Studio mineralogico-petrografico dei reperti in pietra ollare della rocca di Monselice

Il presente lavoro, rimasto inedito fino ad oggi, rende conto dell'attività di ricerca svolta e dei principali risultati conseguiti dall'autore sui reperti in pietra ollare della rocca di Monselice. Il documento, completato nell'agosto 1999, fornisce il quadro mineralogico-petrografico dei reperti oggetto di studio e, per ciascun litotipo, alcune indicazioni sul settore delle Alpi di provenienza della pietra ollare. Il contenuto di questo lavoro riflette lo stato delle conoscenze e delle tecniche adottate al momento della redazione del testo e va ad integrare il contributo di Chiara Malaguti che viene pubblicato su questo stesso volume. This research, carried out in August 1999 (and previously unpublished), focuses on the mineralogical and petrographic analysis of the soapstone fragments found in the excavation of the Rocca di Monselice (Italy). Each lithotype is examined in order to distinguish the origin of the soapstone in different areas of the Alps. This work reflects the state of the art and techniques used in the late 1990s and complements Chiara Malaguti’s paper published in this volum

Effects of MIneralocorticoid and AT-1 Receptor Antagonism on The Diagnosis of Primary Aldosteronism (EMIRA Study)

Supplemental data to the manuscript "Effects of MIneralocorticoid and AT-1 Receptor Antagonism on The Aldosterone-Renin Ratio (ARR) In Primary Aldosteronism Patients (EMIRA Study) – A Within-patient Study" submitted to jCE

Short-term incentives make people save more for the future

Data for the paper entitled "Short-term incentives make people save more for the future" (authors: Enrico Rubaltelli and Lorella Lotto

A Novel GPER Mediated Feed-forward Loop Enhancing Aldosterone Biosynthesis

Supplemental data The Journal of Clinical Endocrinology & Metabolism - JCEM submissio

High velocity friction data and thermochemical modeling data of smectite-rich STx-1b (vacuum dry, room humidity and water partly saturated)

The dataset is described also in the readme.txt file as follows: The columns are organized as follows: * Time [milliseconds]: time * Normal [MPa]: normal stress * Slip [m]: equivalent displacement * Vel [m/s]: equivalent tangential velocity * Shear1 [MPa]: shear stress * Mu1 []: friction coefficient = shear stress / normal stress * Thick [mm]: thickness of the gouge layer measured with the low resolution LVDT * Thick_high [mm]: thickness of the gouge layer measured with the high resolution LVDT Models Subfolder \model\ Each filename is the experiment’s name and is in .CSV format. The header for all the tables is in a separate file: header.CSV. The columns are organized as follows: * Time [seconds]: time * U_1 [°C]: temperature in node 1 * U_20 [°C]: temperature in node 20 * Qso [°C/s]: temperature source * Qsi1 [°C/s]: temperature sink of reaction 1 (smectite interlayer dehydration) * Qsi2 [°C/s]: temperature sink of reaction 2 (smectite dehydroxylation) * U2_1 [MPa]: pressure in node 1 * U2_20 [MPa]: pressure in node 20 * Thpress [Pa/s]: pressure source for thermal pressurization * Omega1 [Pa/s]: pressure source for reaction 1 (smectite interlayer dehydration) * Omega2 [Pa/s]: pressure source for reaction 2 (smectite dehydroxylation) * R1_1 []: reacted fraction for reaction 1 (smectite interlayer dehydration) * R2_1 []: reacted fraction for reaction 2 (smectite dehydroxylation

Smartphone identification database

This folder is composed of six .mat files containing the data used in the article ''Smartphone Identification via Passive Traffic Fingerprinting: a Sequence-to-Sequence Learning Approach'' by Francesca Meneghello, Michele Rossi and Nicola Bui, DOI: 10.1109/MNET.001.1900101. The Python source code is available at https://github.com/signetlabdei/smartphone_identification. If you use this dataset, please cite our paper: @article{Meneghello2020Network, author={Meneghello, Francesca and Rossi, Michele and Bui, Nicola}, journal={IEEE Network}, title={Smartphone Identification via Passive Traffic Fingerprinting: A Sequence-to-Sequence Learning Approach}, year={2020}, volume={34}, number={2}, pages={112-120}

A dataset for hand-eye calibration evaluation

Description: This dataset aims to assess the accuracy of hand-eye calibration methods (i.e., estimation of the transformation between a robot end effector frame and a camera mounted on it). It contains two sets of images and corresponding robot hand poses. The first one (calib_test) contains images of a calibration pattern to estimate the hand-eye transformation. The second one (spirit_reconst) contains images of a pattern to be 3D reconstructed and manually annotated 2D feature points on the images. By performing multi-view 3D reconstruction on the second set and checking the flatness of the reconstructed points, the calibration accuracy can be assessed. The dimension of the calibration pattern in this dataset is 32 mm. Paper: Kenji Koide and Emanuele Menegatti, General Hand-Eye Calibration based on Reprojection Error Minimization, IEEE Robotics and Automation Letters/ICRA201

Supplemental data on "Drug-Resistant Hypertension in A Large Registry of Primary Aldosteronism"

Supplemental data of the article published as: European Journal of Preventive Cardiology, Volume 29, Issue 2, January 2022, Pages e85–e93, https://doi.org/10.1093/eurjpc/zwaa10

Transfer Form Factor v.4.1: a Mathematica code for calculation of one- and two-neutron transfer form factors and cross-sections

oai:researchdata.cab.unipd.it:41Code Wavefunction database Reaction examples Notes and explanation