18F-Sodium Fluoride (NaF) accumulates in areas of active hydroxyapatite deposition and potentially unstable atherosclerotic plaques. We assessed the presence of atherosclerotic plaques in 50 adult patients with HIV (HIV+) who had undergone two cardiac computed tomography scans to measure coronary artery calcium (CAC) progression. CAC and its progression are predictive of an unfavorable prognosis. Tracer uptake was quantified in six arterial territories: aortic arch, innominate carotid artery, right and left internal carotid arteries, left coronary (anterior descending and circumflex) and right coronary artery. Thirty-one patients showed CAC progression and 19 did not. At least one territory with high NaF uptake was observed in 150 (50%) of 300 arterial territories. High NaF uptake was detected more often in non-calcified than calcified areas (68% vs. 32%), and in patients without than in those with prior CAC progression (68% vs. 32%). There was no correlation between clinical and demographic variables and NaF uptake. In clinically stable HIV+ patients, half of the arterial territories showed a high NaF uptake, often in the absence of macroscopic calcification. NaF uptake at one time point did not correlate with prior progression of CAC. Prospective studies will demonstrate the prognostic significance of high NaF uptake in HIV+ patients.

<p>When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot's path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot's motion is developed using a two-fold control approach that combines B-Splines and Hidden Markov Models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start-goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera.</p>

<p>When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot's path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot's motion is developed using a two-fold control approach that combines B-Splines and Hidden Markov Models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start-goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera.</p>

The interest in power managing systems has been growing in recent years since every industrial or domestic plant moves towards techniques to efficiently reduce energy demand and costs related to it. An attractive solution is represented by Non-Intrusive Load Monitoring (NILM) systems, whose primary purpose is to find a more appropriate way of keeping track of the power consumption caused by each of the loads that are connected to the monitored plant. A possible real-life implementation of a NILM system is addressed in this work, discussing all the fundamental blocks in its structure, including detecting events, feature extraction, and load classification, using publicly available datasets. Additionally, we provide a solution for an embedded system, able to analyze aggregated waveforms and to recognize each appliance’s contribution in it. The main algorithm, its features, drawbacks, and implementation are thus explained, showing current and future challenges for the final application.