A Novel Three-Point Modulation Technique for Fractional-N Frequency Synthesizer Applications

This paper presents a novel three-point modulation technique for fractional-N frequency synthesizer applications. Convention modulated fractional-N frequency synthesizers suffer from quantization noise, which degrades not only the phase noise performance but also the modulation quality. To solve this problem, this work proposes a three-point modulation technique, which not only cancels the quantization noise, but also markedly boosts the channel switching speed. Measurements reveal that the implemented 2.4 GHz fractional-N frequency synthesizer using three-point modulation can achieve a 2.5 Mbps GFSK data rate with an FSK error rate of only 1.4 %. The phase noise is approximately -98 dBc/Hz at a frequency offset of 100 kHz. The channel switching time is only 1.1 μs with a frequency step of 80 MHz. Comparing with conventional two-point modulation, the proposed three-point modulation greatly improves the FSK error rate, phase noise and channel switching time by about 10 %, 30 dB and 126 μs, respectively

Relationships between Secondary School Students’ Perceptions of School Adjustment and Well-being

The research is financed by the Ministry of Science and Technology, Taiwan. No. MOST 108-2410-               H-018-016-MY2 Abstract Both school adjustment and well-being are fundamental to a good quality of life for youth. Good school adjustment is important and has far-reaching influences on the psychology and behavior of middle school students, which sets the stage for future educational and occupational opportunities. Good mental health or well-being helps young people develop the skills they need to cope with whatever life throws at them. The purposes of this study were to explore the relationships between secondary school students’ perceptions of school adjustment and well-being. 890 secondary students were selected from central Taiwan. A questionnaire was applied to collect data. Data were analyzed by using descriptive statistics, one-way ANOVA, Pearson’s product-moment correlation, and multiple regression analysis. The findings of this study were as follows: First, students’ perceptions of school adjustment and well-being were moderate level. Second, respondents with different family socio-economic status showed significant differences in school adjustment including dimensions of overall, academic adjustment, proper behavior, and self-affirmation, and also showed significant differences in well-being including dimensions of overall, life satisfaction, physical-mental health, and self-evaluation. Third, there was a medium positive correlation between respondents' school adjustment and well-being. The school adjustment could predict well-being and the peer relationship and the self-assurance were the better predictors of well-being. Keywords: Secondary school students, School adjustment, Well-being DOI: 10.7176/JEP/11-30-19 Publication date:October 31st 202

6-Gingerol Inhibits Growth of Colon Cancer Cell LoVo via Induction of G2/M Arrest

6-Gingerol, a natural component of ginger, has been widely reported to possess antiinflammatory and antitumorigenic activities. Despite its potential efficacy against cancer, the anti-tumor mechanisms of 6-gingerol are complicated and remain sketchy. In the present study, we aimed to investigate the anti-tumor effects of 6-gingerol on colon cancer cells. Our results revealed that 6-gingerol treatment significantly reduced the cell viability of human colon cancer cell, LoVo, in a dose-dependent manner. Further flow cytometric analysis showed that 6-gingerol induced significant G2/M phase arrest and had slight influence on sub-G1 phase in LoVo cells. Therefore, levels of cyclins, cyclin-dependent kinases (CDKs), and their regulatory proteins involved in S-G2/M transition were investigated. Our findings revealed that levels of cyclin A, cyclin B1, and CDK1 were diminished; in contrast, levels of the negative cell cycle regulators p27Kip1 and p21Cip1 were increased in response to 6-gingerol treatment. In addition, 6-gingerol treatment elevated intracellular reactive oxygen species (ROS) and phosphorylation level of p53. These findings indicate that exposure of 6-gingerol may induce intracellular ROS and upregulate p53, p27Kip1, and p21Cip1 levels leading to consequent decrease of CDK1, cyclin A, and cyclin B1 as result of cell cycle arrest in LoVo cells. It would be suggested that 6-gingerol should be beneficial to treatment of colon cancer

MENTOR: Multilingual tExt detectioN TOward leaRning by analogy

Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task. For instance, delivery robots in multilingual cities need to be capable of doing multilingual text detection so that the robots can read traffic signs and road markings. Moreover, the target languages change from region to region, implying the need of efficiently re-training the models to recognize the novel/new languages. However, collecting and labeling training data for novel languages are cumbersome, and the efforts to re-train an existing/trained text detector are considerable. Even worse, such a routine would repeat whenever a novel language appears. This motivates us to propose a new problem setting for tackling the aforementioned challenges in a more efficient way: "We ask for a generalizable multilingual text detection framework to detect and identify both seen and unseen language regions inside scene images without the requirement of collecting supervised training data for unseen languages as well as model re-training". To this end, we propose "MENTOR", the first work to realize a learning strategy between zero-shot learning and few-shot learning for multilingual scene text detection.Comment: 8 pages, 4 figures, published to IROS 202

The Potential Economic Impact of Avian Flu Pandemic on Taiwan

This study analyzes the potential consequences of an outbreak of avian influenza (H5N1) on Taiwan¡¦s macro economy and individual industries. Both the Input-Output (IO) Analysis Model and Computable General Equilibrium (CGE) Model are used to simulate the possible damage brought by lowering domestic consumption, export, and labor supply. The simulation results indicates that if the disease is confined within the poultry sector, then the impact on real GDP is around -0.1%~-0.4%. Once it becomes a human-to-human pandemic, the IO analysis suggests that the potential impacts on real GDP would be as much as -4.2%~-5.9% while labor demand would decrease 4.9%~6.4%. In the CGE analysis, which allows for resource mobility and substitutions through price adjustments, the real GDP and labor demand would contract 2.0%~2.4% and 2.2%~2.4%, respectively, and bringing down consumer prices by 3%. As for the individual sector, the outbreak will not only damage the poultry sector and its upstream and downstream industries, but also affect the service sectors including wholesale, retail, trade, air transportation, restaurants, as well as healthcare services. These results can be used to support public investment in animal disease control measures.Avian Flu Pandemic, Input-output Model, Computable General Equilibrium Model, Livestock Production/Industries,

Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures

This study aims to establish a computer-aided diagnosis system for endobronchial ultrasound (EBUS) surgery to assist physicians in the preliminary diagnosis of metastatic cancer. This involves arranging immediate examinations for other sites of metastatic cancer after EBUS surgery, eliminating the need to wait for reports, thereby shortening the waiting time by more than half and enabling patients to detect other cancers earlier, allowing for early planning and implementation of treatment plans. Unlike previous studies on cell image classification, which have abundant datasets for training, this study must also be able to make effective classifications despite the limited amount of case data for lung metastatic cancer. In the realm of small data set classification methods, Few-shot learning (FSL) has become mainstream in recent years. Through its ability to train on small datasets and its strong generalization capabilities, FSL shows potential in this task of lung metastatic cell image classification. This study will adopt the approach of Few-shot learning, referencing existing proposed models, and designing a model architecture for classifying lung metastases cell images. Batch Spectral Regularization (BSR) will be incorporated as a loss update parameter, and the Finetune method of PMF will be modified. In terms of test results, the addition of BSR and the modified Finetune method further increases the accuracy by 8.89% to 65.60%, outperforming other FSL methods. This study confirms that FSL is superior to supervised and transfer learning in classifying metastatic cancer and demonstrates that using BSR as a loss function and modifying Finetune can enhance the model's capabilities