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以音素平衡方式修訂目前國內臨床嗓音評估及嗓音研究使用的國語語音平衡成人閱讀短文之研究
No full text[[abstract]]本研究目的為收集國內成年人日常生活使用的國語韻母和聲母比例百分比值，並 根據此韻母和聲母比例百分比值，以音素平衡（Phonetically balanced）方式，修 訂目前國內臨床嗓音評估及嗓音研究使用的「國語短文」(附錄一)。目前國內尚 未發表達到音素平衡且適用於成人之語音平衡短文，因此修訂目前國內臨床和研 究使用的國語短文達到音素平衡，可以更自然、更真實反映個案於日常說話之嗓 音表現，有助於語言治療師對個案的嗓音音質及嚴重度進行更精確之判斷。由於 國內缺乏成年人日常生活使用的國語韻母和聲母比例百分比參考值，因此研究方 法是收集成年受試者的生活對話語料並對語料進行分析，以發現出成年受試者日 常生活情境所使用的國語韻母和聲母比例百分比值供研究使用。另外亦透過文獻 回顧，參考國內外編制短文的方法和原則
醫學暨健康學院聽力暨語言治療學系
No full text[[abstract]]非流利型失語症（non-fluent aphasia）多是因為中風（stroke）導致大腦的 皮質層與言語產出相關的區域受損造成語言-言語不流暢的情形。在語言 和言語的治療方面，傳統上是藉由讓患者反覆練習或補償技術，輕度的 失語症患者可以透過傳統治療方法得到一定的恢復效果；但對於中、重 度的失語症患者改善幅度則有限。研究證據顯示，相較於僅接受傳統語 言-言語治療，搭配重複性經顱磁刺激（repetitive transcranial magnetic stimulation, rTMS）治療，更能提升言語產出能力。目前對於 rTMS 療效 的探討多著重於其對運動言語功能的改善，而忽略了支持詞彙產出的語 言面向。本研究將初步探討 rTMS 對言語產出的療效是否來自於個案語 意和語意處理能力獲得強化。另外，本研究也將探討，除了流暢度的改 善，rTMS 能夠誘發哪些語言功能改變。[[note]]畢可
醫學暨健康學院聽力暨語言治療學系
No full text[[abstract]]本研究透過文獻回顧，參考國外編制嗓音測試短文的方法和原則，由於目前國 內嗓音臨床評估及研究使用的「閩南語短文」(附件一)，未達語音及音素平衡， 研究目的為收集母語為閩南語之國內成人的日常生活語料，分析國內成人日常生 活使用閩南語聲母、韻母及聲調分佈百分比，根據分析結果，以音素平衡 (Phonetically balanced)方式修訂「閩南語短文」，使該測試材料能更真實反映個案 日常使用嗓音情形，有助於治療師對於個案嗓音音質及嚴重度進行更精確的判定， 並判斷嗓音對於生活品質及溝通的影響，制定有效的個別計畫。[[note]]邱奕
比較新型與傳統型神經肌肉逆轉劑 於老年膝關節置換術患者麻醉安全之回溯調查
No full text[[abstract]]背景：全球老年人口逐年增加，高齡手術麻醉及甦醒成為重要議題。新型逆轉劑Sugammadex能加速逆轉神經肌肉阻斷並促使一般患者盡早脫離呼吸器，對於老年患者麻醉安全指標缺乏相關研究。
目的：比較使用新型與傳統型神經肌肉逆轉劑對於老年膝關節置換術患者麻醉恢復成效調查。
方法：病歷回溯法自2018年1月1日至2018年8月31日期間，採立意取樣65歲以上行膝關節置換術且接受神經肌肉逆轉劑老年患者，自擬結構式問卷「神經肌肉逆轉劑麻醉恢復病歷回溯表」進行登錄，共收案214例。
結果：不論新型或傳統組皆以女性居多(p<0.05)，其他基本性質未達顯著差異。麻醉照護指標方面，新型組術中接受較多次神經肌肉阻斷劑(Rocuronium)(p<0.05)，但在拔管前吐氣二氧化碳監測(ETCO2)兩組雖達顯著差異但皆於正常範圍內(p<0.05)。新型組不論在麻醉與手術時間 (p<0.05)、給藥至拔管時間(p=0.02)，皆有低於傳統組的趨勢，且新型組在拔管後平均神經肌肉阻斷程度(TOF)為98%，達拔管安全指標。恢復期照護指標方面，新型組意識恢復優於傳統組(p<0.05)。在恢復期合併症方面兩組無論在延遲拔管、低血氧、高血壓、頭暈、嘔吐及喉嚨痛，皆未達顯著差異；新型組在術後整體滿意度得分高於傳統組，呈顯著差異(p<0.05)。
結論：接受新型阻斷劑老年病患能縮短手術麻醉時間、意識恢復及自主呼吸，達到麻醉安全指標。恢復期合併症及術後品質分析與傳統組相近，本研究結果可提供醫護人員在麻醉安全及提升照護品質。
關鍵字：麻醉恢復、神經肌肉逆轉劑、Sugammadex、麻醉安全、老人[[abstract]]Background: The pace of population ageing is much faster than in the past and aging increases the probability of a person to undergo surgery. Elderly patients take more time to recover from general anesthesia therefore generation of muscle reversal agent is important in facilitating anesthesia safety issue.
Objective: To compare the effects of new and traditional neuromuscular reversal agents on anesthesia recovery in elderly patients undergoing knee arthroplasty.
