Interactive and Interpretable Topic Refinement for Analyzing Online Vaccine-Related Narratives

This research aims to develop highly interpretable models that help generate easy-to-explain data representations of social media texts, which will enhance the interpretability of the online measurement extracted from social media user-generated texts. Such a capacity can benefit our research seeking to measure online engagement and its connection to collective decision-making on societal changes. In this research, we develop an interactive and interpretable framework that allows analysts to identify text with similar or distinct narratives. We use social media text related to the Coronavirus disease 2019 (COVID-19) vaccines as a case study and test the capability of our framework in identifying the Anti-vaccine and Pro-vaccine narratives. Our framework offers two major advantages. First, it leverages semi-supervised topic modeling with deep learning architecture to identify topics that distinguishes between Anti-vaccine and Pro-vaccine posts. Second, it incorporates a constrained hierarchical clustering method that allows human-in-the-loop topic refinement through the system interface, where analysts can explore the relationship of topics via visual representation, verify the labels of post instances, or update labels that are more likely to be incorrect or less certain. Our evaluation shows that the results with refinement significantly improve the topics' coherence and allow for exploring the relationship between Anti-vaccine and Pro-vaccine topics

Research on Electrical Characteristics and Reliability Mechanism of GaN High Electron Mobility Transistors and Fin-Field Effect Transistors

In recent years, the growing demand for higher data bandwidth, driven by digitalization across various industries and the widespread use of smartphones, has fueled the rapid development of 5G communication. Additionally, the increasing awareness of environmental concerns and the rising demand for electric vehicles have led to a continuous growth in the market's demand for power devices. These shifts in market demand have prompted significant attention toward Gallium Nitride (GaN). GaN is highly regarded for its advantages, including high electron mobility, excellent thermal stability, and a high breakdown voltage. Therefore, GaN High Electron Mobility Transistors (HEMTs) are particularly anticipated for high-frequency and high-voltage applications. However, power conversion systems always generate heat during operation, which can affect other components, such as central processing units. Silicon-based Fin Field-Effect Transistors (FinFETs), as the most widely used devices in logic operations, exhibit degradation mechanisms closely related to environmental temperature. Therefore, this dissertation primarily focuses on analyzing the electrical characteristics and reliability issues of GaN HEMTs and silicon-based FinFETs through electrical properties, reliability testing, and electrical simulations. In Chapter 3, this dissertation explores the three-stage leakage mechanisms in p-GaN HEMTs. Through the investigation of leakage current contributions at various endpoints, repeated experiments on leakage measurements, low/high voltage off-state stress tests, and temperature-dependent off-state stress tests, the mechanisms at each stage are clarified. These stages are primarily dominated by punch-through leakage, gate electron injection, and defect-assisted thermal field emission. Once the leakage mechanisms are elucidated, the introduction of variables like un-doped GaN (UGaN) thickness and process temperature provides a feasible pathway for adjusting leakage characteristics through process improvements. Experimental results ultimately reveal that a thin UGaN layer and lower process temperatures offer the more effective suppression of punch-through leakage. Conversely, a thick UGaN layer is more effective in curbing current generation. In Chapter 4, the dissertation examines the anomalous saturation current trends between p-GaN HEMTs with low and high carbon doping concentration buffer layers. The p-GaN HEMTs with high carbon doping concentration buffer layers exhibit higher current levels in the saturation region. This result contradicts the common understanding that carbon doping reduces 2DEG concentration. The reasons behind this anomalous trend are clarified through temperature experiments and saturation region stress tests. This anomaly is due to the lower energy barrier in the GaN layer of p-GaN HEMTs with low carbon doping concentration buffer layers. Under saturation region conditions, hot electron injection into the GaN layer occurs more easily, affecting current characteristics. Lastly, the impact of carbon doping concentration on the energy barrier is verified through Silvaco TCAD simulations. In Chapter 5, this dissertation investigates the Drain-Induced barrier lowering (DIBL) effect saturation phenomenon in Schottky-gate GaN HEMTs. Utilizing Silvaco TCAD simulations involving electric fields, energy band diagrams, and 2DEG concentrations at various drain voltage (Vd), the analysis reveals that the saturation of the DIBL effect is rooted in the T- gate structure. This gate structure has the ability to deplete additional 2DEG under large Vd, thus dispersing the electric field that would normally concentrate in the channel, consequently suppressing the DIBL effect. Finally, the thesis examines the relationship between the ability to suppress DIBL and the geometric aspect of the T-gate structure from the perspective of parasitic capacitance. In Chapter 6, this dissertation explores the influence of temperature on the degradation mechanisms in 60nm and 14nm FinFETs. Through fitting the mechanisms, the impact of different voltage conditions on degradation mechanisms is clarified. With increasing gate voltage (Vg), the Hot Carrier Stress (HCS) degradation mechanism transitions from Single Vibrational Excitation (SVE) or Electron-Electron Scattering (EES) mechanisms with relatively higher field dependence to the Multiple Vibrational Excitation (MVE) mechanism with lower field dependence. In the 60nm sample, due to the impact of phonon scattering, the part predominantly governed by SVE transitions to the EES mechanism as the temperature rises. In the 14nm sample at higher temperatures, under higher Vg, the transition shifts from EES to MVE. Finally, the relationship between lifetime (\ucf) and temperature validates this argument

