the New Relevance: Motives behind YouTube Use

This study applies the uses and gratifications theory (UGT) to discover how people are motivated to use YouTube, an example of Internet based technologies, similarly and differently to watch traditional broadcast television. The new features such as commenting, liking and uploading that YouTube offers can be seen as new affordances that might offer ne gratifications to users, which were not found salient to television viewing. A convenience sample of 127 students was recruited to participate in an online survey that included measures of traditional media motives (Rubin, 1983) as well as new media motives adapted from Sundar and Limperos (2013). The study found that participants were motived to use YouTube for passing time/habit and entertainment, which were similar to their motives for watching traditional broadcast television. The scale on new media motives did not provide conceptually coherent motives relevant to either media. Paired-sample t-tests were performed, which revealed some differences in specific items about new motives across the two media outlets

ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Convective meta-thermal concentration for ultrahigh efficient Stirling engine with waste heat and cold utilization

The Stirling engine, which possesses external combustion characteristics, a simple structure, and high theoretical thermal efficiency, has excellent potential for utilizing finite waste heat and cold resources. However, practical applications of this technology suffered from thermal inefficiency due to the discontinuity and instability of waste resources. Despite advances in energy storage technology, temperature variations in the heat-exchanging fluids at the hot and cold ends of the Stirling engine remained significant obstacles. In this work, convective meta-thermal concentration (CMTC) was introduced between the heating (cooling) fluids and the hot (cold) end of the Stirling engine, employing alternating isotropic materials with high and low thermal conductivities. It was demonstrated that CMTC effectively enhanced the temperature difference between the hot and cold ends, leading to a remarkable improvement in Stirling engine efficiency. Particularly, when the Stirling engine efficiency tended to zero due to the limited availability of waste heat and cold resources, CMTC overcame this limitation, surpassing existing optimization technology. Further analysis under various operating conditions showed that CMTC achieved a significant thermal efficiency improvement of up to 1460%. This work expanded the application of thermal metamaterials to heat engine systems, offering an exciting avenue for sustainable energy utilization

Detection of iodixanol-induced allergic reaction signals in Chinese inpatients: a multi-center retrospective database study using prescription sequence symmetry analysis

Objective:This study aimed to explore the signal detection method for allergic reactions induced by inpatient iodixanol injection.Methods:A database of 3,719,217 hospitalized patients from 20 large Chinese general hospitals was processed and analyzed using the prescription sequence symmetry analysis (PSSA) method.Results:126,680 inpatients who used iodixanol and were concurrently treated with anti-allergic drugs were analyzed. In the medical records of these patients, only 32 had documented iodixanol allergies. Statistical analysis identified 22 drugs in 4 categories—calcium preparations, adrenergic/dopaminergic agents, glucocorticoids, and antihistamines—as marker drugs. With time intervals of 3, 7, and 28 days, the adjusted sequence ratios (aSRs) for all anti-allergics and the 4 categories were greater than 1. The 7-day aSRs were 2.12 (95% CI: 2.08–2.15), 1.70 (95% CI: 1.68–1.73), 3.85 (95% confidence interval [CI]: 3.75–2.30), 2.30 (95% CI: 2.26–2.35), and 1.95 (95% CI: 1.89–2.02), respectively. The proportions of adverse drug events indicated by each signal were as follows: all anti-allergics (2.92%–3%), calcium gluconate (0.19%–0.52%), adrenergic/dopaminergic agents (2.20%–3.37%), glucocorticoids (3.13%–3.76%), and antihistamines (1.05%–1.32%).Conclusion:This first multi-center Chinese inpatient database study detected iodixanol-induced allergy signals, revealing that reactions may be much higher than those in collected spontaneous reports. Iodixanol risk exposure was closer to actual pharmaceutical care findings. PSSA application with ≤7-day intervals appears better suited for monitoring late allergic reaction signals with these drugs