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

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

Short-Term Power Demand Forecasting Using Blockchain-Based Neural Networks Models

With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hotspot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a vision-language model (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Videos and code at https://voxposer.github.i

Seismic damage analysis due to near-fault multipulse ground motion

Near-fault pulse-like ground motion is a significant class of seismic records since it tends to cause more severe damage to structures than ordinary ground motions. However, previous researches mainly focus on single-pulse ground motions. The multipulse ground motions that exist in records receive rare attention. In this study, an analysis procedure is proposed to investigate the effect of multipulse ground motions on structures by integrating finite element analysis and an identification method that features each pulse in the multipulse ground motion satisfying the same evaluation criteria. First, the Arias intensity, wavelet-based cumulative energy distribution, and response spectra of identified non-, single-, and multipulse ground motions are compared. Then, the seismic damage on frame structures, a soil slope, and a concrete dam under non-, single-, and multipulse ground motions are analyzed. Results show that the spectral velocity of multipulse ground motions is significantly greater than those of non- and single-pulse ground motions and potentially contains multiple peaks in the long-period range. Seismic damage evaluation indicates that the maximum interstory drift of frame structures with high fundamental periods under multipulse ground motions is about twice that of nonpulse ground motions. Similar characteristics also exist in the soil slope and the concrete dam. Therefore, multipulse ground motions potentially cause more severe damage to structures compared to non- and single-pulse ground motions. The findings of this study facilitate the recognition of the increased seismic demand imposed by the multipulse ground motion in engineering practices, provide new possibilities for ground motion selection in seismic design validation, and shed new light on seismic hazard and risk analysis in near-fault regions