• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.343-353
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• 2016

Oligochaetes distribute widely in freshwater ecosystem, and some species are used as bioindicators for water quality assessment because they are tolerant to organic enrichment. They are acknowledged for potential for environmental health recovery of organic polluted environment. There are a lot of studies on ecology and toxicity assessment using oligochaetes in aquatic environment. In this study, we reviewed literature on ecotoxicology of aquatic oligochaetes. We searched literature from a database 'google scholar' by using keywords such as aquatic, oligochaete, and toxicity. The literature were summarized according to publication years, species, test methods, and chemicals. We obtained 133 articles published from 1953 to 2015 from the database. Among them, 58 papers(43.6% of total) have been published in 1990s. Three species(Lumnbriculus variegatus, Tubifex tubifex, and Limnodrilus hoffmeisteri) have been used most frequently in the study. Different species displayed different toxicological responses to different toxic chemicals. The results on the ecotoxicological study with aquatic oligochaetes revealed the possibility of the development for early warning system using aquatic oligochaetes to monitor aquatic ecosystem disturbance.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.784-794
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• 2020

In order to effectively evaluate the urban water security, the study investigates a novel system to assess factors that impact urban water security and builds an urban water poverty evaluation index system. Based on the contribution rates of Resource, Access, Capacity, Use, and Environment, the study adopts the Water Poverty Index (WPI) model to evaluate the water poverty levels of 14 cities in Gansu during 2011-2018 and uses the least variance method to evaluate water poverty space drive types. The case study results show that the water poverty space drive types of 14 cites fall into four categories. The first category is the dual factor dominant type driven by environment and resources, which includes Lanzhou, Qingyang, Jiuquan, and Jiayuguan. The second category is the three-factor dominant type driven by Access, Use, and Capability, which includes Longnan, Linxia, and Gannan. The third category is the four-factor dominant type driven by Resource, Access, Capability, and Environment, which includes Jinchang, Pingliang, Wuwei, Baiyin, and Zhangye. The fourth category is the five-factor dominant type, which includes Tianshui and Dingxi. The driven types impacting the urban water security factors reflected by the WPI and its model are clear and accurate. The divisions of the urban water security level supply a reliable theoretical and numerical basis for an urban water security early warning mechanism.

• Journal of Contemporary Eastern Asia
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• v.20 no.2
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• pp.85-107
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• 2021

Wildfires have become increasingly common and intense in South Korea because of climate change, but few have recognized the catastrophic level of the problem. Given the significant impact of wildfires, emergency management stakeholders must have effective risk communication structures for rapidly responding to such phenomena and overcoming geographical difficulties. Despite the country spending billions of dollars to build a big databased early warning system, risk communication flow during the 2017 Gangneung wildfire was ineffective, thereby causing substantial economic, social, and environmental losses. To examine the patterns of information exchange in South Korea's risk communication networks and their structural characteristics during the wildfire, we conducted semantic and network analyses of real-time data collected from social media. The results showed that the inefficient flow of risk information prevented emergency responders from adequately assessing the emergency and protecting the population. This study provides new insights into effective risk communication responses to catastrophic events and methods of research on webometric approaches to emergency management.

• Geomechanics and Engineering
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• v.25 no.2
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• pp.135-142
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• 2021

This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.17-22
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• 2021

The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.23-30
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• 2021

In recent years, hazardous flash flooding has caused deaths and damage to infrastructure in Saudi Arabia. In this paper, our aim is to assess patterns and trends in climate means and extremes affecting flash flood hazards and water resources in Saudi Arabia for the purpose to improve risk assessment for forecast capacity. We would like to examine temperature, precipitation climatology and trend magnitudes at surface stations in Saudi Arabia. Based on the assessment climate patterns maps and trends are accurately used to identify synoptic situations and tele-connections associated with flash flood risk. We also study local and regional changes in hydro-meteorological extremes over recent decades through new applications of statistical methods to weather station data and remote sensing based precipitation products; and develop remote sensing based high-resolution precipitation products that can aid to develop flash flood guidance system for the flood-prone areas. A dataset of extreme events has been developed using the multi-decadal station data, the statistical analysis has been performed to identify tele-connection indices, pressure and sea surface temperature patterns most predictive to heavy rainfall. It has been combined with time trends in extreme value occurrence to improve the potential for predicting and rapidly detecting storms. A methodology and algorithms has been developed for providing a well-calibrated precipitation product that can be used in the early warning systems for elevated risk of floods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1360-1388
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• 2021

