The goal of the present experiments was to analyse the binding of oxytocin (OT) and vasopressin (VP) in the hypothalamo-neurohypophyseal system to determine whether [(3)H]OT and [(3)H]VP binding in this system involved interaction with receptor sites or with neurophysins. Using quantitative autoradiography, several experiments were performed to compare [(3)H]OT- and [(3)H]VP-binding characteristics in this system and in brain areas containing identified receptor sites. Saturation experiments indicated much lower affinity of [(3)H]OT and [(3)H]VP binding in the magnocellular nuclei and neural lobe than on brain receptors. Competition experiments using selective ligands indicated interaction with neurophysins rather than with receptors in the hypothalamo-neurohypophyseal system. This system was never labelled in the presence of a [(125)I]OT antagonist, a selective OT receptor ligand. In contrast with receptors elsewhere in the brain, the magnocellular nuclei were labelled by [(3)H]OT and [(3)H]VP in the absence of MgCI(2). In the pituitary neural lobe, density of binding sites was moreover obviously related to the amount of neurosecretory granules, as seen in acutely dehydrated rats. Taken together, these data strongly suggest that in the hypothalamo-neurohypophyseal system [(3)H]OT and [(3)H]VP bind to neurophysins rather than to specific receptors.

In 1746, Lima Region was hit by a severe earthquake and a consecutive tsunami in Callao City caused 96% of casualties in the Callao City population. Under SATREPS Project [1], several studies were realized, and they concluded that a severe earthquake (Mw8.6~8.9) may occur in Lima City [2], following by tsunami which may hit a large coastal area. In that sense, harmful scenarios can occur. Based on last studies, and historical earthquake consequences in Callao City; Local government in La Punta, the most tsunami prone district in Callao, has designated 19 reinforced concrete (RC) buildings as tsunami shelters. Nevertheless, the lack of the structural vulnerability studies of these buildings in front of an earthquake and consecutive tsunami scenario, makes uncertain the good performance of the buildings. Guidelines of other countries such as Japan, The United States and Chile, are oriented to calculate the tsunami forces; nevertheless, these guidelines lack information about structural performance of buildings in front of earthquake-tsunami scenarios. This paper describes a methodology to assess the sequential action of the earthquake and the consecutive tsunami to evaluate the structural performance and the damage level to ensure the safety of buildings and their inhabitants.

Traditional tsunami vulnerability assessments often rely on empirical models and field surveys, which can be time-consuming and have limited accuracy. In this study, we propose a novel approach that integrates high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry with numerical simulation to improve vulnerability assessment efficacy in Ancon Bay, Lima, Peru, by using the Papathoma Tsunami Vulnerability Assessment (PTVA-4) model. For this purpose, a detailed 3D representation of the study area was generated using UAV-based oblique photogrammetry, enabling the extraction of building attributes. Additionally, a high-resolution numerical tsunami simulation was conducted using the TUNAMI-N2 model for a potential worst-case scenario that may affect the Central Peru subduction zone, incorporating topographic and land-use data obtained with UAV-based nadir photogrammetry. The results indicate that the northern region of Ancon Bay exhibits higher relative vulnerability levels due to greater inundation depths and more tsunami-prone building attributes. UAV-based assessments provide a rapid and detailed method for evaluating building vulnerability. These findings indicate that the proposed methodology is a valuable tool for supporting coastal risk planning and disaster preparedness in tsunami-prone areas.

Peru faces a significant tsunami hazard due to its location along the Pacific Ring of Fire. Historical megathrust earthquakes and their resulting tsunamis have caused severe damage, highlighting the need for improved warning systems. This study investigates potential tsunami impacts along the central Peruvian coast—including the regions of Ica, Callao, Lima, and Ancash—using numerical simulations. To enable rapid and efficient simulations, we developed gWave-CPU, a parallelized version of the TUNAMI-N2 model created at Tohoku University. Using 90-m-resolution topographic and bathymetric data in combination with census data, we assessed population exposure under two seismic scenarios: a plausible event based on interseismic coupling and a historical scenario simulating the 1746 tsunami. Under the historical scenario, the exposed population was estimated at 320,128, with the highest concentrations in Callao and Lima. Numerical simulations of four hours of tsunami propagation and inundation were conducted using our parallelized implementation, reducing computation time to 68 minutes—a 26.3-fold speedup compared to the conventional model. The results demonstrate that tsunami inundation and population exposure in this region can be efficiently estimated using the proposed approach, providing a valuable contribution to tsunami hazard assessment, management, and emergency preparedness along Peru’s central coast.

To estimate the impact of earthquakes in urban areas, researchers conventionally rely on simplified models of urban structures for simulations. However, recent advancements in technology enable more accurate earthquake response predictions through high-fidelity models and digital twins. Conventional mesh generation programs can produce general 3D models but are not optimized for large-scale urban simulations, where mesh refinement near material interfaces is essential. In this study, we developed an efficient Delaunay triangulation-based method for city-scale simulations by partitioning the 3D model into subdomains. The proposed method seamlessly uses information from digital elevation model and seismic velocity model. Using the proposed method on a shared-memory machine with 64 parallel processes, we generated a high-fidelity 3D urban model with tetrahedral elements and degrees of freedom (DOF) in 4.3 minutes. To demonstrate the applicability of the generated model, we conducted a large-scale wave propagation simulation using gQuake software, completing the computation in just 11.5 minutes. Results show that the proposed method significantly reduces mesh generation time for complex urban geometries, enhancing the efficiency of large-scale earthquake simulations.