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The European Commission published in 2015 “Energy Union Strategy” to make energy production safer, more sustainable, competitive, and economical. To allow citizens of a community to cooperate to consume and exchange renewable energy they produce, the use of blockchain technology seems natural. In this note, we will start from a concrete case study to see what the technological challenges (and not only) are to be overcome. Furthermore, in the last sections, it will be shown how the creation of the IT infrastructure necessary for the exchange of energy produced can also be used for other purposes. That is according to the smart city paradigm to create a community able to make the most of the touristic, environmental, and social resources of the territory in which it lives.

Doppler tracking is nowadays an essential tool in deep space orbit determination and radio science investigations. One of the most critical steps in this tracking process is the frequency estimation, and the variance of this estimation process is directly linked to the accuracy of spacecraft range-rate estimation, therefore possibly limiting it. In this paper, an FFT-based algorithm is presented and characterized using signals corrupted by additive noise channels of varying intensity. A comparison to the current operational tracking method for Open Loop data, based on Phase-Locked Loops, is presented and discussed. Monte Carlo simulations confirm that the estimation accuracy of the proposed method is appropriate for radio science experiments and achieves better accuracies than the alternative method already in use, in case of low signal-to-noise ratios.

Standing and concurrently performing a cognitive task is a very common situation in everyday life. It is associated with a higher risk of falling in the elderly. Here, we aim at evaluating the differences of the P300 evoked potential elicited by a visual oddball paradigm between healthy younger (35 y) and older (64 y) adults during a simultaneous postural task. We found that P300 latency increases significantly (p0.001) when the elderly are engaged in more challenging postural tasks; younger adults show no effect of balance condition. Our results demonstrate that, even if the elderly have the same accuracy in odd stimuli detection as younger adults do, they require a longer processing time for stimulus discrimination. This finding suggests an increased attentional load which engages additional cerebral reserves.

IoT data analytics can potentially bring benefits to several critical application domains, especially in healthcare. In fact, especially in emergency rooms the detection of critical patients can be a critical task when the number of patients to be monitored is high with respect to the available medical personnel. However, it is also necessary to pay attention to ethics, privacy, and security issues, aiming to prevent attacks and unauthorized access to sensitive data of patients, guaranteeing the correct functioning of the system in a secure environment. To this end, this article presents a knowledge representation framework enabling the intelligent video surveillance of patients, which can be used in combination with IoT-based systems to enhance the detection of critical patients in emergency rooms, while dealing with ethics, privacy, and security issues. These are guaranteed by means of an event-based visual access control specification method, constraining the access to both devices and users. We also describe a clinical scenario related to the early treatment of sepsis in an emergency room, showing how the proposed framework can enhance the detection of such critical disease while guaranteeing ethics, privacy, and security.

In recent years, Deep Learning techniques have achieved some success in bioinformatics tasks, including protein conformation prediction. In this work, we propose a Bidirectional Long Short-Term Memory (BLSTM) network system, called Human Proteins Angles Prediction (HPAP), in order to improve the prediction of dihedral angles of proteins. We have introduced a discrete subdivision in classes of 5° for protein torsion angles and four new features related to accessible surface area and volume. In total there are 73 classes (72 classes include the angles between -180° and 180°, a further class is used to code the free angles at the beginning of the sequence) with a maximum expected error of ±2.5°. We have tested three model variants in several parameter combinations. With our model, we have obtained a decrease of the mean absolute error of about 2° for the $\psi$ angle. Although our dataset is reduced in size, the accuracy of $\varphi$ and $\psi$ angles is comparable to the existing methods. Predicting angles accurately is useful for accurately reconstructing the three-dimensional structure of a protein. In this context, the prediction is limited to the $\varphi$ and $\psi$ angles and we will visualize what happens locally when a prediction is correct. In case the prediction is far from true angles, even a small error can deconstruct the backbone.

Recent years have witnessed the rise of pathomics as a mean to describe histopathological images with quantitative biomarkers for predictive and prognostic ends, combining digital pathology, omic science and artificial intelligence. This novel research branch is the counterpart of radiomics which pursues the same aims extracting knowledge from radiological images. In this paper, we present the design of a pathomic deep learning-based system to predict the treatment outcome in non-small cell lung cancer patients. We describe the system design and optimization under the condition of limited data and limited training, with corresponding tests. The experimental results show the feasibility of the proposed scalable architecture providing also a comparison between different transfer learning strategies.

