Kaikki aineistot
Lisää
The present study investigated the variation in higher education students’ study burnout experiences and how they are related to academic success and social support needs. Similarities and differences between the international and domestic students were also explored. In this mixedmethods study, the data were collected through a self-reported questionnaire, and a total of 902 (response rate 42%) first year master’s students from the fields of arts, business and technology responded. Using Latent Profile Analysis (LPA), we detected three distinct study burnout risk profiles (No exhaustion or cynicism; Exhausted; Exhausted and cynical). The following distinct forms of social support needs were found using theory-based qualitative content analysis: informational, instrumental, emotional, and co-constructional support. We found out that the students with highest risk of burnout had the lowest grade point averages (GPAs). Further investigation showed that international students pass their courses despite the experiences of study burnout, even though the GPAs might deteriorate. When the domestic students experience study burnout symptoms, they both gain fewer study credits and earn lower GPAs. Finally, a relationship between the form of support needed and the burnout profile was identified.
Introduction: The study aim was to construct a technical and tactical analysis of women’s volleyball based on notational analysis in top-level and junior women’s European volleyball matches, to compare these two levels, and to clarify the differences between the winners and losers of a set. Material and Methods: Four matches from the 2010 FIVB Women’s Volleyball World Championships and 2010 CEV Junior Women’s European Championship 2010 were analyzed using Data Volley software. The number and performance level of different skills were recorded in total and were grouped according to the role of the players. Methods of scoring and attacking zones were also analyzed. Results: There were only slight differences between the two levels in terms of success in different skills. When the skill executions were compared between the winning and losing teams of a set within the levels, less successful skill executions and more errors in different skills were found for the losing teams. Conclusions: The results seem to indicate that there are only minor differences between adult and junior women’s volleyball at the highest level. Attacking seems to be the most important skill concerning winning in both levels. The efficiency of attacking seems to depend upon the quality and versatility of the setting and also from the physical abilities of the players.
This paper proposes a wideband 2-5GHz LO phase-shifting generator based on two digitally controlled delay lines. The concept is verified on a two-channel beamsteering direct-conversion receiver prototype implemented in 28nm CMOS. The novel generator provides both tunable phase-shifting and generation of I/Q components, achieving picosecond time resolution. The generator consumes 4.5-11.2mW and occupies 0.021mm2.
This paper proposes a wideband 2-5GHz LO phase-shifting generator based on two digitally controlled delay lines. The concept is verified on a two-channel beamsteering direct conversion receiver prototype implemented in 28nm CMOS. The novel generator provides both tunable phase-shifting and generation of I/Q components, achieving picosecond time resolution. The generator consumes 4.5-11.2mW and occupies 0.021mm.sq.
Synopsis In this study, deep convolutional neural networks (DCNN) are used to synthesize contrast-weighted magnetic resonance (MR) images from quantitative parameter maps of the knee joint obtained with magnetic resonance fingerprinting (MRF). Training of the neural networks was performed using data from 142 patients, for which both standard MR images and quantitative MRF maps of the knee were available. The study demonstrates that synthesizing contrast-weighted images from MRF-parameter maps is possible utilizing DCNNs. Furthermore, the study indicates a need to tune up the dictionary used in MRF so that the parameters expected from the target anatomy are well-covered.
In this article, we present a four-element Vivaldi antenna array and beamsteering receiver IC for the fifth-generation mobile network (5G) new radio (NR). The implemented receiver utilizes a delay-based local-oscillator phase shift technique for accurate beamsteering, and it exhibits 1° to 2.4° phase tuning capability for 2-5 GHz bandwidth accordingly. On-chip delay measurement is performed with pilot signal generation and delay estimation capable of 2-ps accuracy. The IC is fabricated on 28-nm CMOS technology, it occupies an area of 1.4x1.4 mm^2, including bonding pads, and consumes 22.8 mW at 2 GHz for single-receiver path operation. The receiver demonstrates wideband over-the-air reception with the prototype antennas.
Abstract Background: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. Purpose: To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. Study type: Retrospective. Population: Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33–35, gender distribution not available). Field strength and sequence: A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). Assessment: Data were divided into training, validation, test, and radiologist’s assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist’s assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. Statistical tests: Friedman’s test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. Results: The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3–4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss. Data conclusion: Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. Evidence level: 4 Technical efficacy: Stage 1.