弘光科技大學

Featured Application This study provides a validated quantitative imaging methodology for assessing the real-time integrity of personal protective equipment (PPE). The findings offer a scientific basis for developing anthropometrically optimized protective clothing designs and precision fit-testing protocols, particularly for healthcare workers in high-consequence infectious disease (HCID) environments.Abstract Personal protective equipment (PPE) is critical for defending against airborne biological hazards; however, current standard testing protocols often rely on "black-box" aggregate metrics or qualitative visual inspections that fail to pinpoint localized vulnerabilities. This study proposes a novel, spatially resolved quantitative methodology combining a whole-body fluorescent aerosol exposure chamber with an entropy-based image processing algorithm. By establishing a robust linear calibration mode, we accurately mapped and quantified localized aerosol ingress through protective clothing interfaces. Dynamic human-in-simulant tests were conducted using three suit models on two subjects with distinct body morphologies over 2- and 5-min exposure durations. Quantitative results revealed two distinct morphological failure mechanisms. A well-fitted suit resulted in steady " Steady Accumulation," where the total body leakage mass increased consistently (e.g., from 3.29 to 4.19 mu g/cm2) while maintaining stable standard deviation, indicating preserved structural integrity. Conversely, an oversized fit induced "Structural Instability" and an erratic "Bellows Effect." This mismatch was characterized by a dramatic inflation in aerosol leakage standard deviation during extended dynamic movements, rather than a simple increase in the mean leakage. Ultimately, this study empirically proves that protective clothing efficacy is highly morphology-dependent. The proposed quantitative methodology provides a rigorous scientific tool for diagnosing localized interface failures, thereby facilitating targeted improvements in PPE design and occupational safety.

Background: Social isolation is increasing in aging societies; however, its relationship with depressed mood and sarcopenia is not well studied. This study aims to examine the influence of social network on depressed mood and sarcopenia among community-dwelling older adults in Taiwan. Methods: We collected data from a sample of 981 older adults residing in the community. These individuals received government-subsidized preventive healthcare services for adults at a district hospital in Taipei in 2021. The social network of the older adults who participated was assessed using the Lubben Social Network Scale, while depressed mood was assessed using the Geriatric Depression Scale. The definition of sarcopenia used in this study was based on the 2019 Asian Working Group for Sarcopenia. Results: According to this study, sarcopenia was present in approximately 15 % of older adults. Multiple logistic regression analysis showed that older adults who had poor social network and did not meet the recommended 150 min of regular physical activity per week were more likely to have depressed mood. Additionally, older adults who were older, underweight, did not engage in regular physical activity, and had poor social network were more likely to have sarcopenia. Conclusion: Poor social network was associated with increased risks of depressed mood and sarcopenia among older adults.

BackgroundParkinson’s disease (PD) is a rapidly growing neurodegenerative disorder associated with gait impairment, functional decline, and increased fall risk. Conventional clinical assessments rely heavily on brief observational scales and are limited by subjectivity and environmental bias. There is a pressing need for objective, quantitative, and scalable digital assessment tools to support precision management of PD.ObjectiveThis study aimed to develop and validate a smartphone-video-based gait assessment system aligned with a clinically meaningful pathway encompassing PD screening, functional evaluation, and fall risk classification.MethodsIn this cross-sectional observational study, patients with mild-to-moderate PD and age-matched healthy controls were recruited following Institutional Review Board approval. Gait videos were recorded using smartphone cameras and processed through a silhouette extraction pipeline. Spatiotemporal gait features were subsequently extracted using a MoviNet-based deep learning framework. A multi-task framework was developed to perform PD screening, activities of daily living functional grading based on the Schwab and England Activities of Daily Living (S&E ADL) scale, and fall risk classification. Model performance and generalizability were evaluated using subject-independent data partitioning and independent cross-site validation.ResultsA total of 40 participants were recruited. For PD screening, the model achieved an accuracy of 95.7%, a sensitivity of 96.6%, a specificity of 92.2%, and an AUC of 0.994 on the independent test set. For S&E ADL functional grading, the model achieved an overall accuracy of 88.5% across three functional categories (80, 90, and 100%). For fall risk classification, the model achieved an accuracy of 91.1%, a sensitivity of 68.4%, a specificity of 98.3%, and an AUC of 0.909. Overall, the framework demonstrated robust performance across PD screening, functional stratification, and fall risk classification tasks.ConclusionThis study developed a smartphone-video-based gait assessment framework using computer vision and deep learning. The proposed framework demonstrated strong performance in PD identification, severity estimation, and fall risk classification. Future work will expand validation across diverse populations and disease stages, while advancing toward multimodal integration and long-term home monitoring.

Entrepreneurial bricolage is a critical factor for firm success in resource-constrained environments. While entrepreneurial bricolage is widely acknowledged as beneficial in the early stages of firm development, its long-term impact on firm growth remains theoretically ambiguous and empirically inconclusive. The role of contextual factors in this relationship has been largely overlooked. This study aims to examine the effect of entrepreneurial bricolage on firm growth and to investigate the moderating role of network structural holes. Using data from 184 cultural and creative industry (CCI) firms in Taiwan and applying hierarchical regression analysis, the research distinguishes between market structural holes (SHM) and technological structural holes (SHT), proposing that each type plays a different moderating role. The results reveal an inverted U-shaped relationship between entrepreneurial bricolage and firm growth, indicating that moderate levels enhance firm growth, while excessive use may hinder it. Moreover, bridging SHM strengthens this positive relationship, whereas bridging SHT weakens it. The research contributes to bridging entrepreneurship and social network theory by SHM and SHT as contingent factors, offering an interdisciplinary perspective on entrepreneurial bricolage effectiveness. In addition, the empirical evidence highlights the theoretical tension between selective and extensive entrepreneurial bricolage by validating an inverted U-shaped relationship with firm growth.

A constant relative biological effectiveness (RBE) value of 1.1 is used for proton therapy (PT) in many clinical treatment plans. However, several studies show that RBE varies with proton energy, linear energy transfer (LET), and oxygen concentration. This study presents a computational method based on the linear quadratic (LQ) and repair-misrepair-fixation (RMF) models to calculate tumor control probability (TCP) under varying oxygen conditions. We analyze the impact of hypoxia on the parameters of the LQ model, focusing on the ratio and RBE. The proposed method allows for TCP calculations across different oxygen concentrations and for various ion therapies, such as proton and carbon ion therapy. Our results show that increasing the LET from 1 to 12 keV/μm enhances TCP from 61% to 98% under aerobic conditions (21% O2), 45% to 98% under moderately hypoxic conditions (2% O2), and from 1% to 48% under severely hypoxic conditions (0.1% O2). These findings are compared with clinical trial data, demonstrating that hypoxia significantly affects TCP for low-LET radiations.