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In this paper, we propose a location-dependent earthquake prediction based on recurrent neural network algorithms. The location-dependent prediction consists of clustering the seismic dataset based on its geographical parameters (longitude and latitude) using the K-Means algorithm. In addition, we divide each cluster into two specific subsets. The first set consists of events with an average magnitude between 2 and 5, and the second set contains seismic events with magnitudes >5. Clustering allows our models to focus on each region independently. Associated with splitting methods, it helps them to learn the specific trends of each cluster with accurate performance. Moreover, in our work, the large earthquakes with few events are trained independently and remain unaffected by the other dominant events. We implement our models using the powerful recurrent neural network algorithms: Long Short Memory (LSTM), Gated Recurrent Network (GRU), and their hybrid model (LSTM-GRU). They are tested using data related to three different regions: Morocco, Japan, and Turkey. We evaluate the performance according to three metrics: mean absolute error, mean squared error, and root-mean-square error. Compared to other works in the literature, our models generally perform well, especially in predicting large earthquakes. - A location-dependent prediction using a geographic clustering by K-means algorithm. - Earthquake prediction using recurrent neural network algorithms: LSTM and GRU. - Three different seismic regions are studied: Japan, Turkey, and Morocco. - Discussion and comparison of results. [ABSTRACT FROM AUTHOR]

Abstract: Traffic optimization of railroad networks was considered using an algorithm that was biologically inspired by an amoeba-like organism, plasmodium of the true slime mold, Physarum polycephalum. The organism developed a transportation network consisting of a tubular structure to transport protoplasm. It was reported that plasmodium can find the shortest path interconnecting multiple food sites during an adaptation process (Nakagaki et al., 2001. Biophys. Chem. 92, 47–52). By mimicking the adaptation process a path finding algorithm was developed by . In this paper, the algorithm is newly modified for applications of traffic distribution optimization in transportation networks of infrastructure such as railroads under the constraint that the network topology is given. Application of the algorithm to a railroad in metropolitan Tokyo, Japan is demonstrated. The results are evaluated using three performance functions related to cost, traveling efficiency, and network weakness. The traffic distribution suggests that the modified Physarum algorithm balances the performances under a certain parameter range, indicating a biological process. [Copyright &y& Elsevier]

Abstract: PHITS (Particle and Heavy-Ion Transport code System) is a general-purpose three-dimensional Monte Carlo code, developed and maintained by RIST, JAEA and KEK in Japan together with Sihver et al. at Chalmers in Sweden. PHITS can deal with the transports of all varieties of hadrons and heavy ions with energies up to around 100GeV/nucleon, and in this paper the current status of PHITS is presented. We introduce a relativistically covariant version of JQMD, called R-JQMD, that features an improved ground state initialization algorithm, and we will present the introduction of electron and photon transport in PHITS using EGS5, which have increased the energy region for the photon and energy transport from up to around 3GeV to up to several hundred GeV depending on the atomic number of the target. We show how the accuracy in dose and fluence calculations can be improved by using tabulated cross sections. Benchmarking of shielding and irradiation effects of high energy protons in different materials relevant for shielding of accelerator facilities is also presented. In particular, we show that PHITS can be used for estimating the dose received by aircrews and personnel in space. In recent years, many countries have issued regulations or recommendations to set annual dose limitations for aircrews. Since estimation of cosmic-ray spectra in the atmosphere is an essential issue for the evaluation of aviation doses, we have calculated these spectra using PHITS. The accuracy of the atmospheric propagation simulation of cosmic-ray performed by PHITS has been well verified by experimental cosmic-ray spectra taken under various conditions. Based on a comprehensive analysis of the simulation results, an analytical model called “PARMA” has been proposed for instantaneously estimating the atmospheric cosmic-ray spectra below the altitude of 20km. We have also performed preliminary simulations of long-term dose distribution measurements at the ISS performed with the joint ESA-FSA experiment MATROSHKA-R (MTR-R) led by the Russian Federation Institute of Biomedical Problems (IMBP) and the ESA supported experiment MATROSHKA (MTR), led by the German Aerospace Center (DLR). For the purpose of examining the applicability of PHITS to the shielding design in space, the absorbed doses in a tissue equivalent water phantom inside an imaginary space vessel has been estimated for different shielding materials of different thicknesses. The results confirm previous results which indicate that PHITS is a suitable tool when performing shielding design studies of spacecrafts. [Copyright &y& Elsevier]

Abstract: A ladder lottery, known as “Amidakuji” in Japan, is a common way to choose a permutation randomly. A ladder lottery corresponding to a given permutation is optimal if has the minimum number of horizontal lines among the ladder lotteries corresponding to . In this paper we show that for any two optimal ladder lotteries and of a permutation, there exists a sequence of local modifications which transforms into . We also give an algorithm to enumerate all optimal ladder lotteries of a given permutation. By setting , the algorithm enumerates all arrangements of pseudolines efficiently. By implementing the algorithm we compute the number of arrangements of pseudolines for each . [Copyright &y& Elsevier]

Abstract: The phenomenon of earthquake clustering, i.e., the increase of occurrence probability for seismic events close in space and time to other previous earthquakes, has been modeled both by statistical and physical processes. From a statistical viewpoint the so-called epidemic model (ETAS) introduced by Ogata in 1988 and its variations have become fairly well known in the seismological community. Tests on real seismicity and comparison with a plain time-independent Poissonian model through likelihood-based methods have reliably proved their validity. On the other hand, in the last decade many papers have been published on the so-called Coulomb stress change principle, based on the theory of elasticity, showing qualitatively that an increase of the Coulomb stress in a given area is usually associated with an increase of seismic activity. More specifically, the rate-and-state theory developed by Dieterich in the ′90s has been able to give a physical justification to the phenomenon known as Omori law. According to this law, a mainshock is followed by a series of aftershocks whose frequency decreases in time as an inverse power law. In this study we give an outline of the above-mentioned stochastic and physical models, and build up an approach by which these models can be merged in a single algorithm and statistically tested. The application to the seismicity of Japan from 1970 to 2003 shows that the new model incorporating the physical concept of the rate-and-state theory performs not worse than the purely stochastic model with two free parameters only. The numerical results obtained in these applications are related to physical characters of the model as the stress change produced by an earthquake close to its edges and to the A and σ parameters of the rate-and-state constitutive law. [Copyright &y& Elsevier]