INTRA-INDUSTRY TRADE BETWEEN TAIWAN AND ASEAN-5 IN THE AGRO-FOOD SECTOR: PATTERNS AND DETERMINANTS

This paper attempts to identify the patterns and determinants of the levels of IIT between Taiwan and ASEAN-5 during the past three decades. Our empirical results confirm the belief that IIT between Taiwan and ASEAN-5 in the agro-food sector has been growing over time. Although industry-specific factors like market size and product differentiation have desirable impacts on IIT, taste overlaps do not. No deterministic conclusion can be drawn from the effect of trade liberalization as implied by removing trade barriers when shaping the future development of IIT. However, the indirect effect arising from income and consumer preferences' convergence may be the main determinant in promoting intra-industry agro-food trade among the Asian countries.International Relations/Trade,

Efficient Coding Tree Unit (CTU) Decision Method for Scalable High-Efficiency Video Coding (SHVC) Encoder

High-efficiency video coding (HEVC or H.265) is the latest video compression standard developed by the joint collaborative team on video coding (JCT-VC), finalized in 2013. HEVC can achieve an average bit rate decrease of 50% in comparison with H.264/AVC while still maintaining video quality. To upgrade the HEVC used in heterogeneous access networks, the JVT-VC has been approved scalable extension of HEVC (SHVC) in July 2014. The SHVC can achieve the highest coding efficiency but requires a very high computational complexity such that its real-time application is limited. To reduce the encoding complexity of SHVC, in this chapter, we employ the temporal-spatial and inter-layer correlations between base layer (BL) and enhancement layer (EL) to predict the best quadtree of coding tree unit (CTU) for quality SHVC. Due to exist a high correlation between layers, we utilize the coded information from the CTU quadtree in BL, including inter-layer intra/residual prediction and inter-layer motion parameter prediction, to predict the CTU quadtree in EL. Therefore, we develop an efficient CTU decision method by combing temporal-spatial searching order algorithm (TSSOA) in BL and a fast inter-layer searching algorithm (FILSA) in EL to speed up the encoding process of SHVC. The simulation results show that the proposed efficient CTU decision method can achieve an average time improving ratio (TIR) about 52–78% and 47–69% for low delay (LD) and random access (RA) configurations, respectively. It is clear that the proposed method can efficiently reduce the computational complexity of SHVC encoder with negligible loss of coding efficiency with various types of video sequences

A Systematic Literature Review of Empirical Studies on Learning Analytics in Educational Games

Learning analytics (LA) in educational games is considered an emerging practice due to its potential of enhancing the learning process. Growing research on formative assessment has shed light on the ways in which students' meaningful and in-situ learning experiences can be supported through educational games. To understand learners' playful experiences during gameplay, researchers have applied LA, which focuses on understanding students' in-game behaviour trajectories and personal learning needs during play. However, there is a lack of studies exploring how further research on LA in educational games can be conducted. Only a few analyses have discussed how LA has been designed, integrated, and implemented in educational games. Accordingly, this systematic literature review examined how LA in educational games has evolved. The study findings suggest that: (1) there is an increasing need to consider factors such as student modelling, iterative game design and personalisation when designing and implementing LA through educational games; and (2) the use of LA creates several challenges from technical, data management and ethical perspectives. In addition to outlining these findings, this article offers important notes for practitioners, and discusses the implications of the study’s results

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

Prognosis of Nasopharyngeal Carcinoma in the Elderly is Worse than in Younger Individuals–Experience of a Medical Institute

SummaryPurposeWe aimed to evaluate outcomes of the elderly (>65 years) by comparing with younger (<40 years) patients after treatments for nasopharyngeal carcinoma (NPC).Materials and methodsWe retrospectively obtained clinical data from charts for 23 older and 21 younger patients in whom NPC was diagnosed and who underwent curative managements during 2007 and 2011. Occurrence of local recurrence, distant metastasis, and death from any cause were recorded as endpoints. Cox proportional hazards regression was applied to determine age effects on survival risks after adjusting for the potential confounders.ResultsOlder patients more commonly received a diagnosis of chronic diseases than the younger patients (56.5% versus 23.8%, p = 0.036), whereas they were less likely to have received intensive treatments for NPC. After adjusting for medical history and neoadjuvant chemotherapy, older age was the only significant predictor in the study cohort for overall survival and progression-free survival. The adjusted hazard ratio (HR) for death from all causes in older patients was 6.3 (95% confidence interval [CI] = 1.3–30.2), and the adjusted HR for disease progression in older patients was 10.9 (95% CI = 2.3–50.6).ConclusionAging was the only independent prognostic risk factor in this study cohort. Medical history and treatment variations could not fully explain the difference in prognosis. Our results strengthen the need to ameliorate toxicities and improve supportive care for older patients with a diagnosis of NPC

Body Massage Performance Investigation by Brain Activity Analysis

Massage has been widely applied to improve health and reduce stress. However, the performance difference between hands-on treatment and treatment by mechanical devices has been little mentioned. Therefore, the main aim of this paper is to investigate a subject's EEG performance under massage treatment applied by hand and treatment applied by mechanical devices. Massage was applied to four acupoints for three minutes each. The massage acupoint sequence was from left Jian-wai-yu, right Jian-wai-yu, left Zuo-fei-yu, and finally right Zuo-fei-yu. An EEG system of 32 channels was used. Twenty-four volunteers, mainly college students, were enrolled. EEG rhythm powers of each massage sessions were derived. Two-way ANOVA revealed that there were also significant interactions between the massage stage and the massage type on delta (P < 0.01), theta (P < 0.05), and beta rhythms (P < 0.01), and there were significant differences at different stages for the mechanical massage group (F = 5.557, P < 0.01). The mechanical massage group had more significant differences than the hands-on group for stage coherence of around coherence on alpha rhythm. Further rhythm power scalp topography between two massage methods is also investigated

A Smart Collaborative Educational Game with Learning Analytics to Support English Vocabulary Teaching

Learning Analytics (LA) approaches have proved to be able to enhance learning process and learning performance. However, little is known about applying these approaches for second language acquisition using educational games. Therefore, this study applied LA approaches to design a smart collaborative educational game, to enhance primary school children learning English vocabularies. Specifically, the game provided dashboards to the teachers about their students in a real-time manner. A pilot experiment was conducted in a public primary school where the students’ data from experimental and control groups, namely learning and motivation test scores, interview and observation, were collected and analyzed. The obtained results showed that the experimental group (who used the smart game with LA) had significantly higher motivation and performance for learning English vocabularies than the control group (who used the smart game without LA). The findings of this study can help researchers and practitioners incorporate LA in their educational games to help students enhance language acquisition