National Quemoy University

Kinmen peanut (Arachis hypogaea L. cultivar Kinmen No. 1) is a unique crop used to produce local specialty “peanut candy”; however, the peanut skins (PSs) are treated as waste owing to the bitter taste. To support the valorization of this agro-industrial by-product, peanut skin ethanolic extract (PSE) was prepared and evaluated for its hypolipidemic potential in a cholesterol/fat-fed hamster model, together with its antioxidant capacity and chemical composition. Hamsters were fed a cholesterol/fat-enriched diet supplemented with PSE at 0.1%, 0.2%, or 0.4% (w/w) for 8 weeks. Serum lipid profiles were determined, and derived atherogenic indices were calculated. In parallel, antioxidant activity was assessed using 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and reducing power assays, while chemical characterization included total phenolics, crude phytosterols, and HPLC profiling of representative phenolic compounds. PSE significantly reduced serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) compared with the cholesterol/fat-enriched control, whereas triglycerides were not significantly altered. The LDL-C/HDL-C ratio was also reduced in PSE-treated groups, with the greatest reduction observed in the 0.1% PSE group (0.33 ± 0.04 vs. 0.56 ± 0.12 in the negative control). In addition, PSE exhibited marked antioxidant activity, with IC50 values of 141.3 and 76.2 μg/mL in the DPPH and ABTS assays, respectively. Chemical analyses showed that PS contained 1098 ± 189 µg β-sitosterol equivalents/g PS and 199.3 ± 4.6 mg gallic acid equivalent (GAE)/g PS, and HPLC identified p-coumaric acid, ferulic acid, gallic acid, chlorogenic acid, daidzein, catechin, and resveratrol as representative phenolic constituents. Collectively, these findings support Kinmen peanut skin as a promising value-added source of bioactives for functional ingredient development targeting cholesterol dysregulation and oxidative processes.

This study aims to enhance the accuracy of underwater fish detection by proposing a dual-enhanced YOLOv11 model. The approach leverages two key improvements: First, a selective kernel attention (SKA) mechanism is incorporated into the YOLOv11 architecture to enable dynamic selection of multi-scale convolution kernels, improving adaptability to various target sizes. Second, an evolutionary tuner (ET) is employed for hyperparameter optimization to refine model performance further. The proposed model achieves significant gains over the baseline, with improvements of 2.06% in mean average precision (mAP)@0.5 and 6.30% in mAP@0.5:0.95, attaining final scores of 98.629% and 86.933%, respectively. The dual-enhanced model demonstrates superior accuracy and robustness in complex underwater environments, ultimately achieving a precision of 99.069% and a recall of 95.968%.

Infantile colic is a common condition during early infancy with unclear etiology, likely involving multifactorial mechanisms and gut dysbiosis. This nationwide population-based study investigated perinatal risk factors and long-term health outcomes associated with infantile colic. Using Taiwan’s national birth cohort data from 2010 to 2015, we identified 19,191 infants with colic and 95,955 matched controls. The inclusion criteria for the infantile colic group were having two or more related ICD codes (ICD-9-CM 789.0x and 780.9) between 1 and 5 months of age. Conditional logistic regression was applied to identify associated risk factors, and Cox proportional hazard models assessed outcomes over a 5-year follow-up. Infantile colic was significantly associated with neonatal antibiotic use, cesarean delivery, maternal atopic diseases, maternal major depressive disorder, and maternal irritable bowel syndrome. Children with a history of colic exhibited higher incidence rates of disorders of gut-brain interaction, including constipation, diarrhea, irritable bowel syndrome, and other functional abdominal pain disorders. Additionally, increased risks of attention deficit hyperactivity disorder and atopic diseases were observed. These findings suggest that infantile colic may reflect early-life biological vulnerabilities linked to gastrointestinal, atopic, and neurodevelopmental outcomes.

Corporate Social Responsibility (CSR) reporting has become an indispensable mechanism for organizations to communicate their sustainability initiatives. However, the growing volume and complexity of these reports necessitates the integration of Natural Language Processing (NLP)-driven text mining techniques to enhance transparency, comparability, and strategic decision-making. This study employs NLP and text mining methodologies to systematically analyze CSR reports with emphasis on environmental sustainability from major public listed companies in Taiwan. Utilizing Principal Component Analysis (PCA), this study classifies sustainability-related topics, extracts key Sustainable Development Goals (SDG)-aligned terms, and evaluates the textual similarities between CSR reports and SDG targets. Five SDGs encompassing 39 specific targets form the analytical framework, and 225 feature words are identified through text mining. The findings indicate that (1) automated CSR topic classification in Chinese is viable, though expert validation remains crucial for linguistic accuracy and semantic integrity; (2) SDG feature word distribution follows the Pareto Principle, with 10% of words contributing to 50.4%, and 28.4% accounting for 80% of total TF-IDF weights; (3) CSR reporting varies by industry, with financial holdings emphasizing sustainable management, energy supply, and water efficiency, while the electronics sector prioritizes waste reduction, recycling, and product lifecycle management; (4) PCA-based classification effectively aligns CSR reports with SDG targets, with textual similarity analysis proving more accurate than principal component scores. From a strategic business perspective, these findings offer critical insights for corporate leaders seeking to refine their sustainability strategies. NLP-based CSR analysis enables companies to benchmark their Environmental, Social, and Governance (ESG) performance against industry peers, identify sustainability gaps, and align corporate strategies with evolving regulatory landscapes and stakeholder expectations. Financial institutions can leverage these insights to develop sustainable finance mechanisms, while manufacturers can enhance circular economy practices by optimizing resource efficiency and waste management. Moreover, policymakers and investors can utilize NLP-driven text mining techniques to assess corporate sustainability efforts more systematically, ensuring greater accountability and fostering data-driven decision-making in ESG governance.

