Physics-based Residual Kriging for dynamically evolving functional random fields

AbstractWe present a novel approach named Physics-based Residual Kriging for the statistical prediction of spatially dependent functional data. It incorporates a physical model—expressed by a partial differential equation—within a Universal Kriging setting through a geostatistical modelization of the residuals with respect to the physical model. The approach is extended to deal with sequential problems, where samples of functional data become available along consecutive time intervals, in a context where the physical and stochastic processes generating them evolve, as time intervals succeed one another. An incremental modeling is used to account for both these dynamics and the misfit between previous predictions and actual observations. We apply Physics-based Residual Kriging to forecast production rates of wells operating in a mature reservoir according to a given drilling schedule. We evaluate the predictive errors of the method in two different case studies. The first deals with a single-phase reservoir where production is supported by fluid injection, while the second considers again a single-phase reservoir but the production is driven by rock compaction

A Small Power Recovery Expander for Heat Pump COP Improvement

AbstractHeat pumps are becoming more and more applied for heating, due to their possibility of working as cooling systems in the summer period. However, up to now, recovery of expansion work in small system has not been considered as a viable solution, because of the limited amount of recoverable energy and of difficulties in designing and operating a two-phase flow expander.The idea here presented is to investigate the application of a radial piston machine, adapted from oleodynamic motor designs, as an expander that will be coupled with the compressor motor shaft; the consequent power reduction goes directly to COP improvement.First, heat pumps working conditions (pressures, mass and volume flows) are defined, and the possible power recovery is calculated. Then, a model for the performance calculation of the radial piston motors used as expander is presented; the model considers the kinematics of the mechanical system and uses real fluid properties.The results indicate that such a machine could be developed from existing units with limited modifications, encourage to develop a test rig, and to run preliminary experimental work in order to measure the real performance

A Cognitive Robot Control Architecture for Autonomous Execution of Surgical Tasks

The research on medical robotics is starting to address the autonomous execution of surgical tasks, without effective intervention of humans apart from supervision and task configuration. This paper addresses the complete automation of a surgical robot by combining advanced sensing, cognition and control capabilities, developed according to rigorous assessment of surgical require- ments, formal specification of robotic system behavior and software design and implementation based on solid tools and frame- works. In particular, the paper focuses on the cognitive control architecture and its development process, based on formal modeling and verification methods as best practices to ensure safe and reliable behavior. Full implementation of the proposed architecture has been tested on an experimental setup including a novel robot specifically designed for surgical applications, but adaptable to different selected tasks (i.e. needle insertion, wound suturing)

Mendelian breeding units <i>versus</i> standard sampling strategies: mitochondrial DNA variation in southwest Sardinia

We report a sampling strategy based on Mendelian Breeding Units (MBUs), representing an interbreeding group of individuals sharing a common gene pool. The identification of MBUs is crucial for case-control experimental design in association studies. The aim of this work was to evaluate the possible existence of bias in terms of genetic variability and haplogroup frequencies in the MBU sample, due to severe sample selection. In order to reach this goal, the MBU sampling strategy was compared to a standard selection of individuals according to their surname and place of birth. We analysed mitochondrial DNA variation (first hypervariable segment and coding region) in unrelated healthy subjects from two different areas of Sardinia: the area around the town of Cabras and the western Campidano area. No statistically significant differences were observed when the two sampling methods were compared, indicating that the stringent sample selection needed to establish a MBU does not alter original genetic variability and haplogroup distribution. Therefore, the MBU sampling strategy can be considered a useful tool in association studies of complex traits

Predictive Rolling Bearing Maintenance

A method of predicting a fault in a rolling bearing, the rolling bearing including inner and outer rings and rolling bodies evenly angularly distributed therebetween, the method comprising:. processing (in the DSP system 8) a position signal (x(t)) indicative of a relative angular position of the inner ring with respect to the outer rings, and a vibration signal (y(t)) (by the accelerometer 7) indicative of speed-related vibrations in the rolling bearing, such that they correspond to either an angular displacement of the rolling bodies equal to an integer number of angular gaps between adjacent rolling bodies or an integer number of complete rotations of the inner ring with respect to the outer ring; . space sampling (in the A/D acquisition board 9) the processed vibration signal (y(t)) based on the processed position signal (x(t)); and. predicting a fault in the rolling bearing based on the space-sampled vibration signal (y(t))

