Particle verbs (e.g., look up) are lexical items for which particle and verb share a single lexical entry. Using event-related brain potentials, we examined working memory and long-term memory involvement in particle-verb processing. Dutch participants read sentences with head verbs that allow zero, two, or more than five particles to occur downstream. Additionally, sentences were presented for which the encountered particle was semantically plausible, semantically implausible, or forming a non-existing particle verb. An anterior negativity was observed at the verbs that potentially allow for a particle downstream relative to verbs that do not, possibly indexing storage of the verb until the dependency with its particle can be closed. Moreover, a graded N400 was found at the particle (smallest amplitude for plausible particles and largest for particles forming non-existing particle verbs), suggesting that lexical access to a shared lexical entry occurred at two separate time points.
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BACKGROUND:Knowledge on long-term participation is scarce for patients with paid employment at the time of stroke. OBJECTIVE:Describe the characteristics and the course of participation (paid employment and overall participation) in patients who did and did not remain in paid employment. METHODS:Patients with paid employment at the time of stroke completed questions on work up to 30 months after starting rehabilitation, and the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P, Frequency, Restrictions and Satisfaction scales) up to 24 months. Baseline characteristics of patients with and without paid employment at 30 months were compared using Fisher’s Exact Tests and Mann-Whitney U Tests. USER-P scores over time were analysed using Linear Mixed Models. RESULTS:Of the 170 included patients (median age 54.2 interquartile range 11.2 years; 40% women) 50.6% reported paid employment at 30 months. Those returning to work reported at baseline more working hours, better quality of life and communication, were more often self-employed and in an office job. The USER-P scores did not change statistically significantly over time. CONCLUSION:About half of the stroke patients remained in paid employment. Optimizing interventions for returning to work and achieving meaningful participation outside of employment seem desirable.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
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Nowadays, there is particular attention towards the additive manufacturing of medical devices and instruments. This is because of the unique capability of 3D printing technologies for designing and fabricating complex products like bone implants that can be highly customized for individual patients. NiTi shape memory alloys have gained significant attention in various medical applications due to their exceptional superelastic and shape memory properties, allowing them to recover their original shape after deformation. The integration of additive manufacturing technology has revolutionized the design possibilities for NiTi alloys, enabling the fabrication of intricately designed medical devices with precise geometries and tailored functionalities. The AM-SMART project is focused on exploring the suitability of NiTi architected structures for bone implants fabricated using laser powder bed fusion (LPBF) technology. This is because of the lower stiffness of NiTi alloys compared to Ti alloys, closely aligning with the stiffness of bone. Additionally, their unique functional performance enables them to dissipate energy and recover the original shape, presenting another advantage that makes them well-suited for bone implants. In this investigation, various NiTi-based architected structures will be developed, featuring diverse cellular designs, and their long-term thermo-mechanical performance will be thoroughly evaluated. The findings of this study underscore the significant potential of these structures for application as bone implants, showcasing their adaptability for use also beyond the medical sector.
The eleven Universities forming the KreativEU consortium agreed to the common goal of establishing a fully European University, that places the creative potential derived from Europe’s cultural heritage at the heart of its teaching, research and knowledge transfer activities. Committing to a long-term institutional, structural and strategic cooperation the partners will jointly implement an ambitious yet inclusive vision for transforming the study of culture, identity, memory and heritage for the benefit of society. Building upon this strong foundation, KreativEU will provide innovative concepts, methods, and solutions to address both current and future challenges, contributing to a sustainable and harmonious future for communities and the environment alike. KreativEU recognizes the inseparable interconnection of tangible and intangible cultural heritage, as well as the interwoven nature of local and national traditions, crafts, cultural practices, and folklore. The alliance is dedicated to formulating cutting-edge educational and research programmes that reevaluate these elements and their associated ecological surroundings, the lived environment, especially in the context of the digital age. This ecocultural vision serves as the foundational principle guiding KreativEU's efforts, ensuring that a new generation of EU citizens working together across cultures, borders, languages, sectors and disciplines will be educated. Students from the KreativEU are expected to be leaders of change and enablers of societal transformation.To reach this vision, the KreativEU Alliance will work towards the completion of 8 work packages (WP1 - Governance and Management; WP2 - KreativEU Education; WP3 - KreativEU Research; WP4 - KreativEU Culture with and for society; WP5 - KreativEU Knowledge-creation and design network on Smart Sustainability WP6 - KreativEU Heritage European campus; WP7 - KreativEU Mobility; WP8 - Communication and Dissemination).Collaborative partners:Instituto Politécnico de Tomar, Escola Superior de Gestão de Tomar, D.A. Tsenov Academy of Economics, Johoceska Univerzita V Ceskych Budejovicich, Universita Degli Studi di Camerino, Universitaet Greifswald, Pilitechnika Opolska, Universitatae Valahia Targoviste, Trnavska Univerzita V Trnave, Sodestorns Hogskola, Adana Alparslan Turkes Bilim VE Teknoloji University