Methods: From January 1, 2018 to August 31, 2018, the medical records were retrospectively sampled for elderly patients over 65 years old who underwent knee arthroplasty and received neuromuscular reversal. Self-made structural questionnaire "neuromuscular reversal" The agent's anesthesia recovery medical record retrospective table was registered, and a total of 214 cases were received.
Results: Population of the new or traditional reversal agents were predominantly female (p < 0.05), and there were no significant differences in the baseline characteristics. In terms of anesthesia care indicators, the new reversal agents received more than one neuromuscular blocker (Rocuronium) (p<0.05), but the two groups of exhaled carbon dioxide monitoring (ETCO2) before extubation showed significant differences but were within the normal range ( p<0.05). The new reversal agents had a lower trend than the traditional group in the time of anesthesia and surgery (p<0.05), and the time of administration to extubation (p=0.02), and the average neuromuscular blockade after extubation in the new reversal agents (TOF) is 98%. In terms of recovery period care indicators, the recovery of new reversal agents consciousness was better than that of the traditional group (p<0.05). There were no significant differences between the two groups in terms of delayed complication, delayed extubation, hypoxemia, hypertension, dizziness, vomiting, and sore throat.. The overall satisfaction score of the new group was higher than that of the traditional group (p<0.05).
Conclusion: Elderly patients receiving new reversal agents can recover consciousness and spontaneous breathing in a short time, and achieve anesthesia safety indicators
A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal
No full text[[abstract]]Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy
Application of polyhexamethylene guanidine hydrochloride to polylactic acid/polyphenylene block copolymer antibacterial composite membranes: Manufacturing technique and property evaluations
No full text[[abstract]]Polylactic acid (PLA) possesses many advantages, especially biodegradability, and has subsequently been used in many regions. Unfortunately, its disadvantages, including its brittleness, become an obstacle to its application and constrain its development. In this study, Polystyrene-b-poly(ethylene-co-1-butene)-b-polystyrene (SEBS) is used as the toughener and maleic anhydride grafted polypropylene (PP-g-MA) is used as the compatibilizer. The melt-blending technique for polymers is employed to modify PLA in order to produce PLA/SEBS membranes. For PLA/SEBS membranes, the optimal matrices are made of a blending ratio of PLA/SEBS/PP-g-MA being 57/40/3. Next, polyhexamethylene guanidine hydrochloride (PHGH) is combined with the optimal membranes to form PLA/SEBS antibacterial membranes, the crystallinity, thermal properties, and antibacterial properties of which are examined to determine the effect of the PHGH content. The test results show that an appropriate content of SEBS or PHGH has a positive influence on PLA/SEBS antibacterial membranes. Moreover, the antibacterial membranes exhibit greater antibacterial efficacy against Escherichia coli (E. coli) than is gained with an increase in the PHGH content
Electroacupuncture improves TBI dysfunction by targeting HDAC overexpression and BDNF-associated Akt/GSK-3b signaling
No full text[[abstract]]Background: Acupuncture or electroacupuncture (EA) appears to be a potential treatment in acute clinical traumatic brain injury (TBI); however, it remains uncertain whether acupuncture affects post-TBI histone deacetylase (HDAC) expression or impacts other biochemical/neurobiological events.
Materials and methods: We used behavioral testing, Western blot, and immunohistochemistry analysis to evaluate the cellular and molecular effects of EA at LI4 and LI11 in both weight drop-impact acceleration (WD)- and controlled cortical impact (CCI)-induced TBI models.
Results: Both WD- and CCI-induced TBI caused behavioral dysfunction, increased cortical levels of HDAC1 and HDAC3 isoforms, activated microglia and astrocytes, and decreased cortical levels of BDNF as well as its downstream mediators phosphorylated-Akt and phosphorylated-GSK-3β. Application of EA reversed motor, sensorimotor, and learning/memory deficits. EA also restored overexpression of HDAC1 and HDAC3, and recovered downregulation of BDNF-associated signaling in the cortex of TBI mice.
Conclusion: The results strongly suggest that acupuncture has multiple benefits against TBI-associated adverse behavioral and biochemical effects and that the underlying mechanisms are likely mediated by targeting HDAC overexpression and aberrant BDNF-associated Akt/GSK-3 signaling
Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review
No full text[[abstract]]The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed
Automated Diagnosis of Coronary Artery Disease using Scalogram-based Tensor Decomposition with Heart Rate Signals
No full text[[abstract]]Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention and save lives. This study aims to develop a machine learning framework that uses tensor analysis on heart rate (HR) signals to automate the CAD detection task. A third-order tensor representing a time-frequency relationship is constructed by fusing scalograms as vertical slices of the tensor. Each scalogram is computed from the considered time frame of a given HR signal. The derived scalogram represents the heterogeneity of data as a two-dimensional map. These two-dimensional maps are stacked one after the other horizontally along the z-axis to form a 3-way tensor for each HR signal. Each two-dimensional map is represented as a vertical slice in the xy - plane. Tensor factorization of such a fused tensor for every HR signal is performed using canonical polyadic (CP) decomposition. Only the core factor is retained later, excluding the three unitary matrices to provide the latent feature set for the detection task. The resultant latent features are then fed to machine learning classifiers for binary classification. Bayesian optimization is performed in a five-fold cross-validation strategy in search of the optimal machine learning classifier. The experimental results yielded the accuracy, sensitivity, and specificity of 96.62%, 96.53%, and 96.67%, respectively, with the bagged trees ensemble method. The proposed tensor decomposition deciphered higher-order interrelations among the considered time-frequency representations of HR signals