The aim of the present study was to determine whether the ambulance stations in the provincial centre of Samsun, Turkey, were able to cover their entire operational area within 10 mins of receiving an emergency call.\n\nMETHODS This study was based on emergency TPCA-1 inhibitor calls received by the emergency medical services of the study area. Detailed address data from the calls was used to produce thematic maps using the geographic information system (GIS). Buffer analysis was used to determine the adequacy of the stations’ locations in relation to the time taken to respond to the emergency calls.\n\nRESULTS In the study area, there were a total of 11,506 emergency ambulance

calls made in 2009, which revealed a call density of 0.7 calls per ha and 23.8 calls per 1,000 population. A total of 75.8% of the calls were made due to medical reasons, while 11.6% were related to traffic accidents. The GIS-based investigation revealed that the 10-min coverage areas for the four ambulance stations in the provincial centre of Samsun served 76.9% of the area and 97.9% of its population. Of the 10,380 calls for which detailed address data were available, 99.2% were within the stations’ 10-min coverage areas.\n\nCONCLUSION According to the buffer

analysis, the ambulance stations in the provincial centre of Samsun are able to reach 97.9% of the population within the critical 10-min response WH-4-023 Angiogenesis inhibitor time. This study demonstrates that GIS is an indispensable tool for processing and analysing spatial data, which can in turn aid decision-making in the field of geographical epidemiology and public health.”
“This paper proposes a novel method for modelling magneto-rheological (MR) dampers. It uses an elementary hysteresis model (EHM) with a feed-forward neural network (FNN) to capture hysteresis characteristics of an MR damper, and

another FNN to determine the current gain. These parts can be trained separately, thus reducing the size of the training dataset. The inputs of the proposed model include learn more velocity, acceleration, and current to estimate the generated damping force. Unlike previous FNN models, this model does not require force sensor inputs. Simulation results show the high performance of the proposed EHM-based FNN when compared to conventional methods such as a recurrent neural network.”
“A recently proposed model, based on the relative occupancy of tetrahedral and octahedral sites by different cations, was used to study the magnetocrystalline anisotropy of mixed Ni-Zn ferrite nanoparticles. According to this model, the total magnetocrystalline anisotropy is the weighted average of the contributions of the anisotropies of Fe3+ and M2+ ions in A and B sites. The model predictions are confirmed in the case of nickel zinc ferrite. (C) 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved.”
“Cerebral venous sinus thrombosis (CVST) is a rare and potentially life-threatening cause of stroke.