Globalization and Knowledge Spillover: International Direct Investment, Exports and Patents

This paper examines the impact of the three main channels of international trade on domestic innovation, namely outward direct investment, inward direct investment (IDI) and exports. The number of Triadic patents serves as a proxy for innovation. The data set contains 37 countries that are considered to be highly competitive in the world market, covering the period 1994 to 2005. The empirical results show that increased exports and outward direct investment are able to stimulate an increase in patent output. In contrast, IDI exhibits a negative relationship with domestic patents. The paper shows that the impact of IDI on domestic innovation is characterized by two forces, and the positive effect of cross-border mergers and acquisitions by foreigners is less than the negative effect of the remaining IDI.International direct investment; Export; Triadic Patent; Outward Direct Investment; Inward Direct Investment; R&D; negative binomial model

Parylene stiction

This paper presents a preliminary study into stiction between parylene C and substrate surfaces for biocompatible check-valve applications. During fabrication, parylene C is used as the structural material for the check-valve. The substrate surfaces studied include Au, Al, Si, parylene C, XeF_2 treated Si, and silicon dioxide. Stiction between different surfaces is created after sacrificial photoresist etching. Then, the stiction is measured using blister tests, and stiction mechanisms for different materials are investigated. The devices are released with different recipes to examine their effects. Finally, the results of the study reveal methods to control the cracking pressure of parylene check-valves

Improvement of visual acuity in children with anisometropic amblyopia treated with rotated prisms combined with near activity

<b>AIM:</b> To evaluate the efficacy of a new modality for improving visual acuity (VA) in pediatric patients with anisometropic amblyopia.<b>METHODS:</b> Retrospective and interventional case series. Medical records of 360 children with anisometropic amblyopia treated with a modality that included rotated prisms, lenses, and near activities from January 2008 to January 2012 were analyzed. Characteristics such as improvement of VA and contrast sensitivity in amblyopic eyes and resolution of amblyopia (VA≤0.1logMAR or a difference of ≤2 lines in logMAR between the eyes) were assessed.<b>RESULTS:</b> Among the patients, the mean VA of the amblyopic eyes improved from 0.48logMAR (SD=0.16) to 0.12logMAR (SD=0.16) and the mean VA improvement was 0.36logMAR (SD=0.10, <i>P</i><0.001). Resolution of amblyopia was achieved in 233 of 360 patients (64.72%). The mean time for resolution of amblyopia was 8.05 weeks (SD=4.83) or 14.14 sessions (SD=8.76). Among the study group, refraction error did not change significantly after treatment (<i>P</i>=0.437). We found that better baseline VA may be related to success and shorten the time to amblyopic resolution.<b>CONCLUSION:</b> VA and contrast sensitivity improved with rotated prisms, correcting lenses, and near activities in children with anisometropic amblyopia. The VA improvement by this modality was comparable to other methods. However, the time to resolution of amblyopia was shorter with this method than with other modalities. Rotated prisms combined with near acuity could provide an alternative treatment in children with anisometropic amblyopia who can’t tolerant traditional therapy method like patching

THE INFLUENCE OF TAI-CHI EXERCISE ON DYNAMICS OF LOWER EXTREMITY FOR THE ELDERLY DURING SIT-TO-STAND

The purpose of this study was to investigate the influence of Tai Chi exercise on sit-tostand in the elderly. Ten healthy female elders (normal group) and nine healthy Tai-Chi female practitioner (Tai-Chi group) participated in this study. The results indicated: (1) During the forward flexion phase, normal group showed significantly greater hip flexion angle and moment than Tai-Chi group (

Measuring Taiwanese Mandarin Language Understanding

The evaluation of large language models (LLMs) has drawn substantial attention in the field recently. This work focuses on evaluating LLMs in a Chinese context, specifically, for Traditional Chinese which has been largely underrepresented in existing benchmarks. We present TMLU, a holistic evaluation suit tailored for assessing the advanced knowledge and reasoning capability in LLMs, under the context of Taiwanese Mandarin. TMLU consists of an array of 37 subjects across social science, STEM, humanities, Taiwan-specific content, and others, ranging from middle school to professional levels. In addition, we curate chain-of-thought-like few-shot explanations for each subject to facilitate the evaluation of complex reasoning skills. To establish a comprehensive baseline, we conduct extensive experiments and analysis on 24 advanced LLMs. The results suggest that Chinese open-weight models demonstrate inferior performance comparing to multilingual proprietary ones, and open-weight models tailored for Taiwanese Mandarin lag behind the Simplified-Chinese counterparts. The findings indicate great headrooms for improvement, and emphasize the goal of TMLU to foster the development of localized Taiwanese-Mandarin LLMs. We release the benchmark and evaluation scripts for the community to promote future research.Comment: Preprint. Under revie

Inhibition of Serine Protease Activity Protects Against High Fat Diet-Induced Inflammation and Insulin Resistance.

Recent evidence suggests that enhanced protease-mediated inflammation may promote insulin resistance and result in diabetes. This study tested the hypothesis that serine protease plays a pivotal role in type 2 diabetes, and inhibition of serine protease activity prevents hyperglycemia in diabetic animals by modulating insulin signaling pathway. We conducted a single-center, cross-sectional study with 30 healthy controls and 57 patients with type 2 diabetes to compare plasma protease activities and inflammation marker between groups. Correlations of plasma total and serine protease activities with variables were calculated. In an in-vivo study, LDLR-/- mice were divided into normal chow diet, high-fat diet (HFD), and HFD with selective serine protease inhibition groups to examine the differences of obesity, blood glucose level, insulin resistance and serine protease activity among groups. Compared with controls, diabetic patients had significantly increased plasma total protease, serine protease activities, and also elevated inflammatory cytokines. Plasma serine protease activity was positively correlated with body mass index, hemoglobin A1c, homeostasis model assessment-insulin resistance index (HOMA-IR), tumor necrosis factor-α, and negatively with adiponectin concentration. In the animal study, administration of HFD progressively increased body weight, fasting glucose level, HOMA-IR, and upregulated serine protease activity. Furthermore, in-vivo serine protease inhibition significantly suppressed systemic inflammation, reduced fasting glucose level, and improved insulin resistance, and these effects probably mediated by modulating insulin receptor and cytokine expression in visceral adipose tissue. Our findings support the serine protease may play an important role in type 2 diabetes and suggest a rationale for a therapeutic strategy targeting serine protease for clinical prevention of type 2 diabetes

An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators

In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach