View VULMA: Data Set for Training a Machine-Learning Tool for a Fast Vulnerability Analysis of Existing Buildings

The paper presents View VULMA, a data set specifically designed for training machine-learning tools for elaborating fast vulnerability analysis of existing buildings. Such tools require supervised training via an extensive set of building imagery, for which several typological parameters should be defined, with a proper label assigned to each sample on a per-parameter basis. Thus, it is clear how defining an adequate training data set plays a key role, and several aspects should be considered, such as data availability, preprocessing, augmentation and balancing according to the selected labels. In this paper, we highlight all these issues, describing the pursued strategies to elaborate a reliable data set. In particular, a detailed description of both requirements (e.g., scale and resolution of images, evaluation parameters and data heterogeneity) and the steps followed to define View VULMA are provided, starting from the data assessment (which allowed to reduce the initial sample of about 20.000 images to a subset of about 3.000 pictures), to achieve the goal of training a transfer-learning-based automated tool for fast estimation of the vulnerability of existing buildings from single pictures.

Tank Modeling and Its Condition Assessment using Terrestrial Laser Scanner

Condition assessment of the tank must be carried out since it is related to Health, Safety, and Environment (HSE). Assessment is carried out by referring to the applicable standards. This study aims to create a 3D (Dimension) model and assess the tank using Terrestrial Laser Scanner technology. This includes planning, data acquisition, data processing, and data visualization. The data processing process starts with the registration stage with the cloud-to-cloud method, georeferencing, 3D modeling using point cloud, and tank assessment filtered point cloud data. Assessment includes analysis of volume, verticality/slope (in terms of the difference between upper and lower tank), and roundness calculations. The 3D model of the tank was generated with a registration error of less than 1 cm. The volume of tank I and tank II were calculated to 134.108 m3 and 134.067 m3, respectively. The difference between the upper and lower radius for each tank ranges from 2 to 10 mm. Considering the results and recalling the API 650 standard (American Petroleum Institute), each tank is considered reliable and in a good condition.

Early Experience with Dabrafenib–Trametinib Combination in Patients with BRAF-Mutated Malignant Melanoma—A Single-Center Experience

Background Combination of dabrafenib–trametinib is one of the standard treatments in patients with BRAF-mutated advanced malignant melanoma (MM). Real-world data on the usage of this combination is scarce, especially from India. Here, we are reporting our early experience with the usage of this combination therapy. Materials and Methods This is a single institutional data assessment of patients with BRAF-mutated MM registered and treated with BRAF–MEK inhibitors in our hospital. Clinico-pathological features and treatment details were reviewed for all patients. Results A total of seven patients with BRAF-mutated MM treated with this combination therapy with a median age of 66.5 years (range: 49–72 years) and a male:female ratio of 3:4. Six (85.7%) patients had metastatic disease at presentation. In total, 80% of our patient population had two or less than two sites of metastasis at presentation. The initial response rate of the study population was 71%. The drug was well tolerated with fever being the most common side effect which was seen in two (28.5%) of the patients. Conclusion Combination of dabrafenib–trametinib is effective in patients with BRAF-mutated MM with good tolerability. Further studies are required to look for improvement in outcome in this group of patients.