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Predictive models for type 2 diabetes mellitus in han Chinese with insights into cross-population applicability and demographic specific risk factors
No full textBackgruound: The rising global incidence of type 2 diabetes mellitus (T2DM) underscores the need for predictive models that enhance early detection and prevention across diverse populations. This study aimed to identify predictors of incident T2DM within a Han Chinese population, assess their impact across various age and sex demographics, and explore their applicability to European populations.
Methods: Using data from about 65,000 participants in the Taiwan Biobank (TWB), we developed a predictive model, achieving an area under the receiver operating characteristic curve of 90.58%. Key predictors were identified through LASSO regression within the TWB cohort and validated using over 4 million records from Taiwan's Adult Preventive Healthcare Services (APHS) program and the UK Biobank (UKB).
Results: Our analysis highlighted 13 significant predictors, including established factors like glycosylated hemoglobin (HbA1c) and blood glucose levels, and less conventionally considered variables such as peak expiratory flow. Notable differences in the effects of HbA1c levels and polygenic risk scores between the TWB and UKB cohorts were observed. Additionally, age and sex-specific impacts of these predictors, detailed through APHS data, revealed significant variances; for instance, waist circumference and diagnosed mixed hyperlipidemia showed greater impacts in younger females than in males, while effects remained uniform across male age groups.
Conclusion: Our findings offer novel insights into the diagnosis and management of diabetes for the Han Chinese and potentially for broader East Asian populations, highlighting the importance of ethnic and demographic diversity in developing predictive models for early detection and personalized intervention strategies.補正完畢KO
Dynamic modeling for noise mapping in urban areas
No full textEnvironmental noise has been a major environmental nuisance in metropolitan cities. To achieve the goal of sustainable community, noise reduction is an important approach. Without systematic noise mapping, the spatio-temporal distribution of noise variations is hard to capture. This study proposes a new methodology framework to combine statistical models and acoustic propagation for dynamic updates of 2D and 3D traffic noise maps by using a limited number of noise sensors in Taipei City based on multisource data including noise monitoring, vehicle detectors, meteorological data, road characteristics, and socio-demographic data. The hourly mean difference between the predicted and measured noise level is within the range of −6.25 dBA to −4.46 dBA in the 2D noise model. For the 3D noise model, the hourly mean prediction error is within the range of 0.02 dBA to 1.93 dBA. Based on the WHO benchmark for excessive road traffic noise, we found at least 30% of inhabitants in Taipei City are exposed to levels exceeding 53 dBA Lden, and >25% are exposed to noise levels exceeding 45 dBA Lnight. The noise maps not only can help identify vulnerable communities to adopt proper approaches for noise reduction but also can remind the residents to take action to improve their quality of life.補正完畢NL
Closed-form solutions for the Weibull distribution parameters and performance lifetime index with interval-censored data
No full textIn lifetime testing, reliably assessing the life performance index of the Weibull distribution under Type I interval-censored data is a critical task. Although maximum likelihood estimation (MLE) is a conventional approach for parameter estimation, closed-form solutions are unavailable for this data type. To address this limitation, four least-squares estimation methods based on data transformation are developed. The proposed estimations can provide closed-form solutions for the Weibull distribution and life performance index. The asymptotic unbiasedness and normality of the proposed estimators are rigorously established. Their effectiveness is further supported by simulation studies. Moreover, the practical relevance of the methods is illustrated with two real-data applications.補正完畢CH
Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles
No full textIdentifying realistic pollution source profiles and quantifying the contributions of atmospheric particulate matter are crucial for the development of pollution mitigation strategies to protect public health. In this paper, we proposed a multivariate source apportionment model by using a Bayesian framework for latent source profiles to incorporate expert knowledge regarding emissions that can facilitate source profile estimation, and atmospheric effects, such as meteorological conditions, can improve source concentration estimations. This approach can maintain positivity and summation constraints for source contributions and profiles.
