CONCEPTUAL MAPPING MODEL ACROSS LANGUAGES: A TEST IN VIETNAMESE LANGUAGE

The conceptual metaphor, LOVE IS A JOURNEY, has been identified as a process of mapping based on the Conceptual Metaphor Theory (CMT) proposed by Lakoff and Johnson (1980). However, Ahrens (2002) pointed out several problems that the CMT may encounter, especially in setting parameters to be experimentally tested. Ahrens (2002) proposed the Conceptual Mapping Model (CMM) to investigate metaphor expressions by identifying three mappings between the source domain and the target domain: entities, qualities, and functions. After an analysis, the reason for these mappings, called a mapping principle, is indicated. In particular, the CMM can predict the processing of conceptual metaphors in terms of conventional and novel metaphors. This study is intended to test whether the CMM can perform well across languages through the experimental rates of acceptability and interpretability for different types of metaphors. Fifty Vietnamese native speakers were recruited. Each participant judged (on a Likert scale of 1-7) the levels of acceptability and interpretability of three conceptual metaphors in Vietnamese: LIFE IS A BOOK, HAPPINESS IS LIGHT, and LOVE IS FIRE. Each conceptual metaphor consists of six types of sentences, including (a) Literal pair to B, (b) Conventional metaphor, (c) Literal pair to D, (d) Novel metaphor that follows the mapping principle, (e) Literal pair to F, and (f) Novel metaphor that does not follow the mapping principle. The results of t-tests show that in terms of both acceptability and interpretability, conventional metaphors are ranked higher than novel metaphors. The results also indicate that novel metaphors that follow the mapping principle are rated higher than those that do not. Therefore, the mapping principle can constrain the image schemas so that any image that does not belong to the schemas can affect the processing of metaphors

Asian American Voting During the 2020 Elections: A Rising, Divided Voting Group

Asian Americans continue to be an untapped force within American politics. Despite their status as the fastest-growing racial or ethnic group in the United States they have had surprisingly low political participation rates.[1] But 2020 represented a watershed moment. Campaign outreach and voter participation increased, and Asian Americans assumed new prominence on the national stage. Nonetheless, the 2020 elections also demonstrate historical divides within the community and a lack of cohesion as a voting group. This thesis investigates Asian American voter behavior during the 2020 election and links trends within this year\u27s elections to assess Asian American panethnicity. It focuses on anti-Asian sentiment and violence, mobilized Asian American voters, and encouraged the growth of panethnicity. This thesis analyzes Georgia and California\u27s local and national election results to confirm the theory that political threats created an increase in panethnicity. Drawing on election results and Asian American voter behavior in these two states, the findings of this work demonstrate moments in which Asian Americans as a voting bloc shift; however, there is no evidence for long term changes and the development of a nationwide Asian panethnic identity. Ultimately, the voting behavior of Asian ethnic groups in the United States will continue to change. Campaigns must take account of their linguistic and cultural diversity to mobilize Asian Americans successfully

Cultivating Identities in a Place Called Home: Intersectional, Ever Changing Identities of Vietnamese American Youth in Culturally Sustaining Spaces

Educators and scholars have been advocating for culturally sustaining pedagogies in the classroom that extends, honors, and sustains the cultures and backgrounds of our growing Students of Color population. Moving beyond pedagogies in classrooms, I examine culturally sustaining spaces in culture clubs and community-based organizations and how they cultivate the identity development and sense of belonging of Vietnamese American high school students. I find that these students have complex identities that are intersectional and ever changing, existing outside the Black-White binary. Vietnamese culture clubs provide a space that allows students to belong and express their identity in a positive way, but with curriculum as colonizer (Goodwin, 2010), schools have not yet become a place of belonging for all students. Community-based organizations provide alternative spaces that center the experiences of Vietnamese American students, allowing them to engage with their complex identities in a place that becomes like a home

Optimal Ship Maintenance Scheduling Under Restricted Conditions and Constrained Resources

The research presented in this dissertation addresses the application of evolution algorithms, i.e. Genetic Algorithm (GA) and Differential Evolution algorithm (DE) to scheduling problems in the presence of restricted conditions and resource limitations. This research is motivated by the scheduling of engineering design tasks in shop scheduling problems and ship maintenance scheduling problems to minimize total completion time. The thesis consists of two major parts; the first corresponds to the first appended paper and deals with the computational complexity of mixed shop scheduling problems. A modified Genetic algorithm is proposed to solve the problem. Computational experiments, conducted to evaluate its performance against known optimal solutions for different sized problems, show its superiority in computation time and the high applicability in practical mixed shop scheduling problems. The second part considers the major theme in the second appended paper and is related to the ship maintenance scheduling problem and the extended research on the multi-mode resource-constrained ship scheduling problem. A heuristic Differential Evolution is developed and applied to solve these problems. A mathematical optimization model is also formulated for the multi-mode resource-constrained ship scheduling problem. Through the computed results, DE proves its effectiveness and efficiency in addressing both single and multi-objective ship maintenance scheduling problem

Automated essay assessment: an evaluation on paperrater’s reliability from practice / Nguyen Vi Thong

From a perspective of a PaperRater user, the author attempts to investigate the reliability of the program. Twenty-four freshman students and one writing teacher at Dalat University - Vietnam were recruited to serve the study. The author also served as one scorer. The scores generated by PaperRater and the two human scorers were analyzed quantitatively and qualitatively. The statistical results indicate that there is an excellent correlation between the means of scores generated by three scorers. With the aid of SPSS and certain calculation, it is shown that PaterRater has an acceptable reliability which implies that the program can somehow assist in grading students’ papers. The semi-structured interview at the qualitative stage with the teacher scorer helped point out several challenges that writing teachers might encounter when assessing students’ prompts. From her perspective, it was admitted that with the assistance of PaperRater, the burden of assessing a bunch of prompts at a short time period would be much released. However, how the program can be employed by teachers should be carefully investigated. Therefore, this study provides writing teachers with pedagogical implications on how PaperRater should be used in writing classrooms. The study is expected to shed new light on the possibility of adopting an automated evaluation instrument as a scoring assistant in large writing classrooms

Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

Nursing Students\u27 Awareness About Effective Teamwork and Related Factors

This study surveyed the awareness level of nursing students about the effectiveness of teamwork and related factors. At Hong Bang International University (HIU), teamwork training for nursing students at HIU has been applied but it has been difficult for all students to achieve high results and confidence in practicing teamwork skills. The effectiveness of self-study and group activities depends on many factors. The effectiveness of teaching approaches has not been measured and has needed to be addressed to result in the best learning experience for students. The results of this study provided recommendations to improve the quality of training as well as to inform the successful design of an educational program regarding teamwork. A descriptive, cross-sectional design was used. A questionnaire was administered to 129 nursing students who were in their third and final year in the Bachelor of Nursing Program from Hong Bang International University. Participants were 101females and 28 males. The questionnaire Teamwork skill of Saigon University (Huyen, 2010) included 33 items, which were rated on a 5-point Likert scale. Reliability of this instrument for this study was .95. The use of learner-centered methods in the college has shifted the focus of activity from the teacher to the students to develop learner autonomy and independence. This has been considered a challenge for nursing students because they are not only learning at school but also in clinical settings. Therefore, nursing students\u27 awareness about the role of effective teamwork is very important so they can work together to develop their learning quality. Nurse educators could use this study’s results to improve teaching strategies regarding teamwork knowledge and skills for nursing students in Vietnam

Involvement of miR-106b in tumorigenic actions of both prolactin and estradiol.

Prolactin promotes a variety of cancers by an array of different mechanisms. Here, we have investigated prolactin's inhibitory effect on expression of the cell cycle-regulating protein, p21. Using a miRNA array, we identified a number of miRNAs upregulated by prolactin treatment, but one in particular that was strongly induced by prolactin and predicted to bind to the 3'UTR of p21 mRNA, miR-106b. By creating a p21 mRNA 3'UTR-luciferase mRNA construct, we demonstrated degradation of the construct in response to prolactin in human breast, prostate and ovarian cancer cell lines. Increased expression of miR-106b replicated, and anti-miR-106b counteracted, the effects of prolactin on degradation of the 3'UTR construct, p21 mRNA levels, and cell proliferation in breast (T47D) and prostate (PC3) cancer cells. Increased expression of miR-106b also stimulated migration of the very epithelioid T47D cell line. By contrast, anti-miR-106b dramatically decreased expression of the mesenchymal markers, SNAIL-2, TWIST-2, VIMENTIN, and FIBRONECTIN. Using signaling pathway inhibitors and the 3'UTR construct, induction of miR-106b by prolactin was determined to be mediated through the MAPK/ERK and PI3K/Akt pathways and not through Jak2/Stat5 in both T47D and PC3 cells. Prolactin activation of MAPK/ERK and PI3K/Akt also activates ERα in the absence of an ERα ligand. 17β-estradiol promoted degradation of the construct in both cell lines and pre-incubation in the estrogen antagonist, Fulvestrant, blocked the ability of both prolactin and 17β-estradiol to induce the construct-degrading activity. Together, these data support a convergence of the prolactin and 17β-estradiol miR-106b-elevating signaling pathways at ERα

Conceptual Metaphor of Different Conventionality Levels from the Perspectives of Translatability Assessment and Translation Strategies

This study examines the Conceptual Metaphor Theory from an innovative perspective: translatability and translation strategy. The experiment recruited 239 undergraduate students of different translation training to evaluate the translatability of twelve sentences of different metaphor types before translating them into Vietnamese. Additionally, this study examines how students deal with metaphorical mapping images as well as grammatical and lexical refining attempts. The factorial ANOVA results (p=.02) indicate that the effect of metaphor types on translatability levels is conditional on translation training levels, despite the fact that the main effect is on metaphor types, not translation skill. Besides, twelve in-depth strategies to deal with the source sentences are identified, establishing a new model for metaphorical translation strategy. Chi-square analysis reveals associations between translation training levels and strategies (p<.01); and between metaphor types and strategies (p<.01). This study argues for the possibility that translation could be considered among conceptual metaphor's cognitive mechanisms

Backward Dijkstra Algorithms for Finding the Departure Time Based on the Specified Arrival Time for Real-Life Time-Dependent Networks

A practical transportation problem for finding the “departure” time at “all source nodes” in order to arrive at “some destination nodes” at specified time for both FIFO (i.e., First In First Out) and Non-FIFO “Dynamic ” Networks is considered in this study. Although shortest path (SP) for dynamic networks have been studied/documented by various researchers, contributions from this present work consists of a sparse matrix storage scheme for efficiently storing large scale sparse network’s connectivity, a concept of Time Delay Factor (TDF) combining with a “general piece- wise linear function” to describe the link cost as a function of time for Non-FIFO links’ costs, and Backward Dijkstra SP Algorithm with simple heuristic rules for rejecting unwanted solutions during the backward search algorithm. Both small-scale (academic) networks as well as large- scale (real-life) networks are investigated in this work to explain and validate the proposed dynamic algorithms. Numerical results obtained from this research work have indicated that the newly proposed dynamic algorithm is reliable, and efficient. Based on the numerical results, the calculated departure time at the source node(s), for a given/specified arrival time at the destination node(s), can be non-unique, for some Non-FIFO networks’ connectivity