Application of Machine Learning in Real Estate Appraisal Models: A Case Study of Tainan City

The Financial Supervisory Commission (FSC) regulates the calculation methods for banks' own capital and risk-weighted assets. In cases of residential and commercial real estate exposure, the adoption of the "Loan-to-Value (LTV) ratio" is emphasized, highlighting the crucial importance of accurate real estate appraisal for credit risk management in banks. Through the application of machine learning, this study provides banks with real-time and effective property appraisals, enhancing risk management and enabling a more precise assessment of credit risk associated with real estate collateral. This study utilizes real estate transaction data from Tainan, Taiwan, categorizing the data into four building types: residential buildings, apartment complexes, condominiums, and houses. Four different machine learning methods, including linear regression, LASSO, random forest, and support vector machine regression, are employed for predicting the price per square meter of each building type. The performance of these models is evaluated based on RMSE, MAE, and MAPE metrics. The results show that the random forest model performs exceptionally well across all building types, exhibiting the lowest RMSE and MAE, and relatively lower MAPE values. The performance of the Linear Regression and LASSO models is comparable, while the Support Vector Machine Regression model tends to perform less favorably in most building types. The applicability of the models varies across different building types, with the random forest model standing out due to its non-linear modeling capabilities and feature selection advantages. It effectively handles the complex characteristics of diverse building types and provides detailed feature importance analysis. The recommendation is to prioritize the use of the random forest model in practical applications, especially for real estate price predictions involving different building types and multivariate characteristics, offering a more accurate and robust choice

Program Notes of Cyong-Ru Wang Vocal Recital

This report is the program notes of Cyong-Ru Wang\ue2s Vocal Recital held on November 17, 2023, and required for the master\ue2s degree. The repertoire includes the aria \ue2Sposa son disprezzata\ue2 from the opera Bajazet by the Italian composer Antonio Vivaldi of the Baroque era, two songs by the composers Domenico Cimarosa and Giovanni Paisiello of the classical era. For the works of Romantic era, there are three songs by Vincenzo Bellini, four German Lieder by Robert Schumann, four French melodies by Reynaldo Hahn and Hector Berlioz. The repertoire also includes the aria \ue2Mon c\uc5ur s'ouvre \uc3\ua0 ta voix\ue2 from the opera Samson et Dalila and the vocal chamber music titled Three Songs by Frank Bridge of the twentieth century. This report consists of program notes and singing interpretation on the works, including their background knowledge, original and translated texts as well as insights from a singer\ue2s point view

