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(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs.

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain–computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain–computer interface and elicitation of an event-related potential (P300 wave). Significance Conventional electroencephalogram (EEG) recording systems, particularly the hardware components that form the physical interfaces to the head, have inherent drawbacks that limit the widespread use of continuous EEG measurements for medical diagnostics, sleep monitoring, and cognitive control. Here we introduce soft electronic constructs designed to intimately conform to the complex surface topology of the auricle and the mastoid, to provide long-term, high-fidelity recording of EEG data. Systematic studies reveal key aspects of the extreme levels of bending and stretching that are involved in mounting on these surfaces. Examples in persistent brain–computer interfaces, including text spellers with steady-state visually evoked potentials and event-related potentials, with viable operation over periods of weeks demonstrate important advances over alternative brain–computer interface technologies.

The Korean government has been continuously conducting diverse national R&D programs to discover new growth engines. The Republic of Korea is one of the countries with the largest investment in national R&D, but its efficiency was relatively low. In response, this study established a framework to identify the characteristics and direction of outstanding R&D programs. In this study, the performance of the R&D programs was identified in the sub-program unit. The efficiency of the national R&D program was analyzed using the data envelopment analysis model through the outputs of the national R&D programs such as papers and patents. However, patent and paper output would take time to be realized. Therefore, this study also calculated the diversity index of R&D programs to identify their potential expected performance. This study applied the suggested framework in the electric vehicle fields, which is one of the core growth engines of South Korea. A list of outstanding programs was identified from the National Institute of Science and Technology Information (NTIS) data. Additionally, this study also discovered the main technology areas and their current issues of outstanding and brand-new R&D programs. These results could contribute to suggesting the policy direction to conduct high-performance national R&D programs.

Domain Name Server (DNS) amplification Distributed Reflection Denial of Service (DRDoS) attacks are a Distributed Denial of Service (DDoS) attack technique in which multiple IT systems forge the original IP of the target system, send a request to the DNS server, and then send a large number of response packets to the target system. In this attack, it is difficult to identify the attacker because of its ability to deceive the source, and unlike TCP-based DDoS attacks, it usually uses the UDP protocol, which has a fast communication speed and amplifies network traffic by simple manipulating options, making it one of the most widely used DDoS techniques. In this study, we propose a simple convolutional neural network (CNN) model that is designed to detect DNS amplification DRDoS attack traffic and has hyperparameters adjusted through experiments. As a result of evaluating the accuracy of the proposed CNN model for detecting DNS amplification DRDoS attacks, the average accuracy of the experiment was 0.9995, which was significantly better than several machine learning (ML) models in terms of performance. It also showed good performance compared to other deep learning (DL) models, and, in particular, it was confirmed that this simple CNN had the fastest time in terms of execution compared to other deep learning models by experimentation.

The rising number of minorities working in the service industry (US Bureau of Labor Statistics, 2020) leads to more interracial service encounters. Literature has provided evidence of racial bias in the marketplace, such as home-sharing, taxi services or ridesharing, and crowd funding. While a large body of literature provides evidence of ingroup favoritism or outgroup derogation, Boshoff (2012) found that consumers show more negative emotions toward an ingroup service provider under service recovery. Given the increasing concerns about racial discrimination in service industries, this research aims to examine whether consumers show ingroup aversion when service providers have negative review ratings. Specifically, we examine the influence of racial congruences (i.e., race match vs. mismatch) among service providers, reviewers, and consumers. Overall, we have demonstrated conditional ingroup favoritism for White consumers and conditional ingroup aversion for all racial consumers. Our research contributes to the literature by examining the phenomenon of negative average review ratings and the perspective of racial minorities.

