Linking Economic Model and Engineering Model: Application of Sequential Interindustry Model (SIM)

A conventional approach to model the regional economic impacts of a catastrophic disaster has been to employ the results from an engineering model, such as lifeline network model, in an economic model, for example input-output framework or computable general equilibrium model. However, due to the differences in modeling scheme between economic and engineering models, this type of data feed creates problems regarding sensitivity and dynamics of the impacts. In this paper, Sequential Interindustry Model (SIM) is used to disaggregate the process of production chronology to become more sensitive to the changes/damages of economic activities under a disaster situation. SIM is particularly useful to simulate the dynamic processes of impact propagation and of structural changes after a catastrophic disaster. In this paper, the issues and applications of SIM are discussed with numerical examples

Country-Specific Experience, Host Country Government Corruption, And Outward Foreign Direct Investment By Korean Textile Firms

This study explores how organizational learning and host country government corruption influenced outward foreign direct investment by Korean textile firms between 1986 and 1995, given that foreign direct investment decisions are not made in isolation from these two factors. The results provide empirical evidence for the recent organizational learning theory argument that the longevity of country-specific experience has a curvilinear relationship with foreign direct investment. In addition, the results suggest that the positive relationship between host country government corruption and foreign direct investment is moderated by the decreasing effect of host country–specific experience over time, suggesting that a dynamic framework of organizational learning could be used to cautiously qualify the existing bifurcated positive versus negative conclusion about the government corruption–foreign direct investment relationship

How Do Corporate Social Responsibility Activities Influence Corporate Reputation? Evidence From Korean Firms

This study applies the stakeholder theory and explores how corporate social responsibility (CSR) activities are associated with corporate reputation. In particular, we investigate how charitable contributions and toxic emissions as proxies for CSR activities are related with shareholder returns as a proxy for corporate reputation. This study employs a sample of Korean firms reported in the Korea sustainability index database between 2010 and 2012. The results find that charitable contributions and toxic emissions are positively and negatively related with shareholder returns, respectively. In addition, the results show that current shareholder returns are likely to be influenced by past charitable contributions and toxic emissions, suggesting that firms may develop current reputational competences by leveraging on past CSR activities

A degree reduction method for an efficient QUBO formulation for the graph coloring problem

We introduce a new degree reduction method for homogeneous symmetric polynomials on binary variables that generalizes the conventional degree reduction methods on monomials introduced by Freedman and Ishikawa. We also design an degree reduction algorithm for general polynomials on binary variables, simulated on the graph coloring problem for random graphs, and compared the results with the conventional methods. The simulated results show that our new method produces reduced quadratic polynomials that contains less variables than the reduced quadratic polynomials produced by the conventional methods

AVQS: Attack Route-Based Vulnerability Quantification Scheme for Smart Grid

A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification

Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records

Aggressive driving behavior (ADB) is a major cause of traffic accidents. As ADB is controllable, ADB-based driving risk assessment is an effective method for drivers and transportation companies to ensure driving safety. Conventionally, the relationships between ADBs and accident-related records are analyzed when assessing driving risk. However, such records typically overlook driver responsibility for driving risks and depend considerably on the person producing the data (e.g., police officers or insurance managers). Foremost, conventional approaches do not consider non-accident situations that comprise most driving scenarios. Thus, we propose a novel driving risk assessment method that uses only ADB data. In this method, interpretable latent risk factors are extracted from ADB data via sparse non-negative matrix factorization (NMF), and then the driving risk score is computed on a scale of 0-100. The proposed method was validated by adopting a real-world application to assess the driving risk of bus drivers in South Korea and by conducting an evaluation performed by transportation experts in conjunction with the Korea Transportation Safety Authority. Results revealed that the proposed method can discriminate between high-and low-risk driving, thus providing clear guidelines to improve driving. Then, the proposed driving risk score assessment method using NMF was compared with existing machine learning-based risk assessment methods. The proposed method outperformed the conventional methods in terms of driving risk discrimination and interpretability. This study can provide risk assessment guidelines based on driving behavior records and contribute to the application of machine learning in transportation safety management

Cerebral microbleeds are associated with nocturnal reverse dipping in hypertensive patients with ischemic stroke

Background Abnormalities in nocturnal blood pressure dipping are well known for its relationship to cardiovascular diseases. Cerebral microbleeds are frequently observed in patients with hypertension and are known to be potent risk factors for stroke. However, there are scanty reports about the relationship between nocturnal dipping and cerebral microbleeds. Methods We recruited consecutive patients with both hypertension and ischemic stroke within 7 days after symptom onset, and those with cardioembolism were excluded. We applied 24-hour ambulatory blood pressure monitoring two weeks after stroke onset, and we used brain MRI to detect cerebral microbleeds. Various blood pressure parameters such as mean 24-hour blood pressure, awake/sleep blood pressure, and morning surge were compared between cerebral microbleeds (+) vs. (-) groups. Subjects were further classified according to nocturnal dipping status and were analyzed by logistic regression to determine its association with cerebral microbleeds with adjustment for age, gender, and cardiovascular risk factors. Results A total of 162 patients (100 males, age 65.33 ± 10.32 years) were included. Cerebral microbleeds were detected in 65 patients (40.1%). Most ambulatory blood pressure parameters except morning surge were significantly higher in those who had cerebral microbleeds. After adjusting for the confounding factors, the reverse dippers were prone to have cerebral microbleeds (odds ratio, 3.81; 95% confidential interval, 1.36-10.65; p-value = 0.01). Conclusion Cerebral microbleeds are independently associated with reverse dipping on ambulatory blood pressure monitoring in hypertensive stroke patients.This study was supported by a grant from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea. (A101311)Peer Reviewe

A sleep-like state in Hydra unravels conserved sleep mechanisms during the evolutionary development of the central nervous system

Sleep behaviors are observed even in nematodes and arthropods, yet little is known about how sleep-regulatory mechanisms have emerged during evolution. Here, we report a sleep-like state in the cnidarian Hydra vulgaris with a primitive nervous organization. Hydra sleep was shaped by homeostasis and necessary for cell proliferation, but it lacked free-running circadian rhythms. Instead, we detected 4-hour rhythms that might be generated by ultradian oscillators underlying Hydra sleep. Microarray analysis in sleep-deprived Hydra revealed sleep-dependent expression of 212 genes, including cGMP-dependent protein kinase 1 (PRKG1) and ornithine aminotransferase. Sleep-promoting effects of melatonin, GABA, and PRKG1 were conserved in Hydra. However, arousing dopamine unexpectedly induced Hydra sleep. Opposing effects of ornithine metabolism on sleep were also evident between Hydra and Drosophila, suggesting the evolutionary switch of their sleep-regulatory functions. Thus, sleep-relevant physiology and sleep-regulatory components may have already been acquired at molecular levels in a brain-less metazoan phylum and reprogrammed accordingly