The research on Job Characteristic, Psychological Empowerment and Psychological Contract Breach- A case study of Taiwanese expatriates

Expatriates are the key for today's companies to expand international markets and maintain their strongholds in the face of the trend of globalization. However, there are still many companies that do not provide sufficient resources and support for their overseas personnel, or even do not Allow foreign personnel to gain a full understanding of work assignments and authorization of rights and responsibilities ; in this state, foreign personnel may be affected by factors such as the special nature of work in the country where they are assigned, the national customs of the host country, the political economy, and the company culture of the local organization. Under the catalysis, it affects the inner adjustment of personnel stationed abroad. The question this study intends to explore is the different nature of work that expatriates face in overseas units. Is the complexity the reason for the Psychological Empowerment and Psychological Contract Breach of expatriates? And can possessing specific Personality Traits or increasing and fulfilling Organizational Commitment weaken the degree of Psychological Empowerment and Psychological Contract Breach of expatriates, thereby improving their work performance and willingness? The main subjects of this study are Taiwanese full-time workers who have been posted or are currently working in overseas units. Questionnaires were distributed to respondents in different industries through convenience sampling. A total of 109 questionnaires were eventually collected, with a questionnaire response rate of 68.1%; and Narrative statistics, reliability analysis, correlation analysis and regression analysis were used to conduct data analysis. The research results found that: 01. There is a positive relationship between Psychological Empowerment and Psychological Contract Breach. 02. There is a positive relationship between Job Characteristic and Psychological Empowerment. 03. Psychological Empowerment partially mediates the relationship between Job Characteristic and Psychological Contract Breach. Finally, academic and practical implications are proposed based on the results of this study, hoping to provide future suggestions and directions for subsequent research

Machine Learning-Driven Strategies for Optimizing Next-Gen Vehicular Network Resources

This dissertation represents an integrated progression of research that systematically enhances the Internet of Vehicles (IoV) using advanced machine learning strategies to address commu- nication and computational challenges. The foundational work begins with the adaptation of deep reinforcement learning (DRL) for managing IoV resources and facilitating effective vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. It introduces a novel priority-sensitive task offloading scheme using DRL algorithms to optimize resource allocation in a vehicular fog computing setup. Progressing this, multi-access edge computing (MAEC) and reconfigurable intelligent surfaces (RIS) are integrated into the IoV framework to enhance latency-sensitive applications like autonomous driving, employing a multi-agent DRL for improved resource allocation and reduced delays. Advancing into the application of digital twins (DT) and unmanned aerial vehicles (UAVs), the dissertation explores the adoption of these technologies to enhance IoV network adaptability. A DT-informed IoV framework is outlined, leveraging a framework with two joint parallel processing DRL-based algorithms, termed RADiT, for resource allocation that dynamically responds to the quality of service (QoS) demands. This framework marks an evolution in vehicular networks, enabling smart decision-making under the pressures of high mobility and fluctuating connectivity. Next, this framework is extended to incorporate decentralized learning by underscoring a dual-strategy approach combining a novel hybrid asynchronous federated learning (HAFL) method and a multi-agent DRL algorithm, MARS. This innovative confluence enhances the IoV's efficiency, equipping it to handle the unpred- ictable nature of vehicular networks while maintaining QoS across various conditions. Expanding the scope, this research progresses beyond task offloading to a holistic optimization of network resources. It utilizes asynchronous federated learning (AFL) and a multi-agent DRL approach termed DMAAC algorithm, to improve network functionality, particularly focusing on the stringent latency and reliability standards required by the rigorous demands of ultra-reliable low-latency communications (URLLC) in vehicular networks. Simultaneously, a pioneering cooperative caching scheme, Co-Ca, is introduced to manage the caching resources efficiently, playing a crucial role in URLLC support. These strategies, when applied collectively, augment the robustness and operational efficacy of IoV networks, preparing the infrastructure to meet future smart transportation needs. The final segment utilizes quantum computing within the vehicular metaverse for future-proofing the IoV network. This study introduces the quantum-based learning framework tailored for the vehicular metaverse termed QV-MetaFL framework with two sub-algorithms, the quantum sequential-training-program (Q-STP) and the quantum vehicle-context-grouping (Q-VCG), for cost optimization and data heterogeneity management, marking a transition to quantum federated learning (QFL). Extensive simulations for each of the frameworks demonstrate the superiority of all the proposed frameworks, underscoring the potential for the evolution of smart transportation systems

