An Electric Model of Cracked Solar Cells Accounting for Distributed Damage Caused by Crack Interaction

AbstractElectric models based on a distributed series resistance along the grid line can be used to predict the current through the thickness of the Silicon solar cell, as well as the current and the voltage along the grid line. In the presence of a crack intersecting a finger, a localized electrical resistance dependent on the crack opening has to be introduced in that intersection point. In the present study, a refinement of these electric models is proposed by introducing a sheet resistance dependent on the amount of damage induced by cracks in the surrounding material. The proposed model is successfully validated in reference to experimental data on mono-crystalline Silicon solar cells with cracks artificially created by Vickers indentation, providing an insight into the electric degradation mechanisms caused by crack interaction phenomena

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

Landslide hazard and land management in high-density urban areas of Campania region, Italy

Abstract. Results deriving from a research focused on the interplay between landslides and urban development are presented here, with reference to two densely populated settings located in the Campania region, Italy: the city of Naples and the island of Ischia. Both areas suffer adverse consequences from various types of landslides since at least 2000 yr. Our study evidences that, despite the long history of slope instabilities, the urban evolution, often illegal, disregarded the high landslide propensity of the hillsides; thus, unsafe lands have been occupied, even in recent years, when proper and strict rules have been enacted to downgrade the landslide risk. It is finally argued that future guidelines should not be entirely based upon physical countermeasures against mass movements. On the contrary, national and local authorities should enforce the territorial control, obliging citizens to respect the existing regulations and emphasizing the role of alternative, non-structural solutions

Potential of remote sensing data to support the seismic safety assessment of reinforced concrete buildings affected by slow-moving landslides

Different forms of hazard can affect structures throughout their existence. The occurrence of a seismic event in areas exposed to different risks or already affected by other phenomena is highly likely, especially in countries characterized by high seismicity and equally high hydrogeological risk, as Italy. Nevertheless, the seismic safety assessment of reinforced concrete (RC) structures is commonly carried out considering the seismic action only, generally applied to an analytical model, neglecting the stress–strain state induced by previous ongoing phenomena. The aim of this work is to highlight the importance of the seismic safety assessment in a multi-hazard analysis, cumulating the action coming from two different hazards: landslide and earthquake. An existing RC building, located in an area affected by an intermittent landslide phenomenon with slow kinematics, that may also be subjected to strong earthquakes, is used as case study. The Differential Synthetic Aperture Radar Interferometry (DInSAR) approach is used to monitor the evolution in time of the landslide. DInSAR deformation data are used to detect surface ground movements applied to building foundations. A non-linear static analysis procedure is implemented for the code-based seismic safety assessment, in two different scenarios. The seismic assessment of the case-study building is implemented in a condition of structure deformed only for gravity loads, and, then, in a state of known landslide-induced deformed configuration. A comparison is proposed between the building seismic safety assessment performed in both cases, with or without the consideration of the landslide-induced displacements, showing the importance of a multi-hazard evaluation

PS-driven inventory of town-damaging landslides in the Benevento, Avellino and Salerno Provinces, southern Italy

The Apennine provinces of Campania Region (southern Italy), Benevento, Avellino and Salerno, are known for their 'unstable towns' suffering periodic damage from landslides. Their identification and mapping are very challenging tasks, since boundary mapping under urban settlements is not always possible without time-consuming field analysis of building damage and/or expensive mid-term diffuse ground-surface deformation monitoring. To overcome this problem, an inventory of town-damaging landslides, guided by available Permanent Scatterers (PS) ground-deformation data, was prepared. It provides an updated tool suitable to guide future land planning and historical site restoration in the Apennine provinces of Campania Region. Our fourteen Map Sheets show active and local reactivation of suspended/dormant landslides. Overall, 356 landslides were identified, amongst which 162 were identified as flows, 101 as slides, 1 as a spreads and 92 as complex landslides. To supplement our maps, a simplified distribution analysis based on major landslide morphometric characteristics was completed