BackgroundInformal caregivers of people living with dementia often face significant physical and psychological burdens, which may limit their utilization of available support services. Needs assessments aim to identify these stressors and facilitate access to supportive resources. This study evaluates the effectiveness of needs assessments in enhancing service utilization and reducing the burden among informal caregivers of people living with dementia in central Taiwan.MethodsA retrospective cohort study was conducted involving 464 informal caregivers of people living with dementia in central Taiwan, with data collected from January 2020 to December 2021. The primary outcome, caregiver burden, was assessed using the Zarit Burden Interview (ZBI) at the initial needs assessment (Time 1) and six months post-assessment (Time 2). Data on other variables, including service utilization, were collected at baseline, Time 1, and Time 2 through structured questionnaires. Linear regression analyses examined the relationships between caregiver burden, service utilization, and influencing factors.ResultsThe implementation of needs assessments was associated with increased long-term care (LTC) service utilization. The proportion of caregivers using services rose from 37.3% at baseline to 45.5% after six months. The caregiver burden distribution shifted, with a reduction in moderate to severe burden (from 20.4 to 17.8%) and a slight increase in mild to moderate burden (from 43.7 to 47.2%). Linear regression analysis revealed several key factors influencing caregiver burden. Supportive resources were significantly associated with lower Zarit Burden Interview (ZBI) scores, an effect that often strengthened over time. Specifically, enhanced self-care abilities and the involvement of a foreign caregiver significantly reduced burden at both Time 1 (B = -4.79 and B = -5.44, respectively) and Time 2(B = -6.63 and B = -6.78, respectively). The positive impact of shared caregiving responsibilities became statistically significant at Time 2 mark (B = -5.86). Conversely, initial stressors were linked to higher burden. Caregiving for individuals with severe disabilities (B = 3.31) and the need to manage barrier-free environments (B = 5.06) were associated with greater burden at Time 1, though their influence diminished over time. Emotional strain was a strong predictor of increased burden at both time points (B = 3.51 at Time 1; B = 2.93 at Time 2), although its impact also lessened. Finally, older caregiver age was correlated with a lower burden at both time points.ConclusionsThe findings highlight the effectiveness of needs assessments in reducing caregiver burden and enhancing service utilization. Needs assessments play a critical role in identifying caregiver stressors and facilitating targeted interventions, essential for sustainable long-term care and caregiver well-being.

The existence of regular and singular bound state solutions to △pu+f(u)=0,r∈Rn∖{0} is considered. Our result concerns the solution according to its behavior as r→0 and r→∞. Under the assumption that f is supercritical for small u>00$\\end{document}]]> and is subcritical for large u>00$\\end{document}]]>, we show the existence of various types of solutions. The Pohozaev identity plays a crucial role in our investigation.

This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management.

The modeling and prediction of oil prices are very important tasks. However, the predictions of oil price by traditional models are not very effective. The challenge comes from the nonlinear and non-stationary dynamics of oil prices, and they are also heavily correlated with global economic condition and financial fluctuations. This study employs a dual-stage attention-based recurrent neural network (DA-RNN) for oil price forecasting. The DA-RNN architecture includes both an encoder and a decoder. The encoder features an input attention mechanism designed to adaptively identify and choose significant and pertinent driving series. The decoder incorporates a temporal attention mechanism to obtain long-term dependencies of the encoded inputs. The dual-stage attention mechanism of DA-RNN enables both input selection and temporal focus, allowing the model to adaptively choose important and relevant driving series while capturing long-term temporal dependencies. Empirical results indicate that DA-RNN achieved lowest prediction errors, for example, RMSE values of approximately 2.2 for WTI, 2.4 for Dubai, and 2.3 for Brent crude oil prices, which reduces about 30 % error compared to other models. These findings clearly demonstrated that the DA-RNN model outperforms traditional econometric methods and machine learning models, highlighting its potential as a powerful tool for energy market predictions.