A First Evaluation of a Multi-Modal Learning System to Control Surgical Assistant Robots via Action Segmentation

The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This paper presents a cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller

Mendelian breeding units versus standard sampling strategies: Mitochondrial DNA variation in southwest Sardinia

We report a sampling strategy based on Mendelian Breeding Units (MBUs), representing an interbreeding group of individuals sharing a common gene pool. The identification of MBUs is crucial for case-control experimental design in association studies. The aim of this work was to evaluate the possible existence of bias in terms of genetic variability and haplogroup frequencies in the MBU sample, due to severe sample selection. In order to reach this goal, the MBU sampling strategy was compared to a standard selection of individuals according to their surname and place of birth. We analysed mitochondrial DNA variation (first hypervariable segment and coding region) in unrelated healthy subjects from two different areas of Sardinia: the area around the town of Cabras and the western Campidano area. No statistically significant differences were observed when the two sampling methods were compared, indicating that the stringent sample selection needed to establish a MBU does not alter original genetic variability and haplogroup distribution. Therefore, the MBU sampling strategy can be considered a useful tool in association studies of complex traits

Short term outcome of myocarditis and pericarditis following COVID-19 vaccines: a cardiac magnetic resonance imaging study

To evaluate clinical and cardiac magnetic resonance (CMR) short-term follow-up (FU) in patients with vaccine-associated myocarditis, pericarditis or myo-pericarditis (VAMP) following COVID-19 vaccination. We retrospectively analyzed 44 patients (2 women, mean age: 31.7 +/- 15.1 years) with clinical and CMR manifestations of VAMP, recruited from 13 large tertiary national centers. Inclusion criteria were troponin raise, interval between the last vaccination dose and onset of symptoms &lt; 25 days and symptoms-to-CMR &lt; 20 days. 29/44 patients underwent a short-term FU-CMR with a median time of 3.3 months. Ventricular volumes and CMR findings of cardiac injury were collected in all exams. Mean interval between the last vaccination dose and the onset of symptoms was 6.2 +/- 5.6 days. 30/44 patients received a vaccination with Comirnaty, 12/44 with Spikevax, 1/44 with Vaxzevria and 1/44 with Janssen (18 after the first dose of vaccine, 20 after the second and 6 after the "booster" dose). Chest pain was the most frequent symptom (41/44), followed by fever (29/44), myalgia (17/44), dyspnea (13/44) and palpitations (11/44). At baseline, left ventricular ejection fraction (LV-EF) was reduced in 7 patients; wall motion abnormalities have been detected in 10. Myocardial edema was found in 35 (79.5%) and LGE in 40 (90.9%) patients. Clinical FU revealed symptoms persistence in 8/44 patients. At FU-CMR, LV-EF was reduced only in 2 patients, myocardial edema was present in 8/29 patients and LGE in 26/29. VAMPs appear to have a mild clinical presentation, with self-limiting course and resolution of CMR signs of active inflammation at short-term follow-up in most of the cases

Development of a cognitive robotic system for simple surgical tasks

The introduction of robotic surgery within the operating rooms has significantly improved the quality of many surgical procedures. Recently, the research on medical robotic systems focused on increasing the level of autonomy in order to give them the possibility to carry out simple surgical actions autonomously. This paper reports on the development of technologies for introducing automation within the surgical workflow. The results have been obtained during the ongoing FP7 European funded project Intelligent Surgical Robotics (I-SUR). The main goal of the project is to demonstrate that autonomous robotic surgical systems can carry out simple surgical tasks effectively and without major intervention by surgeons. To fulfil this goal, we have developed innovative solutions (both in terms of technologies and algorithms) for the following aspects: fabrication of soft organ models starting from CT images, surgical planning and execution of movement of robot arms in contact with a deformable environment, designing a surgical interface minimizing the cognitive load of the surgeon supervising the actions, intra-operative sensing and reasoning to detect normal transitions and unexpected events. All these technologies have been integrated using a component-based software architecture to control a novel robot designed to perform the surgical actions under study. In this work we provide an overview of our system and report on preliminary results of the automatic execution of needle insertion for the cryoablation of kidney tumours