STUDENTS’ PERCEPTIONS OF ACTIVE LEARNING IN INTRODUCTION TO LITERATURE

There is a growing interest in active learning as a shift from traditional lecturing to improving student-centred learning in English. However, in the Vietnamese context of teaching and learning at tertiary levels, little research has examined students’ perceptions of active learning in approaching Introduction to Literature. This study is therefore aimed to look into this area of interest. Participants in this study were 94 students from junior and seniors majoring in high-quality programs at a university in the Mekong Delta. Data were collected from questionnaires. The findings show that students had positive perceptions of active learning in studying this course. Implications for teaching and learning this course are made.  Article visualizations

The equal-value search: accelerating search in function induction

Bibliography: p. 98-100

FUNCTION DISCOVERY USING DATA TRANSFORMATION

This thesis describes the design and implementation of a system that infers real-valued functions of one argument from example data points. The system, LINUS, can identify a wide range of functions: rational functions, quadratic relations, and many transcendental functions: rational functions, quadratic relations, and many transcendental functions, as well as expressions that can be transformed to rational functions by combinations of differentiation, logarithm and function inverse operations. As a result of its representation of functions and the flexibility of the underlying model, LINUS's ability exceeds that of previous discovery systems. The idea of transforming from one function to another forms the basis of both the search operation and the structural representation of functions, an idea pioneered by an earlier system called "FFD". Augmenting this with on-demand data selection, automatic error analysis, data splitting and solution merging, aggregated transformations, and local approximations, results in a practical discovery method that is shown to be theoretically sound. LINUS is tested on several tasks to evaluate both the expressiveness of its representation and the practicality of its discovery method in the domain of real-valued functions. From the design of LINUS, formal properties are identified that are critical to the data transformation method. First, all transformations must be numerically reversible. Second, for any transformation sequence it must be possible to select examples that satisfy a certain accuracy requirement for that sequence. Third, it must be possible to enumerate all sequences, though the transformations themselves may contain parameters that are not enumerable. Based on these properties, a discovery model is developed that can be applied within more general domains.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]

Function discovery using data transformation

Bibliography: p. 182-187

THE EQUAL-VALUE SEARCH: ACCELERATING SEARCH IN FUNCTION INDUCTION

Function induction is a major component of many learning systems. Its purpose is to extract information in the form of a functional relationship from a given set of examples. Since learning systems strive for generality, search emerges as the best candidate for tackling the task of function induction. However, it is inevitably less efficient than specialized problem-solving methods. Previous researchers have sacrificed generality by developing strategies that utilize domain-specific knowledge to improve efficiency. This thesis presents a different approach, the "equal-value" method, which directly improves search performance while maintaining its generality. The result is a new search strategy that is both general and efficient. Experiments suggest that, in the case of numeric functions, performance can increase by several orders of magnitude compared to generic exhaustive search. While the strategy was developed specifically to address the function induction problem, it is possible that a similar approach applies to other induction problems. In any case, the equal-value search provides a powerful new technique for general function induction.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]

Design optimization of compliant mechanisms for vibration assisted machining applications using a hybrid Six Sigma, RSM-FEM, and NSGA-II approach

Vibration-assisted machining, a hybrid processing method, has been gaining considerable interest recently due to its advantages, such as increasing material removal rate, enhancing surface quality, reducing cutting forces and tool wear, improving tool life, or minimizing burr formation. Special equipment must be designed to integrate the additional vibration energy into the traditional system to exploit those spectacular characteristics. This paper proposes the design of a new 2-DOF high-precision compliant positioning mechanism using an optimization process combining the response surface method, finite element method, and Six Sigma analysis into a multi-objective genetic algorithm. The TOPSIS method is also used to select the best solution from the Pareto solution set. The optimum design was fabricated to assess its performance in a vibration-assisted milling experiment concerning surface roughness criteria. The results demonstrate significant enhancement in both the manufacturing criteria of surface quality and the design approach criteria since it eliminates modelling errors associated with analytical approaches during the synthesis and analysis of compliant mechanisms

Granulocyte colony-stimulating factor reduces biliary fibrosis and ductular reaction in a mouse model of chronic cholestasis

Background: Biliary atresia is a rare congenital bile duct disease that is the leading cause of liver fibrosis in neonates. Granulocyte colony-stimulating factor (GCSF) is a potential therapy for hepatocellular diseases, but data on GCSF for cholestatic conditions remain limited. Materials and methods: The current study examines the role of GCSF in improving bile duct obstruction in mice. Two doses were administered: 10.0 μg/kg/day and 61.5 μg/kg/day, which is the animal equivalent dose of 5.0 μg/kg in humans. Seven days (D7) after bile duct ligation (BDL), Swiss mice were treated with phosphate buffered saline or GCSF for 5 days. The intrahepatic adaptive response of BDL mice was evaluated on postsurgical days D12, D19, and D26. Results: Treatment with 61.5 μg/kg of GCSF resulted in a significant increase in circulating leukocytes and neutrophils on D12. Amelioration of liver injury, as shown by reduced aspartate aminotransferase levels, increased albumin levels and survival rate, as well as reduced intrahepatic inflammation and hepatic myeloperoxidase expression, downregulated ductular proliferation, periportal fibroblast activation, and fibrosis, enhanced expressions of hepatocyte growth factor, peroxisome proliferator-activated receptor-alpha, and ki67, and suppressed expression of cleaved caspase-3 protein, was noted after treatment with 61.5 μg/kg of GCSF. Additionally, GCSF treatment was associated with an increased number of intrahepatic cd3-Sca1+c-Kit+ bone marrow cells. Conclusions: Treatment with 61.5 μg/kg of GCSF resulted in liver regeneration and survival in BDL mice was seen, suggesting its potential use for human liver diseases

Inverse Stellation of CuAu-ZnO Multimetallic-Semiconductor Nanostartube for Plasmon-Enhanced Photocatalysis

One-dimensional (1D) metallic nanocrystals constitute an important class of plasmonic materials for localization of light into subwavelength dimensions. Coupled with their intrinsic conductive properties and extended optical paths for light absorption, metallic nanowires are prevalent in light-harnessing applications. However, the transverse surface plasmon resonance (SPR) mode of traditional multiply twinned nanowires often suffers from weaker electric field enhancement due to its low degree of morphological curvature in comparison to other complex anisotropic nanocrystals. Herein, simultaneous anisotropic stellation and excavation of multiply twinned nanowires are demonstrated through a site-selective galvanic reaction for a pronounced manipulation of light–matter interaction. The introduction of longitudinal extrusions and cavitation along the nanowires leads to a significant enhancement in plasmon field with reduced quenching of localized surface plasmon resonance (LSPR). The as-synthesized multimetallic nanostartubes serve as a panchromatic plasmonic framework for incorporation of photocatalytic materials for plasmon-assisted solar fuel production

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening