Teaching methods in higher education have evolved significantly with technology, facilitating student connectivity and information access. This study examines innovative teaching through blended teaching and teacher enthusiasm, essential for fostering Communities of Inquiry (CoI) to enhance student learning outcomes. A quantitative survey-based study using stratified random sampling was conducted with 357 first-year undergraduates at a Taiwanese university, employing a time-lagged design to capture teacher-student dynamics in knowledge formation and problem-solving within a project-based learning context. Confirmatory factor analysis and structural equation modelling were applied to analyse relationships between innovative teaching, learning performance, cognitive presence, student-teacher relationships, team efficacy, and learning satisfaction. Findings indicate that innovative teaching strengthens cognitive presence, fosters student-teacher relationships, and enhances team efficacy, positively impacting student learning outcomes. Teacher enthusiasm was particularly influential in increasing cognitive presence, reinforcing the role of instructor engagement in blended learning. The study underscores the importance of cultivating CoI's social and cognitive components through collaborative learning, timely feedback, and engaging course content. Addressing inconsistencies in prior research, this study highlights the critical role of teacher enthusiasm and provides practical recommendations for educators and institutions to implement structured interaction opportunities and adaptive teaching strategies to optimise blended learning environments.

UNLABELLED: Family-based designs offer unique advantage for identifying rare risk variants in genetic association studies. There are existing tools for analyzing rare variants in families but lacking components to handle binary traits properly and survival traits. In this report, we introduce an R software package RVFam (Rare Variant association analysis with Family data) designed to analyze continuous, binary and survival traits against rare and common sequencing variants in genome-wide association studies (GWAS) involving family data. Single and multiple variant association tests were implemented while accounting for arbitrary family structures. Extensive simulation studies were performed to evaluate all the approaches implemented in RVFam. AVAILABILITY AND IMPLEMENTATION: http://cran.r-project.org/web/packages/RVFam/.

With the trend of cloud computing, cloud services are aggressively launched. Along with the evolution of smart terminal and broadband technology, the demand about personal services built on cloud is aware. In 2011 April, Chunghwa Telecom (CHT) launch the Hami+Personal Cloud Service which is the leading product among the domestic industry about personal cloud service. Since the Hami+Personal Cloud service was published, a number of personal information management services and innovative services base on cloud was researched by CHT and published on Hami+Personal Cloud platform. To enhance the competitive ability of Hami+Personal Cloud Service, we continue following up the perception of the users about the Personal Cloud Service. We believe that to optimize the user experience will be directly and effectively to enhance customer satisfaction and product competitiveness. In this article, we will describe the planning and design processes that how the research team optimizes the user experiences about Hami+Personal Cloud service.

Abstract Summary: GWAF, Genome-Wide Association analyses with Family, is an R package designed for GWAF. It implements association tests between a batch of genotyped or imputed single nucleotide polymorphisms (SNPs) and a binary or continuous trait with user specified genetic model, and generates informative results from the analyses. In addition, GWAF provides functions to visualize results. We evaluated GWAF using a simulated continuous trait and a binary trait dichotomized from the simulated continuous trait with real genotype data from the Framingham Heart Study's SNP Health Association Resource project. Availability: http://cran.r-project.org/web/packages/GWAF/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

The camera imaging system has been widely used; however, the displaying image appears to have an unequal light distribution. This paper presents novel light-balancing techniques to compensate uneven illumination based on adaptive signal processing. For text image processing, first, we estimate the background level and then process each pixel with nonuniform gain. This algorithm can balance the light distribution while keeping a high contrast in the image. For graph image processing, the adaptive section control using piecewise nonlinear gain is proposed to equalize the histogram. Simulations show that the performance of light balance is better than the other methods. Moreover, we employ line-based processing to efficiently reduce the memory requirement and the computational cost to make it applicable in real-time systems.

This paper presents a color interpolation technique for a single-chip charge-coupled device with color-filter-array format. We propose edge-direction weighting and the local gain approach to reconstruct missing color components. Simulations show that the proposed method can achieve better quality-complexity tradeoff than other algorithms. For real-time implementation, a cost-effective architecture consisting of a pipeline schedule is designed based on our new algorithm. With the time-sharing method, the VLSI architecture can interpolate various colors using a common computational kernel, reducing the circuit complexity. The prototype of the color interpolation processor has been successfully verified with a field-programmable gate array device. The chip only uses about 10K gates and two line buffers.