{"id":12713,"date":"2025-08-27T08:58:45","date_gmt":"2025-08-27T00:58:45","guid":{"rendered":"https:\/\/sprout.scu.edu.tw\/?p=12713"},"modified":"2025-08-27T08:59:35","modified_gmt":"2025-08-27T00:59:35","slug":"%e4%ba%ba%e7%a4%be%e9%99%a2%e6%9a%a8%e7%90%86%e5%ad%b8%e9%99%a2%e6%95%99%e5%b8%ab%e7%99%bc%e8%a1%a8%e6%9c%80%e6%96%b0%e6%9c%9f%e5%88%8a%e8%ab%96%e6%96%87","status":"publish","type":"post","link":"https:\/\/sprout.scu.edu.tw\/?p=12713","title":{"rendered":"\u4eba\u793e\u9662\u66a8\u7406\u5b78\u9662\u6559\u5e2b\u767c\u8868\u6700\u65b0\u671f\u520a\u8ad6\u6587"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"12713\" class=\"elementor elementor-12713\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d45fd20 e-flex e-con-boxed e-con e-parent\" data-id=\"d45fd20\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-48806ade 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2008 \u5e74\u81f3 2020 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\u6797\u60e0\u6587\u6559\u6388<\/strong><\/p><p><strong>\u8ad6\u6587\u540d\u7a31\uff1aStatistical Analysis of Tonal Acquisition in Disyllabic Words among Polish Learners of Mandarin: A Comparative Study.<\/strong><\/p><p>\u4f5c\u00a0 \u00a0 \u8005\uff1aChu, MN (Chu, Man-Ni)\uff0fChang, C (Chang, Carson)\uff0fLin, HW (Lin, Hui-Wen)<\/p><p>\u671f\u520a\u540d\u7a31\uff1aAPPLIED SCIENCES-BASEL\uff08SCI\uff09<\/p><p>\u5377\u671f\u6578\uff1a14(14)<\/p><p>\u9801\u78bc(\u6587\u737b\u865f\u78bc) \uff1a6095<\/p><p>\u51fa\u7248\u6642\u9593\uff1aJUL 2024<\/p><p>\u6458\u8981\uff1a<\/p><p>In this study, we utilized a mixture random effects model and pairwise comparisons to conduct our analysis. Furthermore, we employed visualization techniques, specifically line plots in Python, to illustrate the tonal variations in disyllabic words among Polish learners of Mandarin. Our findings indicate that Polish learners experience more difficulty with the tonal contours of Tone 1 (T1) and Tone 2 (T2) compared to Taiwanese Mandarin (TM) natives, particularly due to the lower pitch range required. We provide potential pedagogical recommendations based on these results. We suggest that integrating training on T1 and T2 accompanied with Tone 3 (T3) and Tone 4 (T4), because of their lower endpoint tones, may offer a more effective learning strategy for these learners.<\/p><p>\u00a0<\/p><p><strong>\u7406\u5b78\u9662\u6578\u5b78\u7cfb \u6797\u60e0\u6587\u6559\u6388<\/strong><\/p><p><strong>\u8ad6\u6587\u540d\u7a31\uff1aFast Screening of Tuberculosis Patients Based on Analysis of Plasma by Infrared Spectroscopy Coupled with Machine Learning Approaches.<\/strong><\/p><p>\u4f5c\u00a0 \u00a0 \u8005\uff1aLin, M (Lin, Mei)\uff0fLu, HC (Lu, Hsiao-Chi)\uff0fLin, HW (Lin, Hui-Wen)\uff0fPan, SW (Pan, Sheng-Wei)\uff0fCheng, BM (Cheng, Bing-Ming)\uff0f Tseng, TR (Tseng, Ton-Rong)\uff0f Feng, JY (Feng, Jia-Yih)\uff0f Ho, ML (Ho, Mei-Lin)<\/p><p>\u671f\u520a\u540d\u7a31\uff1aACS OMEGA\uff08SCI\uff09<\/p><p>\u5377\u671f\u6578\uff1a10(12)<\/p><p>\u9801\u78bc(\u6587\u737b\u865f\u78bc) \uff1a11817-11827<\/p><p>\u51fa\u7248\u6642\u9593\uff1aMAR 20 2025<\/p><p>\u6458\u8981\uff1a<\/p><p>Prompt diagnosis of tuberculosis (TB) enables timely treatment, limiting spread and improving public health for this disease. Currently, a rapid, sensitive, accurate, and cost-effective detection of TB still remains a challenge. For this purpose, we engaged a transmission skill and an attenuated total reflectance (ATR) technique coupled with Fourier-transform infrared spectrometry (FTIR) to study the IR spectra of the plasma samples from TB patients (n = 10) and healthy individuals (n = 10). To ensure high-quality spectral data, spectra were collected in both transmission and ATR modes, with each measurement consisting of 256 scans at a resolution of 8 cm\u20131. For the transmission mode, measurements were repeated five times per sample, while ATR-FTIR measurements were repeated three times per sample. These parameters were carefully optimized through rigorous testing to achieve the highest possible signal-to-noise ratio for patient sample analysis. Using this method, we obtained a total of 100 spectra from 20 samples in the transmission mode and 60 spectra in the ATR-FTIR mode, ensuring sufficient data for robust spectral analysis. Further, we applied machine learning techniques to analyze and classify the IR spectra; by this means, we differentiated those spectra between TB patients and healthy ones. In this work, we modified the transmission-FTIR setup to improve the absorption sensitivity by focusing the IR light on the interface of the sample; while, we used a high-refractive-index crystal ZnSe as a medium to reflect the signals in ATR scheme. Routinely, we compared the spectra obtained from both methods; in their second derivative curves, we notified that there had distinct spectral differences in protein and lipid regions (3500\u20133000, 2900\u20132800, and 1700\u20131500 cm\u20131) between TB and healthy groups. Using three machine learning classifiers\u2500Logistic Regression (LR), Random Forest (RF), and XGBoost (Xg)\u2500we found that the Xg achieved an accuracy of 0.749, precision of 0.703, recall of 0.901, F1 score of 0.790, and an AUC of the ROC curve of 0.82 for absorption spectra in the 3500\u20132700 cm\u20131 region; additionally, the machine learning practice showed that ATR data possessed performance parameters of \u223c 80% in accuracy. We randomly assigned participants (rather than individual scans) to 80% training and 20% test sets to train and validate three machine learning models (LR, RF, and Xg). Based on the results, we concluded that the absorption spectroscopic method demonstrated its superior performance in TB diagnosis. Thus, we have showed that absorption-FTIR spectroscopy is a valuable tool for sorting the TB disease from patients. The spectral IR analysis of plasmas can complement clinical evidence and provides a rapid and accurate diagnosis of TB in clinic.<\/p><p>\u00a0<\/p><p><strong>\u7406\u5b78\u9662\u6578\u5b78\u7cfb \u6797\u60e0\u6587\u6559\u6388<\/strong><\/p><p><strong>\u8ad6\u6587\u540d\u7a31\uff1aAssociation of physical functional activity impairment with severity of sarcopenic obesity: findings from National Health and Nutrition Examination Survey.<\/strong><\/p><p>\u4f5c\u00a0 \u00a0 \u8005\uff1aHuang, SW (Huang, Shih-Wei)\uff0fLee, YH (Lee, Yu-Hao)\uff0fLiao, CD (Liao, Chun-De)\uff0fEscorpizo, R (Escorpizo, Reuben)\uff0fLiou, TH (Liou, Tsan-Hon)\uff0fLin, HW (Lin, Hui-Wen)<\/p><p>\u671f\u520a\u540d\u7a31\uff1aSCIENTIFIC REPORTS\uff08SCI\uff09<\/p><p>\u5377\u671f\u6578\uff1a14(1)<\/p><p>\u9801\u78bc(\u6587\u737b\u865f\u78bc) \uff1a3787<\/p><p>\u51fa\u7248\u6642\u9593\uff1aFEB 15 2024<\/p><p>\u6458\u8981\uff1a<\/p><p>We aim to clarify the relationship between low skeletal muscle mass and varying levels of adiposity and to identify the types of physical function impairments associated with sarcopenic obesity (SO). This study examined cross-sectional data from the National Health and Nutrition Examination Survey with whole-body dual-energy X-ray absorptiometry (DXA) scans. The data included age, gender, DXA-assessed body composition, and physical functional activity with performing daily tasks by questionnaire. We subdivided the data by body composition into a non-SO group and a SO group (ASMI 0-49.99% and FMI of 50-100%), after which the SO data were subdivided into three classes. A higher class indicated higher adiposity and lower muscle mass. The physical function impairment of the two groups was compared. Our study examined 7161 individuals, of which 4907 did not have SO and 2254 had SO, and their data were further divided into three classes (i.e., class I, 826 individuals; class II, 1300 individuals; and class III, 128 individuals). Significant differences in demographics and DXA parameters were identified between the non-SO and SO groups (P &lt; 0.001); the individuals with SO were older, included more women, and exhibited high adiposity and less lean muscle mass. The individuals with class III SO exhibited greater differences and reported more difficulty in performing daily activities. The individuals with class III SO exhibited the most severe physical function impairment. Our study highlights the considerable difficulties encountered by individuals with SO in performing daily activities. Given this finding, customized rehabilitation strategies should be implemented to improve the quality of life of individuals with 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