The Secret of Internet Celebrities: A Qualitative Study of Online Opinion Leaders on Weibo

Internet celebrities are widely observable on social media platforms and are an essential component to a social media marketing program. Drawing on the media success literature and online influencer research, this study reflects Internet celebrities’ fame-seeking practices and discusses on three issues from literature: definition of Internet celebrities, Internet celebrities’ identification criteria and what factors can contribute to individual online influence. A series of interview from three Internet celebrities and three followers of each celebrity were conducted to enrich this study. The results show that Internet celebrities are people who have become famous by means of Internet, and have the ability to influence others. They are characterized as having a certain number of followers; high level of interactivity on their profile; and promising business value. Moreover, this study suggests a five-dimensional perspective to understand individual online influence. We believe that these findings provide new insights for interpretation of Internet celebrities and suggest a possible success formula for fame-seekers to achieve influence on the increasingly competitive social media platforms

Comparing Firocoxib and Meloxicam in the application of Microneedle Patch for Transdermal Drug Delivery

This thesis compares the performance of meloxicam and firocoxib in the aspects of its physical characteristic, chemical compositions, and in-vitro performances for transdermal pain management microneedle patches on farm animals. The microneedle patches are composed of polyvinyl alcohol (PVA), type I collagen (COL), and chitosan (CHI) as base material that carries NSAIDs to achieve therapeutic purposes. Scanning electron microscopy (SEM) was utilized to observe the morphological and physical characteristics of the microneedle patches. Both meloxicam and firocoxib microneedle patches were successfully prepared using the methodology, with organized microneedle distribution and sizing. And Fourier transform infrared spectroscopy (FTIR) confirmed the chemical composition of the PVA-COL-CHI-MEL microneedle patch. The PVA-COL-CHI-MEL microneedle also confirmed its ability to penetrate the skin of pig’s ears and dissolve over 24 hours through in-vitro penetration tests. These results revealed the PVA-COL-CHI-MEL microneedle patch’s potential to deliver pain-management drugs through transdermal contact

Comparing Firocoxib and Meloxicam in the application of Microneedle Patch for Transdermal Drug Delivery

This thesis compares the performance of meloxicam and firocoxib in the aspects of its physical characteristic, chemical compositions, and in-vitro performances for transdermal pain management microneedle patches on farm animals. The microneedle patches are composed of polyvinyl alcohol (PVA), type I collagen (COL), and chitosan (CHI) as base material that carries NSAIDs to achieve therapeutic purposes. Scanning electron microscopy (SEM) was utilized to observe the morphological and physical characteristics of the microneedle patches. Both meloxicam and firocoxib microneedle patches were successfully prepared using the methodology, with organized microneedle distribution and sizing. And Fourier transform infrared spectroscopy (FTIR) confirmed the chemical composition of the PVA-COL-CHI-MEL microneedle patch. The PVA-COL-CHI-MEL microneedle also confirmed its ability to penetrate the skin of pig’s ears and dissolve over 24 hours through in-vitro penetration tests. These results revealed the PVA-COL-CHI-MEL microneedle patch’s potential to deliver pain-management drugs through transdermal contact

Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss

Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pre-train the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the-art, delivering the best testing accuracy (98.38%), precision (0.937), and F1-measure (0.924) on the normal scene testing set, together with the best overall accuracy (98.36%) and precision (0.844) in the challenging scene test set, while the training time can be substantially shortened.Comment: 12 pages, 8 figures, under review by journal of IEEE Transactions on Intelligent Transportation System

Statistical Properties of Robust Satisficing

The Robust Satisficing (RS) model is an emerging approach to robust optimization, offering streamlined procedures and robust generalization across various applications. However, the statistical theory of RS remains unexplored in the literature. This paper fills in the gap by comprehensively analyzing the theoretical properties of the RS model. Notably, the RS structure offers a more straightforward path to deriving statistical guarantees compared to the seminal Distributionally Robust Optimization (DRO), resulting in a richer set of results. In particular, we establish two-sided confidence intervals for the optimal loss without the need to solve a minimax optimization problem explicitly. We further provide finite-sample generalization error bounds for the RS optimizer. Importantly, our results extend to scenarios involving distribution shifts, where discrepancies exist between the sampling and target distributions. Our numerical experiments show that the RS model consistently outperforms the baseline empirical risk minimization in small-sample regimes and under distribution shifts. Furthermore, compared to the DRO model, the RS model exhibits lower sensitivity to hyperparameter tuning, highlighting its practicability for robustness considerations