This study examines the investment efficiency of private and public firms in Korea. Prior studies suggest that the investment efficiency of firms can change according to the companies' agency problem caused by the existence of information asymmetry. Moreover, they argue that there is less information asymmetry in private firms than in public firms, because the major investors of private firms have access to the internal information of the companies. We extend these studies by comparing the investment efficiency of private and public firms using an extended audited financial dataset of Korean firms. Our results show that the investment efficiency of private firms is higher than that of public firms, because the agency problem of the former is lower than that of the latter. Additionally, private firms invest more efficiently in R&D and capital expenditures than public firms. Further, when we use alternative exogenous firm-specific proxies to measure the likelihood of over or under-investment, the results are substantially consistent with the main results. Finally, we re-test our hypotheses by including financial reporting quality proxies as control variables in the main regression model. These investigations further support our main results. Our study contributes to emerging literature on the difference between private and public firms by showing that the investment efficiency of the former is different from that of the latter. In addition, this study provides additional evidence on the agency problem that affects firms' investment decisions.

This study examines the effect of the abnormal pay dispersion on earnings management. Prior studies find that pay dispersion among top executives affect firm performance and executive turnover. We expect that abnormal pay dispersion among top executives affects financial reporting practice as well as firm performance and turnover and provide evidence of positive association between abnormal pay dispersion and earnings management. This result suggests that executives are more likely to be engaged in earnings management to increase their compensation when they feel unfairness from the relative level of compensation. This finding helps financial statement users interpret firm performance and anticipate future outcomes by implying that additional managerial incentives for financial reporting are derived from internal pay dispersion. Our finding that abnormal pay dispersion leads to higher agency costs should also be of interest to shareholders.

This research theorizes that sellers of durable goods can utilize inferences about the buyer’s willingness to pay based not only on her decision to trade in the old good but also on its characteristics. We find empirical support for this theory using transaction data for new car purchases. The results support the notion that dealers infer a higher willingness to pay and charge higher prices to consumers who trade in a used vehicle than to those who do not. We also find that dealers charge even higher prices to those consumers who trade in used cars that are similar to the new one.

My dissertation research examines implications of product trade-ins for consumer demand and retailer pricing behavior. Although product trade-ins are very prevalent in many durable goods markets, such as golf clubs, home appliances, and automobiles, there has been relatively little academic research examining this phenomenon. I examine how information provided by consumers' previous purchases can be leveraged to predict their future choices and its implications for retailer pricing decisions. In the first chapter, I propose a spatial autoregressive multinomial probit model in which the preferences and marketing mix responsiveness of different consumers depend upon their relative proximity to each other. Unlike previous applications of spatial models in marketing that have focused on the impact of geographical proximity between consumers, I develop a contiguity metric which accounts for the similarity between consumer's previous purchases. I demonstrate that information about a consumer's previous purchase can be harnessed to predict the choices of other consumers for other products as well as simply to improve choice predictions of the same consumer for the same product, known in the marketing literature as structural state dependence. I estimate the proposed model using transaction data for new car purchases in the midsize sedan market. The proposed model outperforms the commonly used random coefficient probit model as well as other spatial models that are based solely on the geographic closeness between consumers or that only incorporate preference or response correlation individually, but not together. I show the spatial correlations in one market may be used to yield more accurate predictions of consumer demand in other markets. I also show that ignoring spatial correlation based on previous vehicle similarity is found to underestimate the elasticity differences between brand-loyal consumers and switchers. Building on this idea, in the second chapter, I investigate how retailers incorporate consumer trade-in information in their pricing of new products, and test it in a field setting. First, I develop an analytical model of the phenomenon, and use it to derive the optimal new car prices with and without trade-in information. Second, I test the predictions of my theoretical model by estimating a hedonic regression model of new car prices obtained from transaction data in the midsize sedan market. Finally, I develop and estimate a structural choice model and conduct a series of counterfactual analysis to quantify the benefit of trade-in information to the dealer. The unique equilibrium solution of my theoretical model suggests that retailers charge a premium on new car when consumers trade in their used cars and that the premiums are even higher if the traded in vehicle is more similar to the new one. My empirical analysis supports my theory by showing that consumers who traded-in a used vehicle paid a higher price for the same new car than those who did not. Further, those consumers who traded in used cars that were of the same make and model of the new vehicle paid an even higher premium on their new car purchase. The structural model estimation results and counterfactual analysis show that price discrimination based on consumer trade-in information reduces market share but increases retailer profits because retailers end up leaving less money on the table. This demonstrates that the better information about consumer preferences provided by trade-ins substantially impacts dealer profits.