The Prediction of Bitcoin Price and The Decision to Sell Seizured Bitcoin in Criminal Procedure

From the perspective of asset management, this article explores how law enforcement agencies can use models to predict the price trend of Bitcoin and detect structural change points of return after seizing Bitcoin in criminal procedure, as a reference for selling decisions. Due to the anonymity and decentralization of Bitcoin transactions, law enforcement agencies may not be able to discover the true identity of traders through transaction record tracking. Therefore, Bitcoin has a certain ability to confront the country\ue2s financial supervision and criminal investigation. After its advent, it soon became popular on the dark web, which is full of criminal activities, and became the currency of illegal transactions. Today, cryptocurrency criminal activities are becoming increasingly rampant. Common crime types include: child abuse material, ransomware, stolen funds, cybercriminal administrator, terrrorism financing, scam, fraud shop, darknet market etc. The amount of illegal crimes is increasing year by year. As a result, the number of Bitcoins seized by law enforcement agencies has also increased. From the perspective of asset management, due to the violent price fluctuations of Bitcoin, how law enforcement agencies choose the appropriate time to sell after seizing Bitcoin is an important issue in making price change decisions; especially if a Bitcoin price crisis is detected, law enforcement agencies should promptly sell Bitcoins and keep fiat money instead to avoid significant losses in asset prices. This study collates and analyzes various Bitcoin price prediction models used in domestic literature. In two specific cases, the long short-term memory model is used to predict price trends and the Bayesian change point analysis model is used to detect structural changes. The results were empirically compared with the historical price of Bitcoin. The empirical results show that using the long short-term memory model to predict the price trend of Bitcoin in the next 45 days and using the Bayesian change point analysis model to detect structural change points in Bitcoin returns have a certain degree of effectiveness. The accuracy is of reference value for making Bitcoin price changes decisions

The Study of the Control Yuan National Human Rights Commission\ue2Focusing on \ue3The Paris Principles\ue3

Since modern times, the values of human rights have been constantly changing and developing, and the emergence of globalization has brought about qualitative changes in human rights. Because of the faster travel time, human rights symbols rapidly flow and the human rights politics change correspondingly. In response to this trend, the United Nations has adopted the "Principles relating to the status and functioning of national intitutions for protection and promotion of human rights" (commonly known as the "Paris Principles" ) in 1993 to encourage, advocate and assist countries to set up National Human Rights Institutions, and the "Paris Principles" reveal the organizational elements, mandates and important functions of National Human Rights Institutions. The "Paris Principles" require all countries to establish national human rights institutions at the national level. In order to comply with the advent of human rights globalization and the requirements of the "Paris Principles", countries have also established their own national human rights institutions one after another. Under such a trend, our country is inevitably part of this trend of human rights globalization. In accordance with the Constitution of Five Powers, our country's Control Yuan independently exercises its functions and powers to supervise governments and public servants at all levels. In essence, it already has the mission of protecting human rights. About 50% of the investigation cases actually completed over the years are related to various human rights issues. In order to strengthen the function of the supervisory power in protecting human rights, the Control Yuan established the "Control Yuan National Human Rights Commission" in March 2000, in accordance with the law to strive to promote the improvement of human rights. In order to set up a National Human Rights Institution that meets the requirements of the "Paris Principles", the Legislative Yuan passed the "Organic Act of the Control Yuan National Human Rights Commission" on Human Rights Day on December 10, 2019, and amended the Article 3-1, Paragraph 1, Item 7 of the "Organic Act of the Control Yuan" on the qualifications of Members of the Control Yuan with a professional background in human rights, and was promulgated by the President on January 8, 2020. Pursuant to the authorization of the "Organic Act of the Control Yuan National Human Rights Commission", the Control Yuan had decreed that the "Organic Act of the Control Yuan National Human Rights Commission" will be implemented on May 1, 2020, and the "National Human Rights Commission" will be officially inaugurated on August 1, 2020, the day of the 6th Members of the Control Yuan take office. It has brought our country to a new milestone in the promotion and protection of human rights, and expected to implement the ideal of human rights nationhood in accordance with the standards of international human rights law. This study explores the requirements of the "Paris Principles"; whether the Control Yuan National Human Rights Commission was established under the trend of human rights globalization, that is, the "Organic Act of the Control Yuan National Human Rights Commission" and amendments of the "Organic Law of the Control Yuan" meet the requirements of the "Paris Principles"; what are the constitutional issues may occur after the establishment of the Control Yuan National Human Rights Commission