Furthermore, available expert knowledge regarding source profiles is incorporated as prior knowledge to avoid restrictive assumptions regarding the presence or absence of chemical constituent tracers in source profile modeling. We used long-term PM2.5 measurements collected from two locations with different environmental characteristics in northern Taiwan to demonstrate the feasibility of the proposed model and evaluated its performance by using simulated data.補正完畢NL
Superconducting Driven Electron Cyclotron Resonance as Plasma Generator in Discharge Chamber of Ion Thruster
No full text補正完畢國內新北市，台灣TW
A PCA Approach to Estimate the Q-matrix
No full textThe primary purpose for this research is to estimate the Q-matrix using an exploratory factor analysis (EFA) of tetrachoric correlations. Results from simulation studies suggest that an EFA of tetrachoric correlations is feasible for estimating the Q-matrix, with recovery rates from different concoctions above 0.920. All analyses in this research are implemented in R補正完畢TW
英文
No full textCultural expositions have evolved from static displays to dynamic experiential platforms that integrate immersive technologies, fostering innovation in event organizing and management. In the field of event organizing education, these expositions provide an ideal setting for exploring immersive teaching methods and experiential learning strategies. This study examines the role of AR as a pedagogical tool in event organizing training, focusing on its impact on learners' experiential value, perceived authenticity, satisfaction and skill development. To address gaps in existing research, this study conducted an 18-week quasi-experimental investigation involving 120 undergraduate and postgraduate learners enrolled in event organizing programs in Taiwan. The learners, representing diverse cultural backgrounds, were divided into two groups: an experimental group, which engaged in AR-based event simulations, and a control group, which received traditional lecture-based instruction. Results demonstrated that AR-enhanced training significantly improved learners’ engagement, practical competencies, and ability to design immersive audience experiences. These findings highlight the potential of AR to transform event organizing education by bridging theory with practice, fostering deeper learner engagement, and preparing future event professionals with the skills necessary for real-world applications. This study offers practical implications for event educators, organizers, and industry stakeholders, advocating for the integration of AR-driven experiential learning into event organizing curricula to align with evolving industry demands.補正完畢GB
Flavones and Aminoflavones Increase the Cytotoxicity of NK Cells in Human Non-Small Cell Lung Cancer
No full textNatural flavonoids (flavones) and synthetic aminoflavones are known for their anti‐cancer properties; however, their immunomodulation ability has been largely unexplored. This study determined that synthetic flavones and aminoflavones modulate the cytotoxicity of natural killer (NK) cells against lung cancer cells. Notably, flavones 2, 3, and 6 and aminoflavone 8 were shown to increase the cytotoxicity of NK‐92MI cells against A549 lung cancer cells without adversely affecting MRC5 normal cells. Aminoflavone 8 enhanced NK‐92MI cell cytotoxicity, as evidenced by the elevated expression of cytotoxic effectors, such as IFN‐γ, perforin, and granzyme B. Aminoflavone 8 also inhibited STAT3 phosphorylation in A549 lung cancer and NK‐92MI cells under co‐culture conditions. Moreover, aminoflavone 8 exhibited anti‐tumour effects in a lung cancer xenograft mouse model. Combined therapy with aminoflavone 8 and NK‐92MI cells had synergistic anti‐tumour effects without liver or kidney toxicity. Our analysis revealed that the amino group in the C6 position of aminoflavone 8 was crucial to the enhanced cytotoxicity of NK cells. These findings suggest that aminoflavone 8 can potentiate NK cell cytotoxicity against lung cancer cells, highlighting its potential as a novel therapeutic agent for the treatment of lung cancer.補正完畢GB
ResNet-SE-CBAM Siamese Networks for Few-Shot and Imbalanced PCB Defect Classification
No full textDefect detection in mass production lines often involves small and imbalanced datasets, necessitating the use of few-shot learning methods. Traditional deep learning-based approaches typically rely on large datasets, limiting their applicability in real-world scenarios. This study explores few-shot learning models for detecting product defects using limited data, enhancing model generalization and stability. Unlike previous deep learning models that require extensive datasets, our approach effectively performs defect detection with minimal data. We propose a Siamese network that integrates Residual blocks, Squeeze and Excitation blocks, and Convolution Block Attention Modules (ResNet-SE-CBAM Siamese network) for feature extraction, optimized through triplet loss for embedding learning. The ResNet-SE-CBAM Siamese network incorporates two primary features: attention mechanisms and metric learning. The recently developed attention mechanisms enhance the convolutional neural network operations and significantly improve feature extraction performance. Meanwhile, metric learning allows for the addition or removal of feature classes without the need to retrain the model, improving its applicability in industrial production lines with limited defect samples. To further improve training efficiency with imbalanced datasets, we introduce a sample selection method based on the Structural Similarity Index Measure (SSIM). Additionally, a high defect rate training strategy is utilized to reduce the False Negative Rate (FNR) and ensure no missed defect detections. At the classification stage, a K-Nearest Neighbor (KNN) classifier is employed to mitigate overfitting risks and enhance stability in few-shot conditions. The experimental results demonstrate that with a good-to-defect ratio of 20:40, the proposed system achieves a classification accuracy of 94% and an FNR of 2%. Furthermore, when the number of defective samples increases to 80, the system achieves zero false negatives (FNR = 0%). The proposed metric learning approach outperforms traditional deep learning models, such as parametric-based YOLO series models in defect detection, achieving higher accuracy and lower miss rates, highlighting its potential for high-reliability industrial deployment.國科會(NSTC)補正完畢CH