E-PORTFOLIOS: THEIR IMPACT ON PRESERVICE TEACHERS\u27 SELF-DIRECTED LEARNING AND COMPUTER TECHNOLOGY SKILLS

Students in the Teacher Education Program at the University of Missouri-St. Louis have to complete their professional E-portfolios to be certified for the program. An E-portfolio demonstrates a future teacher¿s knowledge, skills, and abilities acquired through teaching and learning. Five qualitative case studies were investigated to understand how E-portfolios impact preservice teachers¿ self-directed learning readiness (SDLR) and computer technology skills (CTS). Data were gathered from the preservice teachers¿ pre- and post-questionnaires, interviews, observations, and their completed E-portfolios. Two internship students and three student teachers were observed creating their E-portfolios during a 16-week semester. During the period, some sought assistance from the E. Desmond Lee Technology and Learning Center Staff while others worked independently. Using the Self-Directed Learning Readiness Scale (Guglielmino, 1977), all the participants increased their SDLR scores. However, although each of their scores increased, they remained in their initial level. For example, if a person had an initial ¿above average¿ score (227-251), he or she stayed in the same level after creating an E-portfolio. Based on a CTS Questionnaire, which examined the preservice teachers¿ Internet, PowerPoint, Excel, and E-portfolio skills, just to name a few, each preservice teacher increased his or her computer technology skills. Thus, it appears that creating an E-portfolio can serve as a useful tool in helping preservice teachers enhance their self-direction and computer technology literacy. Teachers should carefully consider how computer technology should be used to further their goals of professional development. The knowledge gained from this study may assist adult educators in motivating student teacher candidates to use E-portfolios. Knowledge about the self-directed learning process would contribute to both theory and practice of self-directed learning in the digital age. In addition, this study may provide the foundation for further research into E-portfolio curriculum design and how to use E-portfolios as an assessment tool for effective professional development. Developing E-portfolios may help students in all programs improve their computer technology skills and trigger their self-direction and desire to learn. In addition, E-portfolios may provide faculty with an effective, alternative assessment tool (Barrett, 2000). Future research could examine more students in other teacher education programs

Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization

We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map

Experimental analysis of 3D flow structures around a floating dike

Floating dikes have several advantages over spur dikes including less influence on riverine sediment transport, bed topography, and ecosystems, and a good adaptability to fluvial conditions. Despite these advantages, floating dikes have not been used in many river regulation schemes due to the limited understanding of the 3D flow structures around floating dikes. In this study, a series of experiments were conducted to investigate the 3D flow structures around floating dikes. Results show that, after installing a floating dike on one side of a flume, the surface water flow is deflected to the opposite side of the flume, and a backflow develops around the outer and downstream side of the dike, where both the vertical turbulent intensity and the absolute magnitude of the Reynolds stress are relatively large. Due to the blocking effect of the dike, the cross-sectional area decreases, causing an increase in velocities below and alongside the dike, as well as a decrease in velocities upstream of the dike. Increasing the submerged depth or length of the dike results in an increase in flow velocity adjacent to the dike, as well as an increase in the vertical or lateral scale of the backflow. On the contrary, increasing the dike thickness leads to a weakening or disappearance of the backflow, along with a decrease in the acceleration rate of flow adjacent to the dike

AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection

The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios. In this work, we release a new public Short video sHot bOundary deTection dataset, named SHOT, consisting of 853 complete short videos and 11,606 shot annotations, with 2,716 high quality shot boundary annotations in 200 test videos. Leveraging this new data wealth, we propose to optimize the model design for video SBD, by conducting neural architecture search in a search space encapsulating various advanced 3D ConvNets and Transformers. Our proposed approach, named AutoShot, achieves higher F1 scores than previous state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being derived and evaluated on our newly constructed SHOT dataset. Moreover, to validate the generalizability of the AutoShot architecture, we directly evaluate it on another three public datasets: ClipShots, BBC and RAI, and the F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%, 0.9% and 1.2%, respectively. The SHOT dataset and code can be found in https://github.com/wentaozhu/AutoShot.git .Comment: 10 pages, 5 figures, 3 tables, in CVPR 2023; Top-1 solution for scene / shot boundary detection https://paperswithcode.com/paper/autoshot-a-short-video-dataset-and-state-o

Implementation of the Timetable Problem Using Self-assembly of DNA Tiles

DNA self-assembly is a promising paradigm for nanotechnology. Recently, many researches demonstrate that computation by self-assembly of DNA tiles may be scalable. In this paper, we show how the tile self-assembly process can be used for implementing the timetable problem. First the timetable problem can be converted into the graph edge coloring problem with some constraints, then we give the tile self-assembly model by constructing three small systems including nondeterministic assigning system, copy system and detection system to perform the graph edge coloring problem, thus the algorithm is proposed which can be successfully solved the timetable problem with the computation time complexity ofΘ(mn), parallely and at very low cost