<p>The Eufrasius Cathedral of Poreč in Istria Peninsula, Croatia, was built in the 6th century, The nave collapsed in parts due to the AD 1440 earthquake. Nave and aisles are supported by 18 monolithic columns of Proconnesian marble. Seventeen of the columns bear various fractures, forming two groups: (1) axis-parallel fractures and (2) oblique fractures. Azimuths of dip directions of oblique fractures indicate N-S shaking.</p><p>In this study, the fracture development and cracking of a stone column was modelled using computer code. To model the current fracture pattern and to link it to seismic activity a Lagrangian analysis of continua in three dimensions (FLAC3D) is employed to reveal the non-linear behaviour of the stone column. A 3-Dimensional model based on discrete-element-method (DEM) has been created to study the failure process of the ancient stone column under static and dynamic loads. A combination of vertical and horizontal loads with a dynamic load due to the earthquake has been imposed horizontally. The influence of different parameters such as mechanical properties of rock, the magnitude of the earthquake were also assessed to observe their influence on the failure mechanism of rock. The DEM model was able to describe the observed crack pattern and it has proved the applicability of FLAC3D to describe failure mechanism of stone columns.</p>

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There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of data from web-based (point-and-click) resources or graphical user interfaces that cannot be accessed using command-line tools. The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides (AMP) sequences (46240 hits) from various MARVIN software panels, which can be later utilized to develop novel AMPs. Furthermore, for machine learning research, the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank. As a result, data collection from the web will become faster and less expensive, with no need for manual data extraction. The software is critical as a first step to preparing large datasets for subsequent stages of analysis, such as those using machine and deep-learning applications.