• Asian Journal for Public Opinion Research
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• v.8 no.4
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• pp.432-452
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• 2020

This study explored the representation of risks from the controversial Lynas rare earth refining as a risk event by five Malaysian online mainstream and alternative newspapers using qualitative content analysis. The aim is to uncover the role of the news media in the social amplification and attenuation of risks within the literature evidence as those roles are still uncertain. Content analysis is used to explore the online newspapers' roles guided by the Social Amplification of Risk Framework (SARF). The representations typified environmental, financial, health, occupational, property, radioactive, and technological risks and established connections between four risk types (environmental, financial, radioactive, and health risks). Radioactive risk was repeatedly associated with other risks, suggesting that the volume and information flow focused on radioactive risk as a key ingredient for amplification. This connection shows that the nature of the relationship between risks is multidimensional, contradicting the unidirectional type found in previous studies. Alternative online newspapers amplified and attenuated more risks, thus, providing more diverse coverage than mainstream sources. Consequently, this study provides evidence that risk representation from rare earth refining in a digital news environment is multidimensional and intensified or weakened in a multi-layered pattern. The stakeholders are engaged in a contestation by positioning their narratives to oppose or support their interests, which are amplified or attenuated by the online newspapers as social amplification stations.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.845-852
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• 2015

Cancer illness representations and screening history among residents of Kolkata, India, were investigated along with socio-demographic characteristics in an effort to understand possible motivations for health behavior. A total of 106 participants were recruited from community locations in Kolkata, India and completed surveys including demographics, the illness perception questionnaire-revised (IPQ-R), and previous experience with cancer and screening practices. Participants were 51.5% college educated, 57% female, 51.5% full-time employed with average age of 32.7 years (R: 18-60 years). Descriptive statistics were generated for the subscales of the IPQ-R, cancer-screening practices and cancer experience. Correlation analyses were conducted to investigate associations between cancer representations and socio-demographic variables. Univariate ANOVAs were calculated to determine gender differences in IPQ-R subscales and differences between participants who knew someone diagnosed with cancer versus those who did not. While 76% of participants knew someone with cancer, only 5% of the sample engaged in cancer screening. Participants perceived cancer as a serious illness with negative emotional valence. Younger age (r(100)=-.36, p<0.001) and male gender (F(1, 98)=5.22, p=0.01, ${\eta}_2$=0.05) were associated with better illness coherence. Males also reported greater personal control (F(1, 98)=5.34, p=0.02, ${\eta}_2$=0.05) were associated with better illness coherence. Low screening rates precluded analyses of the relationship between illness representations and cancer screening. Cancer was viewed as a threatening and uncontrollable disease among this sample of educated, middle class Kolkata residents. This view may act as a barrier to seeking cancer screening. Public awareness campaigns aimed at improving understanding of the causes, symptoms and consequences of cancer might reduce misunderstandings and fear, especially among women and older populations, who report less comprehension of cancer.

• 대한예방의학회:학술대회논문집
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• 1994.02a
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• pp.426-430
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• 1994

The assessment of exposure is an important component of the risk assessment process. Exposure information is used in risk assessment in at least two ways: 1) in the identification of hazards and the epidemiologic research investigating exposure-response relationships and 2) in the development of population exposure estimates. In both of these cases, the value of a chemical risk assessment is enhanced by improvements in the quality of exposure assessments. The optimum exposure assessment is the direct measurement of population exposure; however, such measurements are rarely available. Recent developments in methods for exposure assessment allow estimates to be made that are valid representations of actual exposure. The use of these exposure estimates to classify exposures correctly enhances the likelihood that causal associations between exposure and response will be correctly identified and that population risks will be accurately assessed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.109-117
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• 2023

Schedule delays present a major risk factor, as they can adversely affect construction projects, such as through increasing construction costs, claims from a client, and/or a decrease in construction quality due to trims to stages to catch up on lost time. Risk management has been conducted according to the importance and priority of schedule delay risk, but quantification of risk on the depth of schedule delay tends to be inadequate due to limitations in data collection. Therefore, this research used the BERT (Bidirectional Encoder Representations from Transformers) language model to convert the contents of aconstruction log, which comprised unstructured data, into WBS (Work Breakdown Structure)-based structured data, and to form a model of classification and quantification of risk. A process was applied to eight highway construction sites, and 75 cases of rain schedule delay risk were obtained from 8 out of 39 detailed work kinds. Through a K-S test, a significant probability distribution was derived for fourkinds of work, and the risk impact was compared. The process presented in this study can be used to derive various schedule delay risks in construction projects and to quantify their depth.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.122-126
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• 2015

Credit enhancement is absolutely essential for financing Independent Power Producer (IPP) projects in Asia particularly for countries whose sovereign credit rating is on non-investment grade and foreign investment is difficult to achieve. Due to nexus of agreements among varies parties in IPP project, it is hard to clearly visualize the roles of these agreements. Examples are: What credit enhancement factors are most influential to minimize the associated risks of IPP projects? Why are they powerful? What are their roles? Who are less powerful and what are the obstacles that causes them less powerful? A research is conducted to identify the credit enhancement factors for IPP projects in Asia. IPP professionals validated 27 out of 28 identified credit enhancement factors, and five factor groupings were made through factor analysis. Afterwards, network theory is applied to find the unanswered questions, which by graphical and mathematical representations show that the host government's credit enhancement, MDBs, ECAs and other parties' credit enhancement are prominent and of great importance to handle the associated risks of IPP projects in Asia

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.697-704
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• 2007

It is common in financial time series that volatility(conditional variance) as a measure of risk exhibits asymmetry in such a manner that positive and negative values of return rates of the series tend to provide different contributions to the volatility. We are concerned with asymmetric conditional variances for Korean financial time series especially during the time span of 2000-2001. Notice that these periods suffer from 9-11 disaster in US and collapses of stock prices of dot-companies in Korea. Threshold-ARCH models are considered and a Wald test of asymmetry is suggested. News impact curves are illustrated for graphical representations of leverage effects inherent in various Korean financial time series.

• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.1
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• pp.29-36
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• 2015

Base isolation is considered as a seismic protective system in the design of next generation Nuclear Power Plants (NPPs). If seismic isolation devices are installed in nuclear power plants then the safety under a seismic load of the power plant may be improved. However, with respect to some equipment, seismic risk may increase because displacement may become greater than before the installation of a seismic isolation device. Therefore, it is estimated to be necessary to select equipment in which the seismic risk increases due to an increase in the displacement by the installation of a seismic isolation device, and to perform research on the seismic performance of each piece of equipment. In this study, modified NRC-BNL benchmark models were used for seismic analysis. The numerical models include representations of isolation devices. In order to validate the numerical piping system model and to define the failure mode, a quasi-static loading test was conducted on the piping components before the analysis procedures. The fragility analysis was performed by using the results of the inelastic seismic response analysis. Inelastic seismic response analysis was carried out by using the shell finite element model of a piping system considering internal pressure. The implicit method was used for the direct integration time history analysis. In addition, the collapse load point was used for the failure mode for the fragility analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.