Knowledge Discovery Model in Chinese Industrial News

With prevalence of Internet, users can easily retrieve the information what they want from Internet. Information explosion shows that efficient information summarization is aspired to all users. Therefore, an efficient knowledge management methodology becomes very important. Some technologies, such as text mining, for acquiring knowledge from huge amount of electronic documents are recognized as important technology in this field. This work focuses on text-mining applications on Chinese industrial news and knowledge discovery. We use information extract method to extract news into companies, event keyword, time, location, and person categories based on the characteristics of news. The set of five extracted categories is called information template. The templates are summarized by rule induction. We can discover unexpected knowledge from these summarized rules. We built an integrated industrial news text-mining model by using induction rule learner. This model is suitable to manipulate rules in bag-of-word form. Furthermore, we proposed interestingness to measure interesting strength of rules. The users can analyze the discovered rules based this measure. These are helpful to discover unexpected knowledge. It is meaningful to commercial activities if we can discover valuable rules. Besides industrial news application, we believe this model is suitable for knowledge discovery application in other fields

Internationalization in Higher Education – International Student’s Chinese Learning as Serious Leisure in Taiwan

Due to the economic and commercial rise of China, the economic value, political value, and instrumental advantages produced by Chinese have become more significant. The fever for learning Chinese continues to intensify all over the world, and Chinese instruction has become increasingly popular. Taiwan is a country with Chinese as its official language; and an important issue rose in international educational policy is how to attract international students to study Chinese in Taiwan. At the same time, Taiwan has promoted the internationalization of universities in the recent years; it hopes to elevate the visibility of its universities on the international stage, and intends to broaden diplomacy and promote economic development. This study found that international students who came to Taiwan to learn Chinese tended to be Asians. The different original residence locations of international students would significantly affect learning Chinese as serious leisure. International students who rented apartments with others off-campus or who chose home-stays had greater life planning, effort in Chinese learning, continuous benefits from Chinese learning, and a sense of community identification with their learning peers, as compared to international students who lived in school dormitories or lived alone off-campus. International students who had mor e disposable income each month were more likely to gain lasting benefits from Chinese learning and strong community identification with their learning peers. Key words: International education; International students; Chinese learning; Language learning as serious leisure Resumé: En raison de l'essor économique et commercial de la Chine, la valeur économique, la valeur politique et les avantages instrumentaux produits par les Chinois sont devenus plus importants. L'enthouiasme pour apprendre le chinois continue de s'intensifier dans le monde entier, et l'enseignement du chinois est devenu de plus en plus populaire. La langue officielle de Taiwan est le chinois, et l'une des questions importantes dans la politique éducative internationale est de savoir comment attirer des étudiants étrangers à étudier le chinois à Taiwan. En même temps, Taiwan a favorisé l'internationalisation des universités dans les années récentes. Il espère rehausser la visibilité de ses universités sur la scène internationale et il a l'intention d'élargir la diplomatie et promouvoir le développement économique. Cette étude a révélé que les étudiants étrangers qui sont venus à Taiwan pour apprendre le chinois sont plutôt des Asiatiques. Les localisations différentes de résidence originale des étudiants étrangers auraient une influence significative sur leur apprentissage du chinois en tant que loisir sérieux. Les étudiants étrangers qui ont loué des appartements avec d'autres étudiants en dehors du campus ou ceux qui ont choisi une famille d'acceuil avaient une planification de vie plus longue et un effort d'apprendre le Chinois plus grand. Ils bénéficiaient des avantages continus de l'apprentissage du chinois et avaient un sentiment d'identification de communauté avec leurs partenaires de l'apprentissage, par rapport aux étudiants étrangers qui vivaient dans les dortoirs scolaires ou vivaient seuls en dehors du campus. Les étudiants étrangers qui avaient un revenu disponible plus élevé chaque mois étaient plus susceptibles d'obtenir des avantages durables de l'apprentissage du chinois et avaient une identification forte de communauté avec leurs partenaire de l'apprentissage. Mots-clés: éducation internationale; étudiants internationaux; apprentissage du chinois; apprentissage de langue en tant que loisir sérieu

Pentacene-Based Thin-Film Transistors With a Solution-Process Hafnium Oxide Insulator

Abstract—Pentacene-based organic thin-film transistors with solution-process hafnium oxide (HfOx) as gate insulating layer have been demonstrated. The solution-process HfOx could not only exhibit a high-permittivity (κ = 11) dielectric constant but also has good dielectric strength. Moreover, the root-mean-square surface roughness and surface energy (γs) on the surface of the HfOx layer were 1.304 nm and 34.24 mJ/cm2, respectively. The smooth, as well as hydrophobic, surface of HfOx could facilitate the direct deposition of the pentacene film without an additional polymer treatment layer, leading to a high field-effect mobility of 3.8 cm2/(V · s). Index Terms—Hafnium oxide, high permittivity, organic thinfilm transistor (OTFT), solution process, surface energy

ESTIMATION THE PREFERENCE OF ECOTOURISM FOR GAOMEI WETLAND IN TAIWAN

Gaomei Wetland is not only the biggest grassy coastal wetland, but also a wild‐animal protecting area, located on the west‐central coast of Taiwan.Wetlands are considered as one of the most important natural resource, which offer a lot of benefits for human and other creatures. However, it is believed that over-intensive recreational activities in Gaomei Wetland should be responsible for serious damages on natural environment and ecosystem. This study takes Gaomei wetland as an example, and aims to estimate its landscape and ecological services values through Choice experiment. The results of this research showed that Gaomei landscape’s economic value is $2.06 million (USD) per year, and$1.54 million (USD) for its value of ecological services. These findings can help to bring up the awareness of natural resource preservation, and hopefully to keep Gaomei Wetland substantial. The results also indicated that visitors with undergraduate degree or above were willing to pay $6.43 (USD) per year for entry fee to enjoy sunset scenery in Gaomei wetland

Geometric and Electronic Structure of Graphene Bilayer Edges

We present a computational investigation of free-standing graphene bilayer edge (BLE) structures, aka “fractional nanotubes.” We demonstrate that these curved carbon nanostructures possess a number of interesting properties, electronic in origin. The BLEs, quite atypical of elemental carbon, have large permanent electric dipoles of 0.87 and 1.14 debye/Å for zigzag and armchair inclinations, respectively. An unusual, weak AA interlayer coupling leads to a twinned double-cone dispersion of the electronic states near the Dirac points. This entails a type of quantum Hall behavior markedly different from what has been observed in graphenebased materials, characterized by a magnetic field-dependent resonance in the Hall conductivity

Caught in the Crossfire: Fears of Chinese-American Scientists

The US leadership in science and technology has greatly benefitted from immigrants from other countries, most notably from China in the recent decades. However, feeling the pressure of potential federal investigation since the 2018 launch of the China Initiative under the Trump administration, Chinese-origin scientists in the US now face higher incentives to leave the US and lower incentives to apply for federal grants. Analyzing data pertaining to institutional affiliations of more than 2.3 million scientific papers, we find a steady increase in the return migration of Chinese-origin scientists from the US back to China. We also conducted a survey of Chinese-origin scientists employed by US universities in tenure or tenure-track positions (n=1300), with results revealing general feelings of fear and anxiety that lead them to consider leaving the US and/or stop applying for federal grants.Comment: 16 pages, 2 figure

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic