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[[alternative]]A Case Study on the Key Factors of Staff Turnover of Quasi-public Infant Care Centers in Taichung City
No full text[[abstract]]本研究旨在探討臺中市準公共化托嬰中心托育人員離職關鍵因素。本研究以服務臺中市準公共化托嬰中心托育人員為研究對象，採深度訪談法蒐集資料，對五家托嬰中心園長及資深員工進行訪談，並將訪談蒐集得來之逐字稿進行分析。研究結果發現，臺中市準公共化托嬰中心托育人員，離職原因涵蓋多元層面，涉及組織與個人層面因素。托嬰中心人員離職關鍵主因分別為薪資低福利差、工作時間長、工作壓力大、主管、同事及親師間溝通不良及缺乏升遷管道。文末亦提出相關建議，提供給政府主管機關、機構管理者及業者、托嬰中心人員及後續研究者參考。[[abstract]]The present study aimed to explore the key factors of staff turnover of quasi-public infant care centers in Taichung City. This study took the staff of quasi-public infant care centers in Taichung City as the research object, adopted in-depth interview methods to collect data, interviewed a total of five directors and senior staff of infant care centers, and conducted an analysis on the transcripts collected from the interviews. The results of the study found that the causes of staff turnover in quasi-public infant care centers in Taichung City covered multiple levels involving organizational and personal factors. The key factors of staff turnover of infant care centers mainly are: Low salary and benefits, long working hours, high work pressure, poor communication among supervisors, colleagues, parents and teachers, and lack of promotion channels. At the end of the essay, relevant suggestions are also proposed for government authorities, institution managers and owners, staff of infant care centers, and future researchers
Effects of Acupressure on Anxiety: A Systematic Review and Meta-Analysis
No full text[[abstract]]Objectives: The research aim was to perform a systematic review and meta-analysis evaluating the ability of acupressure to reduce anxiety.
Design: Randomized controlled trials were obtained through a search of electronic medical databases (four in English and one in Chinese) from inception to October 5, 2020. Two authors searched the databases, evaluated studies' methodological quality, and performed data extraction independently. The final studies for analysis were identified after discussion with the third author.
Results: We obtained 27 studies for our systematic review and meta-analysis. Eight studies had a low overall risk of bias, and 13 had some bias concerns with methodological quality. According to the results, acupressure significantly reduced patient anxiety (standardized mean difference?=?1.152; 95% confidence interval: 0.847–1.459, p?<?0.001), and the study heterogeneity was high (Q?=?299.74, p?<?0.001, I2?=?91.333%). Two studies reported acupressure-associated adverse events. We also performed a sensitivity analysis by omitting one outlier study, which had the largest effect size; however, high heterogeneity remained (I2?=?87.816%). A subgroup analysis revealed significant differences between participant types (Q?=?46.573, p?<?0.001), levels of methodological quality (Q?=?6.228, p?=?0.044), and massage equipment (Q?=?4.642, p?=?0.031).
Conclusions: Our meta-analysis suggests that acupressure can alleviate anxiety. Acupressure was more effective for inpatients and preoperative patients when finger massage was applied. In individuals with anxiety and a stable hemodynamic status, acupressure could be a promising treatment option. However, the substantial heterogeneity across studies means that any inference from the results should be performed cautiously
Norketamine, the main metabolite of ketamine, induces mitochondria-dependent and ER stress-triggered apoptotic death in urothelial cells via a Ca2+-regulated ERK1/2-activating pathway
No full text[[abstract]]Ketamine-associated cystitis is characterized by suburothelial inflammation and urothelial cell death. Norketamine (NK), the main metabolite of ketamine, is abundant in urine following ketamine exposure. NK has been speculated to exert toxic effects in urothelial cells, similarly to ketamine. However, the molecular mechanisms contributing to NK-induced urothelial cytotoxicity are almost unclear. Here, we aimed to investigate the toxic effects of NK and the potential mechanisms underlying NK-induced urothelial cell injury. In this study, NK exposure significantly reduced cell viability and induced apoptosis in human urinary bladder epithelial-derived RT4 cells that NK (0.01-0.5 mM) exhibited greater cytotoxicity than ketamine (0.1-3 mM). Signals of mitochondrial dysfunction, including mitochondrial membrane potential (MMP) loss and cytosolic cytochrome c release, were found to be involved in NK-induced cell apoptosis and death. NK exposure of cells also triggered the expression of endoplasmic reticulum (ER) stress-related proteins including GRP78, CHOP, XBP-1, ATF-4 and -6, caspase-12, PERK, eIF-2α, and IRE-1. Pretreatment with 4-phenylbutyric acid (an ER stress inhibitor) markedly prevented the expression of ER stress-related proteins and apoptotic events in NK-exposed cells. Additionally, NK exposure significantly activated JNK, ERK1/2, and p38 signaling and increased intracellular calcium concentrations ([Ca2+]i). Pretreatment of cells with both PD98059 (an ERK1/2 inhibitor) and BAPTA/AM (a cell-permeable Ca2+ chelator), but not SP600125 (a JNK inhibitor) and SB203580 (a p38 inhibitor), effectively suppressed NK-induced mitochondrial dysfunction, ER stress-related signals, and apoptotic events. The elevation of [Ca2+]i in NK-exposed cells could be obviously inhibited by BAPTA/AM, but not PD98059. Taken together, these findings suggest that NK exposure exerts urothelial cytotoxicity via a [Ca2+]i-regulated ERK1/2 activation, which is involved in downstream mediation of the mitochondria-dependent and ER stress-triggered apoptotic pathway, consequently resulting in urothelial cell death. Our findings suggest that regulating [Ca2+]i/ERK signaling pathways may be a promising strategy for treatment of NK-induced urothelial cystitis
Logistic ridge regression to predict bipolar disorder using mRNA expression levels in the N-methyl-D-aspartate receptor genes
No full text[[abstract]]Background: It is hypothesized that demographic variables and mRNA expression levels in the N-methyl-D-aspartate receptor (NMDAR) genes can be employed as potential biomarkers to predict bipolar disorder using artificial intelligence and machine learning approaches.
Methods: To determine bipolar status, we established a logistic ridge regression model resulting from the analysis of age, gender, and mRNA expression levels in 7 NMDAR genes in the blood of 51 bipolar patients and 139 unrelated healthy individuals in the Taiwanese population. The NMDAR genes encompasses COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR. We also compared our approach with various state-of-the-art algorithms such as support vector machine and C4.5 decision tree.
Results: The analysis revealed that the mRNA expression levels of COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR were associated with bipolar disorder. Moreover, the logistic ridge regression model (area under the receiver operating characteristic curve = 0.922) performed maximally among predictive models to infer the complicated relationship between bipolar disorder and biomarkers. Additionally, the results for the age- and gender-matched cohort were similar to those of the unmatched cohort.
Limitations: The cross-sectional study design limited the predictive value.
Conclusion: This is the first study demonstrating that the mRNA expression levels in the NMDAR genes may be altered in patients with bipolar disorder, thereby supporting the NMDAR hypothesis of bipolar disorder. The study also indicates that the mRNA expression levels in the NMDAR genes could serve as potential biomarkers to distinguish bipolar patients from healthy controls using artificial intelligence and machine learning approaches
The efficacy and tolerability of antibiotics in scrub typhus: an updated network meta-analysis of randomized controlled trials
No full text[[abstract]]Objectives: Inadequate treatment of scrub typhus results in severe complications such as septic shock and is also associated with a high median mortality rate of 6%. However, there has been no conclusive evidence about the superiority of different antibiotics in managing scrub typhus in terms of efficacy and tolerability.
Methods: We conducted a network meta-analysis (NMA) using the frequentist method. The included participants were pediatric and adult patients infected with scrub typhus. The primary outcome was the cure rate in the patients included. The subgroup analysis was done according to pediatric or adult patients.
Results: Overall, 14 randomized controlled trials (RCTs) with 1264 participants were included in this study. The NMA revealed that all the investigated antibiotics were associated with cure rates similar to those of doxycycline. The chloramphenicol and minocycline were ranked to be associated with the highest cure rate in the pediatric subgroup and adult subgroup, respectively. Second-generation quinolones, including ofloxacin, ciprofloxacin, and chloramphenicol, were associated with significantly lower adverse event rates than doxycycline.
