• Archives of Plastic Surgery
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• 제47권2호
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• pp.146-152
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• 2020

Background Preoperative computed tomography angiography (CTA) of the abdominal wall vessels is used when planning free flap breast reconstruction (FFBR) because it provides a surgical road map which facilitates flap harvest. However, there are few reports on the effect of abnormal findings on the operative plan. Methods We conducted a retrospective study of all FFBRs performed at a tertiary referral center over a 6-year period (November 2011 to June 2017). One consultant radiologist reported on the findings. Details on patient demographics, CTA reports, and intraoperative details were collected. Results Two hundred patients received preoperative CTAs. Fourteen percent of patients (n=28) had abnormal findings. Of these findings, 18% were vascular anomalies; 36% tumorrelated and 46% were "miscellaneous." In four patients, findings subsequently prevented surgery; they comprised a mesenteric artery aneurysm, absent deep inferior epigastric (DIE) vessels, bilateral occluded DIE arteries, and significant bone metastases. Another patient had no suitable vessels for a free flap and the surgical plan converted to a pedicled transverse rectus abdominis musculocutaneous flap. The remaining incidental findings had no impact on the surgical plan or appropriateness of FFBR. More than one in 10 of those with abnormal findings went on to have further imaging before their operation. Conclusions CTA in FFBR can have a wider impact than facilitating surgical planning and reducing operative times. Incidental findings can influence the surgical plan, and in some instances, avoid doomed-to-fail and unsafe surgery. It is therefore important that these scans are reported by an experienced radiologist.

• 유통과학연구
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• 제13권9호
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• pp.5-11
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• 2015

Purpose - The purpose of this research is to provide insights that can be used for deliberate decision making around challenging big data investments by measuring the economic value of such big data implementations. Research design, data, and methodology - We perform empirical research through an event study. To this end, we measure actual abnormal returns of companies that are triggered by their investment announcements in big data, or firm size information, during the three-year research period. The research period targets a timeframe after the introduction of big data at Korean firms listed on the Korea stock markets. Results - Our empirical findings discover that on the event day and the day after, the abnormal returns are significantly positive. In addition, our further examination of firm size impacts on the abnormal returns does not show any evidence of an effect. Conclusions - Our research suggests that an event study can be useful as an alternative means to measure the return on investment (ROI) for big data in order to lessen the difficulties or decision making around big data investments.

• International Journal of Safety
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• 제11권1호
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• pp.26-32
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• 2012

This paper introduces a feasibility evaluation method for prognosis systems based on an empirical model in nuclear power plants. By exploiting the dynamical signature characterized by abnormal phenomena, the prognosis technique can be applied to detect the plant abnormal states prior to an unexpected plant trip. Early $operator^{\circ}{\emptyset}s$ awareness can extend available time for operation action; therefore, unexpected plant trip and time-consuming maintenance can be reduced. For the practical application in nuclear power plant, it is important not only to enhance the advantages of prognosis systems, but also to quantify the negative impact in prognosis, e.g., uncertainty. In order to apply these prognosis systems to real nuclear power plants, it is necessary to conduct a feasibility evaluation; the evaluation consists of 4 steps (: the development of an evaluation method, the development of selection criteria for the abnormal state, acquisition and signal processing, and an evaluation experiment). In this paper, we introduce the feasibility evaluation method and propose further study points for applying prognosis systems from KHNP's experiences in testing some prognosis technologies available in the market.

• 산업융합연구
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• 제1권1호
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• pp.127-142
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• 2003

This Study examines the implications for event studies using the daily stock data. The output present the event study results. The event period is defined from 30 days before through 30 days after the event date, and is broken into four "windows" for abnormal return cumulation: the pre-event period, days -30 through -2; dajys -1 and 0, a period commonly investigated for the immediate impact of the event; and the post-event period, days +1 through +30. It shows how firm's information offerings affect the price process and consequent issues. The Patell Z test is an examples of a standardized abnormal return approach, which estimate a separate standard error for each security-event and assumes cross-sectional independence. The generalized sign test adjusts for the fraction of positive abnormal returns in the estimation period instead of assuming 0.5.

