Taek Lee

Monitoring bacterial community structure and variability in time scale in full-scale anaerobic digesters

Using a high-throughput pyrosequencing technology, this study assessed bacterial community structure and time-scale variability in great detail in seven full-scale anaerobic digesters operated variously in terms of influent substrate, digestion temperature, and reactor configuration. Pyrosequencing generated a total of 83,774 sequence reads from 40 digester sludge samples collected monthly for six months. The highest number of sequence reads were detected within Proteobacteria (20.5%), followed by those within Bacteroidetes (19.7%), Firmicutes (17.8%), and Chloroflexi (4.8%). The relative composition of bacterial populations was varied within the digesters as well as between the digesters, and the bacterial community structures were mainly influenced by digestion temperature. Detailed bacterial community structures were assessed by analyzing the operational taxonomic units (OTUs) based on 97% sequence similarity, which resulted in a total of 9051 OTUs. Among these, a total of 31 core OTUs were analyzed and inferred phylogenetically, which enabled us to classify the sequences within an unclassified phylum. Unclassified sequences were mostly affiliated with the sequences within Spirochaetes and Firmicutes. Interestingly, numerically dominant novel phylotypes (18% of the total sequence reads) presumably involved in anaerobic digestion within Spirochaetes were identified. Temporal variability was further explored using a non-metric multidimensional scaling ordination which demonstrated that the variability of the bacterial community within the digesters was smaller than between digesters. Correlation analysis demonstrated that digester performance and operational conditions affected the pattern of bacterial community in the ordination. Additionally, a multi-response permutation procedure revealed that the bacterial communities within the digesters were more similar than those belonging to other digesters statistically, demonstrating a patchiness of the digesters in the distribution of bacterial populations. Overall, this study revealed the correlation of bacterial community structure and time-scale variability with digester performance and operating conditions.

Distribution and Abundance of Spirochaetes in Full-scale Anaerobic Digesters

To investigate the distribution and abundance of spirochaetal communities within anaerobic digesters, pyrosequencing of the 16S rRNA gene was conducted. Phylogenetic analysis identified a cluster which included the majority of core spirochaetal operational taxonomic units (OTUs) and environmental clones but no pure-culture strains. Distribution of the core OTUs demonstrated an importance of local factors in shaping the structure of Spirochaetes. Spirochaetal to bacterial 16S rRNA gene copy numbers varied from 1.3% to 30.0% depending on digester samples. Environmental variables were found to influence the relative abundance of Spirochaetes. In a batch anaerobic digestion experiment testing the response to different substrates, acetate most stimulated the activity of Spirochaetes, suggesting possible acetate oxidation by syntrophic acetate oxidation process. Taken together, the results obtained in this study provides an insight into the ecology of Spirochaetes in anaerobic digesters and a basis for future studies examining ecological function of these bacteria.

A security risk analysis model for information systems

Information security is a crucial technique for an organization to survive in these days. However, there is no integrated model to assess the security risk quantitatively and optimize its resources to protect organization information and assets effectively. In this paper, an integrated, quantitative risk analysis model is proposed including asset, threat and vulnerability evaluations by adapting software risk management techniques. It is expected to analyze security risk effectively and optimize resources to mitigate the risk.

Cyber Threat Trend Analysis Model Using HMM

Prevention is normally recognized as one of the best defense strategy against malicious hackers or attackers. The desire of deploying better prevention mechanisms has motivated many security researchers and practitioners, who are studies threat trend analysis models. However, threat trend is not directly revealed from the time-series data because the trend is implicit in its nature. Besides, traditional time-series analysis, which predicts the future trend pattern by relying exclusively on the past trend pattern, is not appropriate for predicting a trend pattern in dynamic network environments (e.g., the Internet). Thus, supplemental environmental information is required to uncover a trend pattern from the implicit (or hidden) raw data. In this paper, we propose cyber threat trend analysis model using hidden Markov model (HMM) by incorporating the supplemental environmental information into the trend analysis.

Self-adaptive testing to determine sample size for flash memory solutions

Embedded system testing, especially long-term reliability testing, of flash memory solutions such as embedded multi-media card, secure digital card and solid-state drive involves strategic decision making related to test sample size to achieve high test coverage. The test sample size is the number of flash memory devices used in a test. Earlier, there were physical limitations on the testing period and the number of test devices that could be used. Hence, decisions regarding the sample size depended on the experience of human testers owing to the absence of well-defined standards. Moreover, a lack of understanding of the importance of the sample size resulted in field defects due to unexpected user scenarios. In worst cases, users finally detected these defects after several years. In this paper, we propose that a large number of potential field defects can be detected if an adequately large test sample size is used to target weak features during long-term reliability testing of flash memory solutions. In general, a larger test sample size yields better results. However, owing to the limited availability of physical resources, there is a limit on the test sample size that can be used. In this paper, we address this problem by proposing a self-adaptive reliability testing scheme to decide the sample size for effective long-term reliability testing.

