Awake Da Vinci robotic partial nephrectomy: First case report ever in a situation of need

We report a unique case of a robotic partial nephrectomy performed under continuous spinal anesthesia (CSA). A 63-year-old woman, active smoker with mild obesity and previous right pneumonectomy, was diagnosed with a growing 5.5-cm renal right cystic tumor. Being at high risk for general anesthesia, a loco-regional approach was indicated. Therefore, after multidisciplinary discussion, a robotic-assisted partial nephrectomy under CSA was considered mandatory. After T4-T5 sensory and motor block, retroperitoneoscopic robot-assisted surgery was successfully performed. Postoperative period was uneventful, with optimal pain control. This unique case demonstrates the feasibility of robotic surgery under CSA, for imperative indications

The Differential Slow Moving Dynamic of a Complex Landslide: Multi-sensor Monitoring

World Landslide Forum (4º. 2017. Liubliana, Eslovenia)Monitoring is essential to understand the mechanics of landslides, and predict their behavior in time and space. In this work we discuss the performance of multi-sensor monitoring techniques applied to measure the kinematics and the landslide hydrology of Portalet landslide complex, which is located in the SW-facing slopes of Petrasos peak at the border between Spain and France. In the summer 2004, the excavation of a parking lot at the foot of the slides triggered a secondary failure in the lower part of the slope, accelerating the dynamic of the landslide complex. The deployed hydro-meteorological network has been useful to understand that the greatest infiltration in the moving mass is produced in spring due to the combination of snow melt and seasonal rainfall. Landslide surface kinematics measured with differential GPS (D-GPS) were useful to measure the slower (<10 cm/year) and faster (20–30 cm/year) dynamic of the landslide complex. Advanced DInSAR was useful to monitor the slower ground displacements from long datasets of SAR images, providing a wider spatial coverage and measurement point density than the D-GPS. In addition, the NL-InSAR processing strategy was applied to monitor the faster motion using short datasets of TerraSAR-X images excluding the snow cover period. The installed horizontal extensometers were useful to study the extension of the head scarp and its relationship with landslide hydrology, which is affected by the retrogressive effect of the landslide due to the loss of lateral confining pressure. Finally, an inclinometric robot system (AIS) was the only technique capable of detecting 5–6 time faster motion after the snow melt, since it provides daily measurements with high accuracy even during the snow cover period. These data, even if expensive to gather, are necessary to improve the hydro-mechanical modeling of large slow landslides, such as those already proposed for Portalet landslide complex.Geohazard InSAR Laboratory and Modelling Group, Instituto Geológico y Minero de España, EspañaNational Research Council, ItaliaEscuela de Minas, Univesidad de Oviedo, EspañaDares Technology, EspañaAltamira Information, EspañaPeer reviewe

Advances on Measuring Deep-Seated Ground Deformations Using Robotized Inclinometer System

In the field of geo-hazards and geo-engineering, monitoring networks represent a key element for the geological risk assessment and the design and management of large infrastructures construction. In the last decade, we have observed a strong development on remote sensing techniques but just small changes in the subsoil observations. However, this type of measurement is very important to have a three-dimensional representation of the studied area, since the surface measurements often represent a sum of deformations that develop in a complex way in the subsoil. In this paper, we present a robotic inclinometer system developed to acquire deep-seated ground deformations in boreholes. This instrumentation combines advantages offered by manual inclinometer measurements with a robotized approach that improves the results in term of accuracy, revisiting time, and site accessibility. The Automated Inclinometer System (AIS) allows one to explore automatically all the length of the monitored borehole using just one inclinometer probe with a semi-wireless system. The paper presents the system and a detailed dataset of measurements acquired on three inclinometer tubes installed for the monitoring of the construction phase of the new Line C Metro of Rome. The dataset was acquired in real monitored site and undisturbed conditions and can represent a benchmark for modern inclinometer measurements

ADVICE: A New Approach for Near-Real-Time Monitoring of Surface Displacements in Landslide Hazard Scenarios

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An Electrical Impedance Tomography System for Brain Stroke Imaging based on a Lebesgue-Space Inversion Procedure

An electrical impedance tomography (EIT) system for brain stroke monitoring is presented in this paper. The developed setup is composed by an ad-hoc measurement prototype equipped with an efficient imaging method for the reconstruction of the distribution of the electric conductivity of the body under test. In particular, a time-difference formulation is adopted, and the resulting ill-posed equation is solved by means of an iterative procedure performing a regularization in the framework of Lebesgue spaces. The performance of the method has been assessed by means of several numerical simulations. Moreover, a preliminary validation with experimental data has been performed, too. The obtained results confirm that the approach is able to effectively detect inclusions with different sizes and locations inside the considered head models

A Novel Hyperspectral Method to Detect Moldy Core in Apple Fruits

An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by Alternaria alternata, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days&rsquo; time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits