65 research outputs found
The Influence of Major Satisfaction on Learning Engagement of Agriculture-Related Vocational Colleges in China: Taking Learning Motivation as a Mediating Variable
This study explores the intermediary structure model of agriculture-related vocational college students. Learning motivation is the mediating effect between major satisfaction and learning engagement. The objects of this study were 630 college students majoring in agriculture in SX city higher vocational colleges, Zhejiang Province. Three scale tools, namely, major satisfaction, learning engagement and learning motivation, were used to investigate the subjects. Likert five-point scale was used for scoring. The measurement model and structural model were constructed by structural equation model. The results show that major satisfaction has a positive effect on learning engagement. Learning motivation is a complete mediator between major satisfaction and learning engagement. The results show that the higher degree of major satisfaction has a good influence on learning engagement, but the strong learning motivation is more beneficial to learning engagement. Therefore, the school should not only improve students’ satisfaction with their major, but also stimulate students’ learning motivation in learning engagement
Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty
In this paper, the intra-day multi-interval rolling-window joint dispatch and
pricing of energy and reserve is studied under increasing volatile and
uncertain renewable generations. A look-ahead energy-reserve co-optimization
model is proposed for the rolling-window dispatch, where possible contingencies
and load/renewable forecast errors over the look-ahead window are modeled as
several scenario trajectories, while generation, especially its ramp, is
jointly scheduled with reserve to minimize the expected system cost considering
these scenarios. Based on the proposed model, marginal prices of energy and
reserve are derived, which incorporate shadow prices of generators' individual
ramping capability limits to eliminate their possible ramping-induced
opportunity costs or arbitrages. We prove that under mild conditions, the
proposed market design provides dispatch-following incentives to generators
without the need for out-of-the-market uplifts, and truthful-bidding incentives
of price-taking generators can be guaranteed as well. Some discussions are also
made on how to fit the proposed framework into current market practice. These
findings are validated in numerical simulations
Distributed Robust Bilinear State Estimation for Power Systems with Nonlinear Measurements
This paper proposes a fully distributed robust bilinear state-estimation (D-RBSE) method that is applicable to multi-area power systems with nonlinear measurements. We extend the recently introduced bilinear formulation of state estimation problems to a robust model. A distributed bilinear state-estimation procedure is developed. In both linear stages, the state estimation problem in each area is solved locally, with minimal data exchange with its neighbors. The intermediate nonlinear transformation can be performed by all areas in parallel without any need of inter-regional communication. This algorithm does not require a central coordinator and can compress bad measurements by introducing a robust state estimation model. Numerical tests on IEEE 14-bus, 118-bus benchmark systems, and a 1062-bus system demonstrate the validity of the method
Hybrid-space reconstruction with add-on distortion correction for simultaneous multi-slab diffusion MRI
Purpose: This study aims to propose a model-based reconstruction algorithm
for simultaneous multi-slab diffusion MRI acquired with blipped-CAIPI gradients
(blipped-SMSlab), which can also incorporate distortion correction.
Methods: We formulate blipped-SMSlab in a 4D k-space with kz gradients for
the intra-slab slice encoding and km (blipped-CAIPI) gradients for the
inter-slab encoding. Because kz and km gradients share the same physical axis,
the blipped-CAIPI gradients introduce phase interference in the z-km domain
while motion induces phase variations in the kz-m domain. Thus, our previous
k-space-based reconstruction would need multiple steps to transform data back
and forth between k-space and image space for phase correction. Here we propose
a model-based hybrid-space reconstruction algorithm to correct the phase errors
simultaneously. Moreover, the proposed algorithm is combined with distortion
correction, and jointly reconstructs data acquired with the blip-up/down
acquisition to reduce the g-factor penalty.
Results: The blipped-CAIPI-induced phase interference is corrected by the
hybrid-space reconstruction. Blipped-CAIPI can reduce the g-factor penalty
compared to the non-blipped acquisition in the basic reconstruction.
Additionally, the joint reconstruction simultaneously corrects the image
distortions and improves the 1/g-factors by around 50%. Furthermore, through
the joint reconstruction, SMSlab acquisitions without the blipped-CAIPI
gradients also show comparable correction performance with blipped-SMSlab.
Conclusion: The proposed model-based hybrid-space reconstruction can
reconstruct blipped-SMSlab diffusion MRI successfully. Its extension to a joint
reconstruction of the blip-up/down acquisition can correct EPI distortions and
further reduce the g-factor penalty compared with the separate reconstruction.Comment: 10 figures+tables, 8 supplementary figure
Sampling strategies and integrated reconstruction for reducing distortion and boundary slice aliasing in high-resolution 3D diffusion MRI
Purpose: To develop a new method for high-fidelity, high-resolution 3D multi-slab diffusion MRI with minimal distortion and boundary slice aliasing.
Methods: Our method modifies 3D multi-slab imaging to integrate blip-reversed acquisitions for distortion correction and oversampling in the slice direction (kz) for reducing boundary slice aliasing. Our aim is to achieve robust acceleration to keep the scan time the same as conventional 3D multi-slab acquisitions, in which data are acquired with a single direction of blip traversal and without kz-oversampling. We employ a two-stage reconstruction. In the first stage, the blip-up/down images are respectively reconstructed and analyzed to produce a field map for each diffusion direction. In the second stage, the blip-reversed data and the field map are incorporated into a joint reconstruction to produce images that are corrected for distortion and boundary slice aliasing.
Results: We conducted experiments at 7T in six healthy subjects. Stage 1 reconstruction produces images from highly under-sampled data (R = 7.2) with sufficient quality to provide accurate field map estimation. Stage 2 joint reconstruction substantially reduces distortion artifacts with comparable quality to fully-sampled blip-reversed results (2.4× scan time). Whole-brain in-vivo results acquired at 1.22 mm and 1.05 mm isotropic resolutions demonstrate improved anatomical fidelity compared to conventional 3D multi-slab imaging. Data demonstrate good reliability and reproducibility of the proposed method over multiple subjects.
Conclusion: The proposed acquisition and reconstruction framework provide major reductions in distortion and boundary slice aliasing for 3D multi-slab diffusion MRI without increasing the scan time, which can potentially produce high-quality, high-resolution diffusion MRI
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