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Academic self-regulation is a key factor for motivation and learning achievement. Yet with the large range of individual factors, this is not a one-size-fits-all proposition. This study of L2 Technical English students at two German universities explored learners’ expectations and motivations, in particular regarding self-regulation and self-efficacy via the individual’s time investment in self-led study. In an initial survey, learners (N=1646) reported on their English skill levels and anticipated learning habits. Complementarily, the retrospective survey investigated learners’ (N=796) actual behavior during the course, their perceptions of language skill improvement, and their satisfaction. The initial survey indicates a clear understanding that time investment in self-regulated study will lead to greater improvement, an outcome confirmed in the retrospective survey. Additionally, students who invested more time in their coursework were more satisfied with their achievement, although most learners acknowledge they should have studied more. The results verify that learners recognize the nexus between self-regulation and language skill improvement, yet university students are not satisfied with their capacity to self-regulate their language learning strategies. While differences in students’ skill levels and academic self-efficacy result in divergent degrees of progress, students of all types report benefits to their language skills when motivated to self-regulated study.
This paper focuses on Artificial Intelligence (AI) in robotic surgery. The question of safety and autonomy follows through the whole paper. Guidelines like Safety Integrity Levels which apply to dependable systems in general are described shortly. Overall, this work does not explicitly supply advantages of AI and instructional guidelines to build autonomous robots, instead, concentrates on challenges in the use of AI. In conclusion, there are still many open issues in the use of AI which cause potential gaps in reliability.
This article compares the standard electrical method of partial discharge detection with a novel optical detection method based on silicon photomultipliers. A third, complementary, single-loop antenna method is added to represent the ultra-high frequency method commonly used in gas-insulated switchgear/lines. A trio of air-insulated electrode designs that simulate the fundamental fault/discharge types in gaseous insulation (protrusion – corona discharge, floating conductive particle, surface discharges) are employed. Phase-resolved partial discharge activity patterns are compiled for each electrode design. The patterns are analyzed using spatial statistics and the interpretation of the obtained data trends explained by means of an example. Ultimately, the consistency and reliability of discharge detection by the optical methods for each fault/discharge type are evaluated, and suggestions for improvement are made.
Background:
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years the resting-state fMRI (rs-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere.
Methods:
Twenty patients suffering from brain lesions close to supposed language relevant cortical areas were included. Rs-fMRI and task-based (tb-fMRI) were performed for the purpose of preoperative language assessment. Tb-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language critical and language supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the tb-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice-index.
Results:
The RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly.
Conclusion:
In general, the results suggest that determining language dominance in the human brain is feasible both with tb-fMRI and rs-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years, the resting-state fMRI (RS-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere. Twenty patients suffering from brain lesions close to supposed language-relevant cortical areas were included. RS-fMRI and task-based (TB-fMRI) were performed for the purpose of preoperative language assessment. TB-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language-critical and language-supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the TB-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice index. Thereby, the RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly. In general, the results suggest that determining language dominance in the human brain is feasible both with TB-fMRI and RS-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
This paper shows how magnetic noises can be reduced with a wound field synchronous machine (WFSM) with magnetic
asymmetry compared to a permanent magnet synchronous machine (PSM), operating at base speed range and full load. In order to reproduce a real noise behavior, the two rotor types are operated in a complete electric drive unit (EDU) consisting of an electric motor, gearbox, inverter and overall housing. In the concept study, the noise characteristics of the two electric machines is evaluated and compared via mechanical finite element method (FEM) simulations using the equivalent radiated power (ERP) level and Campbell diagrams. Furthermore, it is shown that critical frequency orders can already be identified by the analysis of the magnetic force density from the electromagnetic design without computationally intensive ERP calculations. In this context, the ERP investigations have shown that the unique feature of the magnetically asymmetric WFSM is the reduction of the slot harmonics. At the current state of the art, the slot harmonics can only be reduced with a rotor skewing. The disadvantage of this is a reduction in performance and a more expensive production. It is also remarkable that the magnetic asymmetry reduces the slot harmonics more than the rotor skewing in the PSM. In addition, both machines are still considered with a short-pitched stator winding to optimize the overall noise level by reducing the 24th frequency order.
The paper deals with the investigation of magneto-sensitive elastomers(MSE) and their application in technical actuator systems. MSE consist of an elasticmatrix containing suspended magnetically soft and/or hard particles. Additionally,they can also contain silicone oil, graphite particles, thermoplastic components, etc.,in various concentrations in order to tune specific properties such as viscosity, con-ductivity and thermoelasticity, respectively. The focuses of investigations are thebeneficial properties of MSE in prototypes for locomotion and manipulation purposesthat possess an integrated sensor function. The research follows the principle of amodel-based design, i.e. the working steps are ideation, mathematical modelling,material characterization as well as building first functional models (prototypes). Thedeveloped apedal (without legs) and non-wheeled locomotion systems use the inter-play between material deformations and the mechanical motion in connection with theissues of control and stability. Non-linear friction phenomena lead to a monotonousforward motion of the systems. The aim of this study is the design of such mechanicalstructures, which reduce the control costs. The investigations deal with the movementand control of‘intelligent’mechanisms, for which the magnetically field-controlledparticle-matrix interactions provide an appropriate approach. The presented grippersenclose partially gripped objects, which is an advantage for handling sensitive objects.Form-fit grippers with adaptable contour at the contact area enable a uniform pressuredistribution on the surface of gripped objects. Furthermore, with the possibility ofactive shape adaptation, objects with significantly differing geometries can be gripped.To realise the desired active shape adaptation, the effect of field-induced plasticity ofMSE is used. The first developed prototypes mainly confirm the functional principles assuch without direct application. For this, besides the ability of locomotion andmanipulation itself, further technological possibilities have to be added to the systems.
In this work, a simple cost effective method to create tunable self-assembled three-dimensional nanostructure array-like nanoantennas on a tip of an optical quartz glass fiber is described. The structures are prepared using lithography-less dry etching. Gold nanoparticles are used as an etching mask using a thin metal-film thermal dewetting technique. The structures are applied as sensor for label-free analysis of organic substances in ppb range, such as drug residues in groundwater. The measurements are carried out by means of a Surface-enhanced Raman scattering (SERS) effect, an exchangeable sensor head, and a portable Raman device. This method allows in situ applications. Parathiocresol is used as a model substance to characterize the SERS cells. For metallization, gold and silver are compared. Reproducible SERS enhancement factors up to 10 7 are evaluated.