Shipbuilding and repairing enterprise has the characteristics of many hazards and accidents. Therefore, the safety management ability of shipbuilding and repairing MIS (management information system) must be effectively guaranteed. The verification on safety management is the necessary measure to ensure and improve the safety management ability of MIS. Safety verification can not only increase the safety of MIS, but also make early warning of potential risks in management to avoid the accidents. Based on the authoritative standards in the field of safety in shipbuilding and repairing enterprise, this paper applied modeling and verification method based on ontology to safety verification of MIS, extracted the concepts and associations from related safety standards to construct axiom set to support safety verification on MIS of shipbuilding and repairing enterprise. Then, this paper developed the corresponding safety ontology modeling and verification tool-SOMVT. By the application and comparison of two examples, this paper effectively verified the safety of MIS to prove the modeling method and the SOMVT can improve the safety of MIS in a much more effective and stable way to traditional manual analysis.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.91-98
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• 2021

One of the concerns in the economic policy circle is the fiscal sustainability. This current research revisit the notion of fiscal sustainability for Malaysia using the Indicator of Fiscal Sustainability (IFS) developed by Croce and Juan-Ramón (2003) where we employ samples of time-series data from 1970 to 2017. The findings reveal that 40 out of 48 years, during which the calculated IFS algorithm is above the threshold of 1, imply Malaysia was fiscally unsustainable. Despite having been fiscally unsustainable, Malaysia's fiscal stance shows improvement as a result of fiscal consolidation and fiscal reforms during the sample period. This is shown by the improved calculated IFS algorithm on average, which the value improved from 1.465 in 1970-1993 to 1.377 in 1998-2004 and to 1.146 in the 2006-2013. From the policy front, this indicator can serve as a precautionary early warning measure in formulating future fiscal path for Malaysia. This can be executed by targeting debt ratio and shifting the allocation of expenditures away from less efficient toward more growth-enhancing ones, which eventually would regain fiscal space to counter any incoming economic shocks in the future. This can enhance the fiscal transparency and assist in formulating a fiscal policy strategy in Malaysia.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.6
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• pp.965-973
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• 2021

The behavior of angelfish Pterophyllum scalare exposed to low and high temperatures was monitored by video tracking, and information such as the initial speed, changes in speed, and locations of the fish in the tank were analyzed. The water temperature was raised from 26℃ to 36℃ or lowered from 26℃ to 16℃ for 4 h. The control group was maintained at 26℃ for 8 h. The experiment was repeated five times for each group. Machine learning analysis comprising a long short-term memory model was used to train and test the behavioral data (80 s) after pre-processing. Results showed that when the water temperature changed to 36℃ or 16℃, the average speed, changes in speed and fractal dimension value were significantly lower than those in the control group. Machine learning analysis revealed that the accuracy of 80-s video footage data was 87.4%. The machine learning used in this study could distinguish between the optimal temperature group and changing temperature groups with specificity and sensitivity percentages of 86.9% and 87.4%, respectively. Therefore, video tracking technology can be used to effectively analyze fish behavior. In addition, it can be used as an early warning system for fish health in aquariums and fish farms.

• Earthquakes and Structures
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• v.22 no.1
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• pp.53-64
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• 2022

This study proposes an intelligent semi-active isolation system combining a variable-stiffness control device and ground motion characteristic prediction. To determine the optimal control parameter in real-time, a genetic algorithm (GA)-fuzzy control law was developed in this study. Data on various types of ground motions were collected, and the ground motion characteristics were quantified to derive a near-fault (NF) characteristic ratio by employing an on-site earthquake early warning system. On the basis of the peak ground acceleration (PGA) and the derived NF ratio, a fuzzy inference system (FIS) was developed. The control parameters were optimized using a GA. To support continuity under near-fault and far-field ground motions, the optimal control parameter was linked with the predicted PGA and NF ratio through the FIS. The GA-fuzzy law was then compared with other control laws to verify its effectiveness. The results revealed that the GA-fuzzy control law could reliably predict different ground motion characteristics for real-time control because of the high sensitivity of its control parameter to the ground motion characteristics. Even under near-fault and far-field ground motions, the GA-fuzzy control law outperformed the FPEEA control law in terms of controlling the isolation layer displacement and the superstructure acceleration.