 healthy adults and 100 adult persons with dysarthria (PWD). Non-parametric criterion-based norms (specificity: 0.95) were used to derive a standard metric for each of the four component variables, and corresponding sensitivity values for the presence of dysarthria were identified. Overall classification accuracy of the total score was determined using a ROC analysis. The resulting cutscores showed a high accuracy in the separation of PWD from healthy speakers for the naturalness and the total score. Study 2: A sub-group of 20 PWD enrolled in study 1 were administered a second KommPaS examination. ICC analyses revealed good to excellent retest reliabilities for all parameters. [ABSTRACT FROM AUTHOR]

Background: Previous research has shown that teachers hold misconceptions about multimedia learning (e.g., multimedia instruction needs to be adapted to students' learning styles), which may be at odds with evidence‐based teaching. Objectives: Refutation texts are a classical method to reduce misconceptions and thus to stimulate conceptual change. We wanted to know whether making use of a computer algorithm to personalize refutation texts would best initiate teachers' conceptual change. Methods: We designed an online experiment, in which N = 129 in‐service teachers read either (1) expository texts (without direct refutation), (2) common refutation texts, or (3) personalized refutation texts. The teachers filled in a misconception questionnaire pre and post to assess their conceptual change. Results and Conclusions: Statistical analyses revealed that personalized refutation texts initiated the strongest conceptual change, which was driven by increased feelings of guilt and shame. Common refutation texts did not foster teachers' conceptual change as compared to expository texts. These findings indicate that refutation texts should be personalized for experienced practitioners such as teachers. Takeaways: Personalized refutation seems to be promising in the context of online teacher training programs. Further research should test to which extent the present findings also apply to other groups of experienced learners or practitioners. Lay Description: What is already known about this topic?: Teachers hold misconceptions about multimedia learning (e.g., learning materials should be adapted to students' individual learning styles, such as visualizers or verbalizers).Refutation texts, naming a commonly held misconception, disproving it and introducing a scientific explanation, are a common means to reduce misconceptions.Personalization fosters learning by drawing the learner's attention toward the discrepancy between their own beliefs and the learning material, further creating an impasse experience.Said impasse experience may trigger teachers' conceptual change, as, for teachers' conceptual change, a certain degree of discomfort is required. Yet, anger, caused by lessoning teachers on their topic may cause repulse and hamper learning. What this paper adds?: With a computer algorithm, we can efficiently personalize refutation texts by automatically matching them to teachers' answers in a pre‐test. Such a personalized refutation instruction may especially foster conceptual change.Within a randomized experiment, the personalized refutation instruction worked best compared to common refutation texts and expository texts.Feelings of guilt and shame moderated the effect of a personalized refutation, as teachers felt more addressed in their misconceptions and thus experienced the required impasse experience.Feelings of anger did not play an important role within our experiment. The implications of study findings for practitioners: Computer algorithms enable efficient personalization of instruction to better deal with heterogeneous groups of learners (e.g., with big differences in prior knowledge or experience, such as in the case of in‐service teachers).Refutation texts work better for teachers when they are personalized. Common refutation texts do not work better than expository texts.An advantage of digital instruction is the use of algorithms to efficiently personalize instructions even for larger groups. [ABSTRACT FROM AUTHOR]

During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge. [ABSTRACT FROM AUTHOR]

The impact of algorithms on everyday life is ever increasing. Medicine and public health are not excluded from this development – algorithms in medicine do not only challenge, change and inform research (methods) but also clinical situations. Given this development, questions arise concerning the competency level of prospective physicians, thus medical students, on algorithm related topics. This paper, based on a master's thesis in library and information science written at Humboldt‐Universität zu Berlin, gives an insight into this topic by presenting and analysing the results of a knowledge test conducted among medical students in Germany. F. J. [ABSTRACT FROM AUTHOR]

[Display omitted] • We simulate renewables pathways under different support phase-out scenarios. • We extend an investment algorithm by preferences and calibrated returns. • Our improved algorithm incorporates more heterogeneity resulting in stronger effects. • Further auction support is necessary for most capacity targets in the case countries. • Countries should coordinate policy changes due to cross-border effects. Support schemes have been central to the expansion of renewable electricity globally and in the European Union. As technologies mature, individual member states may decide to phase out these policies. While previous research has shown that such policy changes affect investors' decisions, we investigate how they affect pathways and electricity prices by simulating investment decisions in an agent-based model in two case countries. This paper contributes and applies an adapted investment decision algorithm that incorporates empirically observed technology and return preferences and is calibrated by return observations. The new algorithm yields more refined and stronger effects compared to its predecessor. Results show that the phase-out of auctions in Germany and the Netherlands slows down their deployment of renewable capacity by up to ∼60% and ∼35%, respectively. With the exception of photovoltaics and onshore wind projects in the Netherlands, the targeted capacities can only be reached by continuing support in both countries. Furthermore, ending support in a large country like Germany leads to higher electricity prices and fosters a market-driven but insufficient capacity expansion in smaller neighbours like the Netherlands. As the electricity grids in many countries are strongly interconnected, such cross-border effects are of international relevance. Our findings suggest that continued auctions may be necessary and that countries should coordinate policy changes to stay on track for meeting their renewables targets. [ABSTRACT FROM AUTHOR]

As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks' layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision. [ABSTRACT FROM AUTHOR]