The construction technique of raw earth, which has always been in use in most of the world, has left large monuments or architectural complexes to cultural heritage that need special attention due to the notable vulnerability of the material. A convenient way to deal this threat, besides physical intervention, is by using an information system, such as HBIM (Heritage Building Information Modeling), as a tool for damage assessment and conservation planning. This paper reports on its application in an archaeological setting, in particular, on the Huaca Arco Iris, a religious building of the old city of Chan Chan (Peru), the largest monumental complex in mud on the American continent. The study is part of the bilateral international project between the Consiglio Nazionale delle Ricerche (CNR) and the Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC) in the use of HBIM for the prediction of possible natural or anthropogenic damages to buildings in raw mud. Exploiting the data coming from the direct and indirect analyses, a dedicated ontology is built to guide the management of these data within the information system. The creation of an HBIM system for the archaeological domain, based on the trinomial data–information–knowledge, is presented and validated. Following this approach, a customizable HBIM has been created with the 3D model of the spatial entities of the Huaca. As a result, the semantic relationship of an external wall, taken as the benchmark test of our experiment, with the contained bas-relief and the conservation cover is tested.

The amount of damage to cultural heritage sites is increasing rapidly every year. This is due to inadequate heritage management and uncontrolled urban growth as well as unpredictable seismic and atmospheric events that manifest themselves in a continuously deteriorating ecosystem. Thus, applications of artificial intelligence (AI) in remote-sensing (RS) techniques (machine-learning and deep-learning algorithms) for monitoring archaeological sites have increased in recent years. This research involves the surrounding area of the archaeological site of Chan Chan in Peru in particular. An approach that is based on the use of AI algorithms for building footprint segmentation and changedetection analysis by means of RS images is proposed. It involves a UNet convolutional network based on an EfficientNet B0 to B7 encoder. The network was trained on two public data sets from SpaceNet that were based on WV2 and WV3 satellite images: SpaceNet V1 (Rio), and SpaceNet V2 (Shanghai). In the pre-processing phase, the images from the two data sets have been equalized in order to improve their quality and avoid overfitting. The building segmentation has been performed on HRV images of the study area that were downloaded from Google Earth Pro. The value that was achieved in the IoU metric was around 70% in both experiments. The purpose of this proposed methodology is to assist scientists in drafting monitoring and conservation protocols based on already-recorded data in order to prevent future disasters and hazards.

Background The global rise in cases of acute myocardial infarction (AMI) poses a significant health challenge, which is why adult stem cells have gained great importance in recent years, due to their potential to promote the regeneration of the flowcardiac tissue, among which multipotent mesenchymal stromal cells (MSCs) stand out, thanks to their clinical usefulness. Objectives To evaluate the effect of the dose, timing, and route of administration of MSCs on their regenerative capacity after MI. Methods We searched for randomized clinical trials and experimental studies published up to April 25, 2024, in Medline (PubMed) and Scopus. Results Nine clinical studies were included in the qualitative assessment. The main routes of application were coronary, intramyocardially, epicardial topical and systemic venous perfusion, which could have clinical effectiveness with doses of 1x106 MSC/Kg,1 to 3 x 106, 4x106 and those greater than 0.5x106, respectively. The median number of viable cells administered was 2.4x106 (IQR: 1.6-2.4) in the PCI group versus 1.6x106 in the RCVI group (p=0.167). Median ex vivo retention was 2.55% in the RCVI group, 30 days after AMI, and 39.40% in the PCI group. At 4 and 12 months of follow-up, a better left ventricular end-to-end (LVEF) was observed in the group treated with ADSCs, at 4 (51.8% ±5.4% vs. 35.5% ±1.9%) and 8 weeks (52.1%± 3.4% versus 34.2% ±4.7%, p = 0.048). Conclusions The dosing, timing of administration, and routes of administration were important factors to assess the efficacy of the MSC.