• 아태비즈니스연구
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• 제13권1호
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• pp.247-262
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• 2022

Purpose - The purpose of this study is to examine the impact of the Pandamic Declaration on 'untact firms' listed in KOSPI and KOSDAQ market in order to verify Investor Mania Hypothesis. Design/methodology/approach - This study collected financial data for 44 untact firms in KOSPI and KOSDAQ market. Then, we employed ESM(Event Study Methodology), EGARCH model and DID(Difference-In-Difference) for analysis. Findings - First, in contrast with the benchmarking index, KOSPI 200 which shows a negative (-) abnormal return trend, the untact firms have positive abnormal return trend consistently. Second, after the Pandemic Declaration, the variability of abnormal return for the untact firms is found to be significantly positive. Third, we find that the cumulative abnormal return and volatility of the untact firms significantly increase after the Pandemic Declaration. Research implications or Originality - Based on the Investor Mania Hypothesis, we confirm that the market potential of untact firms after the Pandemic Declaration is observed when compared with the KOSPI 200.

• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.256-266
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• 2016

Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.463-468
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• 2007

The abnormal sound of generator frame is analyzed by a acceleration signal. The spike-like time signal is major characteristics of impacting force. The distributional map of vibration level is one of visualization method. With map, noise source was easily detected. After de_assembly of generator, loose part of internal component is the source of impact force by mechanical movement of stator inherently. In contact condition of part with clearance, the level of impact signal is different at each revolution and impact signal did not happens periodically.

• Asia pacific journal of information systems
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• 제30권2호
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• pp.328-352
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• 2020

If a firm announces an investment in IT security, how the market value of its competitors reacts to the announcement? We try to shed light on this question through an event study design. To test the relationship, we collected 143 announcements on cybersecurity investment and measured the subsequent impact on 533 competitors' abnormal returns, spanning from 2000 to 2019. Our estimation results present that, on average, the announcements have no observable impact on the market value of announcing firms and competitors as well, which is consistent with findings of a prior study. Interestingly, however, the impact becomes evident when we classify our samples by industries (Finance vs. non-Finance or ICT vs. non-ICT) and firm size (Big vs. Small). We interpret our empirical findings through the lenses of contagion effect and competition effect between announcing firms and their competitors. Key finding of our study is that, for financial service firms, the effect resulting from the announcement on cybersecurity investment transfers to competitors in the same direction (i.e., contagion effect).

• Journal of Computing Science and Engineering
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• 제2권2호
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• pp.180-199
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• 2008

Technology has had a tremendous impact on our daily lives. Recently, technology and its impact on aging has become an expanding field of inquiry. A major reason for this interest is that the use of technology can help older people who experience deteriorating health to live independently. In this paper we give a brief review of the in-home monitoring technologies for the elderly. In the pilot study, we analyze the possibility of employing the data generated by a continuous, unobtrusive nursing home monitoring system for predicting elevated(abnormal)pulse pressure(PP) in elderly(PP=systolic blood pressure-diastolic blood pressure). Our sensor data capture external information(behavioral) about the resident that is subsequently reflected in the predicted PP. By continuously predicting the possibility of elevated pulse pressure we may alert the nursing staff when some predefined threshold is exceeded. This approach may provide additional blood pressure monitoring for the elderly persons susceptible to blood pressure variations during the time between two nursing visits. We conducted a retrospective pilot study on two residents of the TigerPlace aging in place facility with age over 70, that had blood pressure measured between 100 and 300 times during a period of two years. The pilot study suggested that abnormal pulse pressure can be reasonably well estimated (an area under ROC curve of about 0.75) using apartment bed and motion sensors.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.131-137
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• 2020

The research investigates the impact of the lockdown period caused by the COVID-19 to the stock market of India. The study examines the extent of the influence of the lockdown on the Indian stock market and whether the market reaction would be the same in pre- and post-lockdown period caused by COVID-19. Market Model Event study methodology is used. A sample of 31 companies listed on Bombay Stock Exchange (BSE) are selected at random for the purpose of the study. The sample period taken for the study is 35 days (24 February-17 April, 2020). An event window of 35 days was taken with 20 days prior to the event and 15 days during the event. The event (t1) being the official announcement of the lockdown. The results indicate that the market reacted positively with significantly positive Average Abnormal Returns during the present lockdown period, and investors anticipated the lockdown and reacted positively, whereas in the pre-lockdown period investors panicked and it was reflected in negative AAR. The study finds evidence of a positive AR around the present lockdown period and confirms that lockdown had a positive impact on the stock market performance of stocks till the situation improves in the Indian context.