Anti-gonorrhea activities of Bacillus velezensis M4019 isolated from a local aquaculture pond

Neisseria gonorrhoeae (GC) is a human pathogen causing sexually transmitted infection gonorrhea. GC possesses a growing health concern due to emerging antibiotic resistance. Therefore, discovering new treatments is needed. This study isolated Bacillus velezensis M4019 from an aquaculture pond in Kaohsiung and displayed specific antimicrobial activity against GC. We then investigated the antimicrobial potential from the secretion of this isolate. The results revealed reduced GC growth via agar dilution assay, encompassing antimicrobial activity as evidenced by time-kill curve analysis. We also observed biofilm inhibition in vitro, reduced infectivity on ME180 cells, and conducted mechanistic exploration through transmission electron on microscope observations. Furthermore, the responsible compound extracted suggests that the unknown compound may be a molecule under 1kDa with medium polarity and temperature resistant property. Our findings highlight M4019's anti-gonococcal property, potentially through secreting GC-targeting antimicrobials, which has not been reported

Assessing growth rate and carbon fixation of Gracilaria spp. in different environments

Macroalgae are regarded as one of the important biological carbon sinks, converting inorganic carbon to organic carbon and sequestering carbon into deep oceans or marine sediments in the form of Particulate Organic Carbon (POC) and Dissolved Organic Carbon (DOC). Gracilaria tenuistipitata and Gracilaria coronopifolia are two typical large-scale cultured species in Taiwan. The former grows in brackish water, and the latter is distributed in the southern coastal areas. In this thesis, G. tenuistipitata and G. coronopifolia were cultured at different salinities for 14 days. The results showed that G. tenuistipitata had the highest growth rate (2.43 \uc2\ub1 0.12 % d-1) at salinity 10-15, and the carbon fixation rate was 961.7 \uc2\ub1 40.5 mg-C m-2 d-1, of which the DOC fixation rate was 121.4 \uc2\ub1 4.0 mg-C m-2 d-1. G. coronopifolia had the highest growth rate (2.78 \uc2\ub1 0.2 % d-1) at salinity 32, and the carbon fixation rate was 1218.5 \uc2\ub1 39.3 mg-C m-2 d-1, of which the DOC fixation rate was 220.0 \uc2\ub1 35.1 mg-C m-2 d-1. Summarizing the experimental results, the growth rate of macroalgae is proportional to the carbon fixation rate, and the main product is POC. The produced DOC of G. tenuistipitata and G. coronopifolia account for 13% and 18% of the total carbon fixation rate, respectively. The growth rate of macroalgae is not necessarily positively correlated with the DOC production rate, but the produced DOC cannot be ignored. The refractory DOC (RDOC) produced by Gracilaria spp. is not covered in this thesis, but the residence time of RDOC in the ocean is estimated to be thousands of years and it will be an important issue that needs to be studied on seaweed research in the future