Analysis of the Innovation and Limitations of Artificial Intelligence in Language Generation: A Case Study of ChatGPT

With the rapid development of technology, a large number of artificial intelligence models have emerged. Artificial intelligence has received widespread attention in language generation, but there are still shortcomings in natural language processing. To promote the widespread application of artificial intelligence, this article, represented by ChatGPT, uses qualitative research methods (text analysis) to evaluate the performance of artificial intelligence in article translation and simulated dialogue, and analyzes its innovation and limitations in language generation. This article analyzes that artificial intelligence has innovation in vocabulary richness and contextual adaptability, but has limitations in semantic understanding and logical coherence. Based on this, this article proposes model optimization in technology to break through technical limitations; strengthen regulatory measures in law and formulate relevant rules; emphasize the role of users in applications and enhance human-computer interaction modes. In the future, the breadth and depth of research can be further expanded to promote the efficient application of language generation

The Internal and External Determinants of Cost Efficiency in the U.S. Commercial Banks

This paper employs two models to provide empirical evidence on the impact of internal and external determinants on cost efficiency in the U.S. The sample consists of 76 commercial banks in the U.S. with 547 observations. Stochastic Frontier Analysis (SFA) model is used for the first step. Four pre-tests are conducted to test the applicability of data and the model. After passing Gamma, M3T, skewness, and likelihood ratio test, cost efficiency scores can be worked out by SFA. In the second step, the scores are regarded as the dependent variables in the Tobit regression model. Bank-level variables such as the logarithm of total assets, loan loss reserves to gross loans, equity to assets, and return on average return, as well as country-level like GDP, inflation, and unemployment rate, are treated as independent variables. Correlation and coefficients are identified in the Tobit regression model. The results indicate that too big to fail issues exist in the estimation of cost efficiency. Large banks tend to be less cost-efficient than those small counterparts because banks with large assets lean on the bailout of government. Besides, more cost inefficiency is contributed by medium-sized banks. Loan loss reserves to gross ratio has a negative relationship with cost efficiency. Banks with high-quality assets normally decrease the amount of loan loss reserves during a good economic condition. As a result, the decline of reserves for loss losses enhances cost efficiency. Regulations about capital impede the cost efficiency. Profitability can be reflected by the return on average assets ratio. Banks with high profitability have less cost efficiency. Those banks can promote their cost efficiency by improving cost control ability and downsizing staff. There is a negative impact of unemployment rate on cost efficiency however no significant relationship is founded among GDP growth rate, inflation and cost efficiency

The effect of CSR disclosure on company profitability: Empirical evidence from China

The machinery manufacturing industry has made outstanding contributions to China's economic development. While manufacturing companies are pursuing maximum profitability, they have caused many social problems, especially environmental pollution. However, these social problems directly affect the profitability of enterprises. As a result, more and more manufacturing enterprises are conscious of the importance of undertaking CSR and disclosing CSR. However, there is still no consensus on what impact CSR will have on the profitability of the company. This dissertation first uses the stakeholder theory and signal theory to determine the framework of the theoretical part of this paper. After that, based on the literature review, this paper quantifies CSR disclosure and profitability. Then, for the sake of determining the relationship between CSR disclosure and profitability, the paper has the data regression analysis based on financial data and CSR disclosure scores of 80 listed companies from 2013 to 2017. Finally, this paper discusses how CSR disclosure affects profitability based on the analysis results, which providing a basis for promoting the implementation of CSR in manufacturing and other industries. In short, this paper argues that CSR disclosure has a positive impact on profitability. This paper puts forward some suggestions on CSR disclosure from different perspectives

Optimal therapy schedule of chimeric antigen receptor (CAR) T cell immunotherapy

Chimeric antigen receptor (CAR) T-cell therapy is a personalized immunotherapy approach in which a patient's T cells are genetically engineered to express synthetic receptors that specifically recognize and target tumor-associated antigens. This approach has demonstrated remarkable success in treating B-cell malignancies by directing CAR-T cells against the CD19 protein. However, treatment efficacy is influenced by the composition and distribution of CAR-T cell subsets administered to the patient. To investigate the impact of different CAR-T cell subtypes and infusion strategies, we developed a mathematical model that captures the dynamic interactions between tumor cells and CAR-T cells within the tumor immune microenvironment. Through computational simulations, we explored how varying the dosage and subtype proportions of infused CAR-T cells affects tumor dynamics and therapeutic outcomes. Our findings highlight the critical role of CAR-T cell subset composition in optimizing treatment efficacy, underscoring the necessity of precise dosing control and tailored infused strategies to maximize therapeutic success