A new numerical method for a class of Volterra and Fredholm integral equations

In the present work, we introduce a new numerical method based on a strong version of the mean-value theorem for integrals to solve quadratic Volterra integral equations and Fredholm integral equations of the second kind, for which there are theoretical monotonic non-negative solutions. By means of an equality theorem, the integral that appears in the aforementioned equations is transformed into one that enables a more accurate numerical solution with fewer calculations than other previously described methods. Convergence analysis is given

PREDICTION OF DEFORMATION CAUSED BY LANDSLIDES BASED ON GRAPH CONVOLUTION NETWORKS ALGORITHM AND DINSAR TECHNIQUE

Abstract. Around the world, the occurrence of landslides has become one of the greatest threats to human life, property, infrastructure, and natural environments. Despite extensive research and discussions on the spatiotemporal dependence of landslide displacements, there is still a lack of understanding concerning the factors that appear to control displacement distribution in landslides because of their significant variations. This paper implements a Graph Convolutional Network (GCN) to predict displacement following the Moio della Civitella landslide in southern Italy and identify factors that may affect the distribution of movement following the landslide. An interferometric technique, known as permanent scatter interferometry (PSI), has been developed based on Synthetic Aperture Radar (SAR) satellite imagery to derive permanent scatter points that can be used to represent the deformation of landslides. This study utilized the GCN regression model applied to PSs points and data reflecting geological and geomorphological factors to extract the interdependency between paired data points, resulting in an adjacency matrix of the interval [0, 0,8). The proposed model outperforms conventional machine learning and deep learning algorithms such as linear regression (LR), K-nearest neighbors (KNN), Support vector regression (SVR), Decision tree, lasso, and artificial neural network (ANN). The absolute error between the actual and predicted deformation is used to evaluate the proposed model, which is less than 2 millimeters for most test set points

Autologous Hematopoietic Stem Cell Transplantation (AHSCT): Standard of Care for Relapsing–Remitting Multiple Sclerosis Patients

Abstract Autologous hematopoietic stem cell transplantation (AHSCT) has been used in the treatment of highly active multiple sclerosis (MS) for over two decades. It has been demonstrated to be highly efficacious in relapsing–remitting (RR) MS patients failing to respond to disease-modifying drugs (DMDs). AHSCT guarantees higher rates of no evidence of disease activity (NEDA) than those achieved with any other DMDs, but it is also associated with greater short-term risks which have limited its use. In the 2019 updated EBMT and ASBMT guidelines, which review the clinical evidence of AHSCT in MS, AHSCT indication for highly active RRMS has changed from “clinical option” to “standard of care”. On this basis, AHSCT must be proposed on equal footing with second-line DMDs to patients with highly active RRMS, instead of being considered as a last resort after failure of all available treatments. The decision-making process requires a close collaboration between transplant hematologists and neurologists and a full discussion of risk–benefit of AHSCT and alternative treatments. In this context, we propose a standardized protocol for decision-making and informed consent process

Rockfall threatening cumae archeological site fruition (Phlegraean fields park—naples)

Natural hazards threaten many archaeological sites in the world; therefore, susceptibility analysis is essential to reduce their impacts and support site fruition by visitors. In this paper, rockfall susceptibility analysis of the western slope of the Cumae Mount in the Cumae Archaeological Site (Phlegraean Fields, Naples), already affected by rockfall events, is described as support to a management plan for fruition and site conservation. Being the first Greek settlement in southern Italy, the site has great historical importance and offers unique historical elements such as the Cumaean Sibyl’s Cave. The analysis began with a 3D modeling of the slope through digital terrestrial photogrammetry, which forms a basis for a geomechanical analysis. Digital discontinuity measurements and cluster analysis provide data for kinematic analysis, which pointed out the planar, wedge and toppling failure potential. Subsequently, a propagation-based susceptibility analysis was completed into a GIS environment: it shows that most of the western sector of the site is susceptible to rockfall, including the access course, a segment of the Cumana Railroad and its local station. The work highlights the need for specific mitigation measures to increase visitor safety and the efficacy of filed-based digital reconstruction to support susceptibility analysis in rockfall prone areas