• Childhood Kidney Diseases
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• v.23 no.2
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• pp.77-85
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• 2019

Contrast-associated acute kidney injury (CA-AKI) is a major concern when iodinated contrast material is administered, especially in patients at risk. Efforts have been undertaken to understand the detrimental effects of contrast media (CM). With the use of low-osmolar or iso-osmolar CM the incidence of CA-AKI has steadily decreased within the past decade; however, especially in the pediatric population information is scarce. Incidence rates have been reported to range between 0% to 18.75%, particularly depending on indication, selection of population (i.e. preexisting co-morbidities), and definition of AKI. Different biomarkers have been proposed, but confirmatory studies are either lacking or have contributed to their lack of diagnostic power. Proteomic approaches have been employed and may pave the way to such discovery. Prevention strategies have been tested and proposed, but the recently published AMACING and PRESERVE trials have shown that commonly used strategies (such as systematic hydration or administration of N-acetylcysteine) have no role in the prevention of CA-AKI. We propose that thoughtful assessment of one's fluid state is the most appropriate approach and depending on the hydration status diuretics or fluid administration should be provided to achieve an euvolemic state ahead of contrast exposure.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.135-143
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• 2021

Information has an essential role in decision-making for investors who will invest in financial markets, especially regarding the policies on the condition of COVID-19. The purpose of this study is to determine the market reaction to the information published by the government regarding the policy changes to the provisions of Trading Halt on the IDX in an emergency using the event study method. The population in this study was companies listed on the Indonesia Stock Exchange in March 2020; the sample selection technique was purposive sampling. Data analysis used a normality test and one sample T-test. The results of the study found that there were significant abnormal returns on the announcement date, negative abnormal returns around the announcement date, and significant trading volume activity occurring three days after the announcement. The existence of a significant positive abnormal return on the announcement date indicates that the market responds quickly to information published by the government. The practical implication of this research can be taken into consideration for investors in making investment decisions to analyze and determine the right investment options so that investors can minimize the risk of their investment and maximize the profits they want to achieve.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1-10
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• 2021

This study examines the dynamic pattern of the exchange rate volatilities of the ASEAN-5 currencies from January 2006 to August 2020. The exchange rates applied in this study comprise bilateral and effective exchange rates in order to investigate the influence of the US dollar on the stability of the ASEAN-5 currencies. Since a volatility model employed in this study is a natural cubic spline volatility model, the Monte Carlo simulation is consequently conducted to determine an appropriate criterion to select a number of quantile knots for this model. The simulation results reveal that, among four candidate criteria, Generalized Cross-Validation is a suitable criterion for modeling the ASEAN-5 exchange rate volatilities. The estimated volatilities showed the inconstant dynamic patterns reflecting the uncertain exchange rate risk arising in international transactions. The bilateral exchange rate volatilities of the ASEAN-5 currencies to the US dollar are more variable than their corresponding effective exchange rate volatilities, indicating the influence of the US dollar on the stability of the ASEAN-5 currencies. The findings of this study suggest that the natural cubic spline volatility model with the quantile knots selected by Generalized Cross-Validation is practical and can be used to examine the dynamic patterns of the financial volatility.

• Korea Trade Review
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• v.44 no.6
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• pp.345-361
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• 2019

In recent years, Chinese firms have explosively increased outward foreign direct investment (oFDI). While state-owned enterprises (SOEs) are still dominant in Chinese oFDI, privately-owned enterprises (POEs) are also accelerating their internationalization. These two types of Chinese firms differ in their behavior regarding oFDI. The objective of this paper is to analyze the differences in the choice of oFDI locations between Chinese SOEs and POEs by considering host country factors. By integrating the literature on Chinese firms' oFDI and on FDI locations, we developed six hypotheses concerning how host country factors affect their choice of location. We tested our hypotheses by conducting multiple regression analysis with recent secondary data on 413 Chinese MNEs in 88 countries between 2005 and 2016. The results of the test show that in selecting oFDI locations, Chinese SOEs invest relatively more in countries with richer natural resources, more abundant strategic assets, less production efficiency, higher political risk, and lower institutional quality compared with Chinese POEs. It is our hope that the empirical results of this paper will contribute to research on Chinese oFDI.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.83-93
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• 2021

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1097-1114
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• 2021

The primary objective of this work was to discover a solution for the survival of people in an emergency flood. The geographical information was obtained from remote sensing techniques. Through helpline numbers, people who are in need request support. Although, it cannot be ensured that all the people will acquire the facility. A proper link is required to communicate with people who are at risk in affected areas. Mobile sensor networks with field-programmable gate array (FPGA) self-configurable radios were deployed in damaged areas for communication. Ad-hoc networks do not have a centralized structure. All the mobile nodes deploy a temporary structure and they act as a base station. The mobile nodes are involved in searching the spectrum for channel utilization for better communication. FPGA-based techniques ensure seamless communication for the survivors. Timely help will increase the survival rate. The received signal strength is a vital factor for communication. Cognitive radio ensures channel utilization in an effective manner which results in better signal strength reception. Frequency band selection was carried out with the help of the GRA-MADM method. In this study, an analysis of signal strength for different mobile sensor nodes was performed. FPGA-based implementation showed enhanced outcomes compared to software-based algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.343-359
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• 2021

Purpose - The purpose of this paper is to investigate the relationship between earnings management and the disclosure effect of share repurchase. In addition, we analyze whether the relationship between earnings management and share repurchase is affected by executive stock options. Design/methodology/approach - We calculate the discretionary accrual amount for the year immediately preceding the share repurchase and the cumulative excess return around the announcement of the share repurchase, and examine the relationship between the two by regression analysis. Findings - We confirmed a negative relationship between discretionary accrual in the year immediately preceding the share repurchase and the market response to the share repurchase disclosure. In particular, it was found that the negative relationship between discretionary accrual and stock price return on share repurchase announcement was found to decrease in companies to which executive stock options were granted. Research implications or Originality - When uncertainties exist in the motives for share repurchase, we find that earnings management and executive stock options can be useful tools for reducing the adverse selection risk inherent in share repurchase announcements.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.