Determinants of Behavioral Intention to Use Hybrid Education Among Painting Students in Public Universities in Chengdu, China

Purpose: The purpose of this study is to examining determinants of behavioral intention to use hybrid education among undergraduate students, majoring in painting at three public universities in Chengdu, China. Key variables are perceived ease of use (PEOU), perceived usefulness (PU), perceived satisfaction (PS), social influence (SI), performance expectancy (PE), facilitating conditions (FC), and behavioral intention (BI). Research design, data, and methods: The researchers used quantitative method by distributing questionnaire to 500 participants via offline and online channels. The sampling techniques involve judgmental, quota and convenience samplings. The content validity was approved by three experts, applying Item Objective Congruence (IOC) Index. All constructs were reserved by Cronbach’s Alpha coefficient values by pilot testing of 30 participants. Afterwards, Confirmatory Factor analysis (CFA) and Structural Equation Model (SEM) were executed in the data analysis, including goodness-of-fit, validities, and reliabilities. Results: All latent variables had a significant influence on behavioral intention. In addition, perceived ease of use had the strongest significant influence on perceived usefulness. Conclusion: Future researchers are recommended to extend the research model in considering to more variables in technology adoption theories in different region. Universities could improve hybrid education system to uplift students' engagement and learning performance

Influencing Factors of Behavior Intention of Master of Arts Students Towards Online Education in Chengdu Public Universities, China

Purpose: This study aims to investigate influencing factors of behavioral intentions to use online education of Master of Arts students from three public universities in the Chengdu region of China. The conceptual model contains perceived ease of use, perceived usefulness, social influence, effort expectancy, self-efficacy, perceived satisfaction, and behavioral intention. Research design, data and methodology: The researchers employed a quantitative approach of survey distribution to 501 participants. The sample techniques involve judgmental, quota and convenience sampling. The content validity method of Item Objective Congruence (IOC) Index was used, resulting all measuring items reserved by three experts. Pilot testing of 30 participants was approved under Cronbach’s Alpha reliability test. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were performed for data analysis, including goodness of model fits, validity, and reliability testing. Results: Perceived ease of use had the strongest influence on perceived usefulness toward behavioral intention. Furthermore, perceived usefulness, social influence, self-efficacy, perceived satisfaction, except effort expectancy, significantly impacted behavioral intention. Conclusions: The findings lead to the recommendations that educational administrators at public universities to enhance the behavioral intention to use online education by providing well-design online learning system and promote various benefits of using

Let AI Entertain You: Increasing User Engagement with Generative AI and Rejection Sampling

While generative AI excels in content generation, it does not always increase user engagement. This can be attributed to two main factors. First, generative AI generates content without incorporating explicit or implicit feedback about user interactions. Even if the generated content seems to be more informative or well-written, it does not necessarily lead to an increase in user activities, such as clicks. Second, there is a concern with the quality of the content generative AI produces, which often lacks the distinctiveness and authenticity that human-created content possesses. These two factors can lead to content that fails to meet specific needs and preferences of users, ultimately reducing its potential to be engaging. This paper presents a generic framework of how to improve user engagement with generative AI by leveraging user feedback. Our solutions employ rejection sampling, a technique used in reinforcement learning, to boost engagement metrics. We leveraged the framework in the context of email notification subject lines generation for an online social network, and achieved significant engagement metric lift including +1% Session and +0.4% Weekly Active Users. We believe our work offers a universal framework that enhances user engagement with generative AI, particularly when standard generative AI reaches its limits in terms of enhancing content to be more captivating. To the best of our knowledge, this represents an early milestone in the industry's successful use of generative AI to enhance user engagement

Character Segmentation System Based on C# Design and Implementation

AbstractAt present, most of the OCR recognizing through individual character, thus the quality of character segmentation is the key point to affect the quality of OCR recognition system. This paper introduces the formula of projective method in analysis of preliminary segmentation for images. Moreover it applied analysis for connected spatial domain, the correct results shows that writing image well matched. After two analyses and segmentation, characters can be segmented correctly. In order to provide useful solutions to these two problems that characters keying must be performed rapidly and documents digitizing can be conserved for a long time. Therefore, we must place emphasis on the research and development of the character segmentation

Generating 3D faces using multi-column graph convolutional networks

In this work, we introduce multi-column graph convolutional networks (MGCNs), a deep generative model for 3D mesh surfaces that effectively learns a non-linear facial representation. We perform spectral decomposition of meshes and apply convolutions directly in the frequency domain. Our network architecture involves multiple columns of graph convolutional networks (GCNs), namely large GCN (L-GCN), medium GCN (M-GCN) and small GCN (S-GCN), with different filter sizes to extract features at different scales. L-GCN is more useful to extract large-scale features, whereas S-GCN is effective for extracting subtle and fine-grained features, and M-GCN captures information in between. Therefore, to obtain a high-quality representation, we propose a selective fusion method that adaptively integrates these three kinds of information. Spatially non-local relationships are also exploited through a self-attention mechanism to further improve the representation ability in the latent vector space. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction. Moreover, with the help of variational inference, our model has excellent generating ability

Efficient production of human acidic fibroblast growth factor in pea (Pisum sativum L.) plants by agroinfection of germinated seeds

<p>Abstract</p> <p>Background</p> <p>For efficient and large scale production of recombinant proteins in plants transient expression by agroinfection has a number of advantages over stable transformation. Simple manipulation, rapid analysis and high expression efficiency are possible. In pea, Pisum sativum, a Virus Induced Gene Silencing System using the pea early browning virus has been converted into an efficient agroinfection system by converting the two RNA genomes of the virus into binary expression vectors for Agrobacterium transformation.</p> <p>Results</p> <p>By vacuum infiltration (0.08 Mpa, 1 min) of germinating pea seeds with 2-3 cm roots with <it>Agrobacteria </it>carrying the binary vectors, expression of the gene for Green Fluorescent Protein as marker and the gene for the human acidic fibroblast growth factor (aFGF) was obtained in 80% of the infiltrated developing seedlings. Maximal production of the recombinant proteins was achieved 12-15 days after infiltration.</p> <p>Conclusions</p> <p>Compared to the leaf injection method vacuum infiltration of germinated seeds is highly efficient allowing large scale production of plants transiently expressing recombinant proteins. The production cycle of plants for harvesting the recombinant protein was shortened from 30 days for leaf injection to 15 days by applying vacuum infiltration. The synthesized aFGF was purified by heparin-affinity chromatography and its mitogenic activity on NIH 3T3 cells confirmed to be similar to a commercial product.</p

Comparative genomics reveals 104 candidate structured RNAs from bacteria, archaea, and their metagenomes

Novel motifs identified in a comparative genomic analysis of bacterial, archaeal and metagenomic data reveals over 100 candidate structured RNAs