Conclusion: The current updated NMA provides evidence for the efficacy of chloramphenicol and minocycline in scrub typhus management. However, future large-scale RCTs with longer follow-up times are warranted
Multimodal digital storytelling presentations among middle-school learners of English as a Foreign Language: Emotions, grit, and perceptions
No full text[[abstract]]Despite the increasing attention to technology-enhanced language learning in English-as-a-foreign-language contexts, investigation regarding the effects of multimodal technologies on affective factors (particularly emotions and grit) in digital storytelling has remained underexplored. Therefore, this mixed-methods study reports on positive/negative emotions, grit and learner perceptions in the digital storytelling presentation process as the result of different presentation modes (robot-assisted versus PowerPoint-assisted). With 52 9th-grade middle-school students from two intact classes in a junior high school in central Taiwan, the results from multiple data sources (an emotion questionnaire, a grit survey, a perception survey, student in-class sharing) revealed that the robot-assisted mode was more advantageous in contributing to more positive emotions and in making learners grittier, foregrounding higher perseverance of effort in the learning process. The students having the robot-assisted mode also responded more positively to the overall learning experience
The Behavioral Intention and Influencing Factors of Medical Staff toward COVID-19 Vaccinations
No full text[[abstract]]This study mainly explored the behavioral intention and influencing factors of medical staff toward COVID-19 vaccinations. Medical staff were taken as the research subjects. This study selected 300 research subjects by the intentional sampling method and conducted a questionnaire survey. A total of 260 questionnaires were recovered (a recovery rate of 86%), and the number of valid questionnaires was 212, for an effective questionnaire rate of 81%. SPSS and AMOS were used for statistical analysis. As known from the research results: (1) medical staffs’ perception of COVID-19 vaccinations had a positive and significant impact on their behavioral intention for receiving COVID-19 vaccinations; (2) medical staffs’ perception of COVID-19 vaccinations had a negative and significant impact on the barriers to receiving COVID-19 vaccinations; (3) medical staffs’ motivation of receiving COVID-19 vaccinations had a positive and significant positive effect on their behavioral intention of receiving COVID-19 vaccinations; and (4) medical staffs’ motivation of receiving COVID-19 vaccinations had a positive and significant impact on the barrier to receiving COVID-19 vaccinations
A Magnetic Field Canceling System Design for Diminishing Electromagnetic Interference to Avoid Environmental Hazard
No full text[[abstract]]Electromagnetic interference is a serious and increasing form of environmental pollution, creating many issues in the areas of health care and industrial manufacturing. The performance of high-precision measurement equipment used in health care and the manufacturing industry is sensitive to electromagnetic interference. However, extremely low-frequency magnetic fields (ELFMF), with a frequency range from 3 to 30 Hz, generated by high-power lines have become the main interference source in high-tech foundries. This paper presents a magnetic cancelling system that works by combining active cancelling technology and passive cancelling technology to reduce the ELFMF around high-precision measurement equipment. The simulation and experimental results show the validity and feasibility of the proposed system
LT-FS-ID: Log-transformed Feature Learning and Feature-scaling Based Machine Learning Algorithms to Predict the K-barriers for Intrusion Detection Using Wireless Sensor Network
No full text[[abstract]]The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms
Automated accurate fire detection system using ensemble pretrained residual network
No full text[[abstract]]Nowadays, fires have been commonly seen worldwide and especially forest fires are big disasters for humanity. The prime objective of this work is to develop an accurate fire warning model by using images. In this work, two new deep feature engineering models are proposed to detect the fire accurately using images. To create deep features, residual networks (ResNet) are chosen since these networks are one of the highly accurate convolutional neural networks. In this work, four pretrained ResNets: ResNet18, ResNet50, ResNet101, and InceptionResNetV2 are used. These networks were trained using a cluster of ImageNet dataset and features were extracted using the last pooling and fully connected layers of these networks. Hence, eight feature vectors are chosen using these networks and the top 256 features of these networks are chosen using neighborhood component analysis (NCA). Support vector machine (SVM) classifier has been used for classification. Moreover, by using the eight feature vectors generated, two ensemble models have been presented. In the first ensemble model, generated all features are concatenated and the top 1000 features are chosen using a feature selector used (NCA), and these features are classified using SVM. In the second ensemble model, iterative hard majority voting (IHMV) has been applied to the generated results. The developed ensemble ResNet models attained 98.91% and 99.15% classification accuracies using an SVM classifier with a 10-fold cross-validation strategy. Our results obtained demonstrate the high classification accuracy of our presented ensemble pretrained ResNet-based deep feature extraction models. These developed models are ready to be tested with higher databases before actual real-world application