Service quality factors affecting customer attitudes in online-to-offline commerce

Information Systems and e-Business Management - In an online-to-offline (O2O) environment, a company operates multiple channels: online, mobile, and offline. A company attracts customers and...     

Risk assessment of polycyclic aromatic hydrocarbons in meat and edible oils: results of a total diet study in South Korea

Food Science and Biotechnology - Polycyclic aromatic hydrocarbons (PAHs) constitute carcinogens. In this study, the risk of PAHs being consumed through meat and edible oils was assessed using a...     

1,3,5,8-Tetrahydroxyxanthone suppressed adipogenesis via activating Hedgehog signaling in 3T3-L1 adipocytes

Food Science and Biotechnology - In this study, we investigated the effect of 1,3,5,8-tetrahydroxyxanthone (THX) on the adipogenesis of 3T3-L1 adipocytes. THX, a xanthone isolated from Gentianella...     

A semantic approach to improving machine readability of a large-scale attack graph

Automation in cyber security can be achieved by using attack graphs. Attack graphs allow us to model possible paths that a potential attacker can use to intrude into a target network. In particular, graph representation is often used to increase visibility of information, but it is not effective when a large-scale attack graph is produced. However, it is inevitable that such a voluminous attack graph is generated by modeling a variety of data from an increasing number of network hosts. Therefore, we need more intelligent ways of inferring the knowledge required to harden network security from the attack graph, beyond getting information such as possible attack paths. Ontology technology enables a machine to understand information and makes it easier to infer knowledge based on relational facts from big data. Constructing ontology in the domain of attack graph generation is a prerequisite for increasing machine intelligence and implementing an automated process. In this paper, we propose a semantic approach to make a large-scale attack graph machine readable. The approach provides several benefits. First, users can obtain relational facts based on reasoning from a large-scale attack graph, and the semantics of an attack graph can provide intuition to users. In addition, intelligence-based security assessment can be possible using the obtained ontological structures. By improving the machine readability of an attack graph, our approach could lead to automated assessment of network security.

     

Development of a web-based emergency preparedness plan system in Korea

The chemical industry treats a huge quantity of hazardous and harmful flammable liquids, combustible gases and toxic materials. Therefore, there is a high potential for fires, conflagrations, explosions and toxic leaks. To minimize the possibility of such hazards, the Korean government has been enforcing an EPP (Emergency Preparedness Plan) in accordance with the Toxic Chemicals Controls Act since 1996. However, 70% of the targeted small and medium-sized enterprises are struggling with the independent implementation of EPP, and 30% of the EPP grades being used are not amenable to approval and further application. As a result, we have developed a web-based emergency preparedness plan system. The main purpose of the program is to provide a safety management system to each facility in order to enable accidents to be prevented and accidents to be immediately controlled. The program also helps government or related agencies to control a number of accidents that occur in small companies throughout the entire country.     

Hydrogen Isotope Separation in Confined Nanospaces: Carbons,Zeolites, Metal–Organic Frameworks,and Covalent Organic Frameworks

One of the greatest challenges of modern separation technology is separating isotope mixtures in high purity. The separation of hydrogen isotopes can create immense economic value by producing valuable deuterium (D) and tritium (T), which are irreplaceable for various industrial and scientific applications. However, current separation methods suffer from low separation efficiency owing to the similar chemical properties of isotopes; thus, high‐purity isotopes are not easily achieved. Recently, nanoporous materials have been proposed as promising candidates and are supported by a newly proposed separation mechanism, i.e., quantum effects. Herein, the fundamentals of the quantum sieving effect of hydrogen isotopes in nanoporous materials are discussed, which are mainly kinetic quantum sieving and chemical‐affinity quantum sieving, including the recent advances in the analytical techniques. As examples of nanoporous materials, carbons, zeolites, metal–organic frameworks, and covalent organic frameworks are addressed from computational and experimental standpoints. Understanding the quantum sieving effect in nanospaces and the tailoring of porous materials based on it will open up new opportunities to develop a highly efficient and advanced isotope separation systems.