Comparison of wearable activity tracker with actigraphy for sleep evaluation and circadian rest-activity rhythm measurement in healthy young adults

Objective The purpose of this study was to evaluate the applicability of data obtained from a wearable activity tracker (Fitbit Charge HR) to medical research. This was performed by comparing the wearable activity tracker (Fitbit Charge HR) with actigraphy (Actiwatch 2) for sleep evaluation and circadian rest-activity rhythm measurement. Methods Sixteen healthy young adults (female participants, 62.5%; mean age, 22.8 years) wore the Fitbit Charge HR and the Actiwatch 2 on the same wrist; a sleep log was recorded over a 14-day period. We compared the sleep variables and circadian rest-activity rhythm measures with Wilcoxon signed-rank tests and Spearmans correlations. Results The periods and acrophases of the circadian rest-activity rhythms and the sleep start times did not differ and correlated significantly between the Fitbit Charge HR and the Actiwatch 2. The Fitbit Charge HR tended to overestimate the sleep durations compared with the Actiwatch 2. However, the sleep durations showed high correlation between the two devices for all days. Conclusion We found that the Fitbit Charge HR showed high accuracy in sleep evaluation and circadian rest-activity rhythm measurement when compared with actigraphy for healthy young adults. The results suggest that the Fitbit Charge HR could be applicable on medical research as an alternative tool to actigraphy for sleep evaluation and measurement of the circadian rest-activity rhythm.

Developer Micro Interaction Metrics for Software Defect Prediction

To facilitate software quality assurance, defect prediction metrics, such as source code metrics, change churns, and the number of previous defects, have been actively studied. Despite the common understanding that developer behavioral interaction patterns can affect software quality, these widely used defect prediction metrics do not consider developer behavior. We therefore propose micro interaction metrics (MIMs), which are metrics that leverage developer interaction information. The developer interactions, such as file editing and browsing events in task sessions, are captured and stored as information by Mylyn, an Eclipse plug-in. Our experimental evaluation demonstrates that MIMs significantly improve overall defect prediction accuracy when combined with existing software measures, perform well in a cost-effective manner, and provide intuitive feedback that enables developers to recognize their own inefficient behaviors during software development.

A probabilistic approach to estimate the damage propagation of cyber attacks

With rapid development in the Internet technology, business management in an organization becomes dependent on network dependency and cohesiveness in a critical information and communications infrastructure. However, the occurrence of cyber attacks has increased, targeted against vulnerable resources in information systems. Hence, in order to protect private information and computer resources, risk analysis and damage propagation need to be studied. However, the existing models present mechanisms for risk management, and these models can only be applied to specified threats such as a virus or a worm. Therefore, a probabilistic model for damage propagation based on Markov process is proposed, which can be applied to diverse threats in information systems. The proposed model enables us to predict the occurrence probability and occurrence frequency of each threat in the information systems.

Security risk vector for quantitative asset assessment

There are standard risk analysis methodologies like GMITS and ISO17799, but new threats and vulnerabilities appear day by day because the IT organizations, its infrastructure, and its environment are changing. Accordingly, the methodologies must evolve in step with the change. Risk analysis methods are generally composed of asset identification, vulnerability analysis, safeguard identification, risk mitigation, and safeguard implementation. As the first process, the asset identification is important because the target scope of risk analysis is defined. This paper proposes a new approach, security risk vector, for evaluating assets quantitatively. A case study is presented.

A seamless lawful interception architecture for mobile users in IEEE 802.16e networks

Lawful interception (LI) involves legally accessing private communication such as telephone calls or email messages. Numerous countries have been drafting and enacting laws concerning the LI procedures. With the proliferation of portable Internet services such as the IEEE 802.16e wireless mobile networks, surveillance over illegal users is an emerging technical issue in LI. The ever-migrating users and their changing IPs make it harder to provide support for seamless LI procedures on 802.16e networks. Few studies, however, on seamless LI support have been conducted on the 802.16e mobile networks environments. Proposed in this paper are a seamless LI architecture and algorithms for the 802.16e networks. The simulation results demonstrate that the proposed architecture improves recall rates in intercepting mobile user, when compared to the existing LI architectures.