In case of a power failure, the measurement module can control the power supply to the router and the communication module to conserve battery power. It connects to the Data Management Center (DMC) using an ISDN line. The communication module can perform sophisticated processes, such as calculation of the Japan Meteorological Agency (JMA) seismic intensity, continuous recording of data and near real-time data transmission. The measurement module functions as a conventional strong-motion seismograph for high-precision observation. A triaxial accelerometer, FBA-ES-DECK (Kinemetrics Inc.) is built into the sensor module.

A UPS, a GPS antenna and a dial-up router are also installed together with a K- NET02. The new seismograph (K- NET02) consists of a sensor module, a measurement module and a communication module. The new system went into operation in June 2004, although seismographs have still to be replaced in other areas. New seismographs have been installed at 443 observatories mainly in southwestern Japan where there is a risk of strong-motion due to the Nankai and Tonankai earthquakes. As technology has progressed and new technologies have become available, NIED has developed a new K- NET with improved functionality. Thus, eight years have passed since K- NET started, and large amounts of strong-motion records have been obtained. K- NET was constructed after the Hyogoken-Nambu (Kobe) earthquake in January 1995, and began operation in June 1996. The National Research Institute for Earth Science and Disaster Prevention (NIED) operates K- NET (Kyoshin Network), the national strong-motion observation network, which evenly covers the whole of Japan at intervals of 25 km on average. Renewal of K- NET (National Strong-motion Observation Network of Japan) We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery). This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better.

In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Unifying Inference of Meso-Scale Structures in Networks.

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