LICIACube (Light Italian Cubesat for Imaging of Asteroid) is a 6U CubeSat project led by the Italian Space Agency (ASI), to be carried by NASA’s DART (Double Asteroid Redirection Test) mission to the Didymos binary asteroid system. The DART mission objectives are to measure the change in momentum of Didymos secondary (informally called Didymoon) after the impact, characterize the asteroid composition, and increase the knowledge of the system physical parameters in order to assess the performance of the asteroid deflection strategy. The main goal of LICIACube is to support DART mission goals by taking images of the impact effects on the surface and the generated ejecta plume. LICIACube will be deployed from DART in proximity of the target, before the impact, and will perform an autonomous fly-by of Didymos. This paper offers an overview of the LICIACube project status, science objectives and technological challenges.

To accurately estimate the mass of a comet, is a challenging task, given the complexity of its dynamical environment, and particularly so while close to perihelion, where the coma emissions are stronger. This study describes an operational approach for simultaneous orbit determination and mass estimation of a cometary target, using detailed dynamical models and combining measurements from multiple instruments. The testbed for the proposed approach is represented by the Proteus mission towards the main belt comets 288P and 238P-Read, for which formal uncertainties for the mass and other parameters of interest are estimated though a covariance analysis. It will be shown that the required relative accuracy of 10% in mass estimation can be achieved for both cometary targets under realistic operational conditions.

Melanoma is the deadliest form of skin cancer and it is diagnosed mainly visually, starting from initial clinical screening and followed by dermoscopic analysis, biopsy and histopathological examination. A dermatologist's recognition of melanoma may be subject to errors and may take some time to diagnose it. In this regard, deep learning can be useful in the study and classification of skin cancer. In particular, by classifying images with Deep Neural Network methodologies, it is possible to obtain comparable or even superior results compared to those of dermatologists. In this paper, we propose a methodology for the classification of melanoma by adopting different deep learning techniques applied to a common dataset, composed of images from the ISIC dataset and consisting of different types of skin diseases, including melanoma on which we applied a specific pre-processing phase. In particular, a comparison of the results is performed in order to select the best effective neural network to be applied to the problem of recognition and classification of melanoma. Moreover, we also evaluate the impact of the preprocessing phase on the final classification. Different metrics such as accuracy, sensitivity, and specificity have been selected to assess the goodness of the adopted neural networks and compare them also with the manual classification of dermatologists.

This paper presents a patient-centered interaction project of Pepper humanoid robotic therapy applications for children with attention deficit. This new therapeutic methodology was created to support and make therapeutic work more attractive. Pepper comes with a tablet and two identical cameras. The tablet is used to make the patients interact with the application, while the cameras are used to capture their emotions in real-time to understand the degree of attention and any difficulties they may have. The interaction with the tablet takes place through some exercises in the form of games. The exercises performed by the patients are analyzed and combined with the data acquired by the cameras. The combination of these data is elaborated to propose adequate levels of therapeutic activity. This process leads to the digitization of the patients' therapeutic path so that any improvement (or worsening) is monitored and makes Pepper a reliable and predictable technological intermediary for the child. The work was developed in collaboration with a diagnostic and therapeutic center, where it is being tested. By interacting with a humanoid robot, children show greater involvement, which can be explained, according to psychologists, by the fact that a robot is emotionally less rich than humans and the patients feel less fearful.

The increasing interest of the research community in the intertwined fields of brain-machine interface (BMI) and robotics has led to the development of a variety of brain-actuated devices, ranging from powered wheelchairs and telepresence robots to wearable exoskeletons. Nevertheless, in most cases, the interaction between the two systems is still rudimentary, allowing only an unidirectional simple communication from the BMI to the robot that acts as a mere passive end-effector. This limitation could be due to the lack of a common research framework, facilitating the integration of these two technologies.In this scenario, we proposed ROS-Neuro to overcome the aforementioned limitations by providing a common middleware between BMI and robotics. In this work, we present a working example of the potentialities of ROS-Neuro by describing a full closed-loop implementation of a BMI based on motor imagination. The paper shows the general structure of a closed-loop BMI in ROS-Neuro and describes the specific implementation of the packages related to the proposed motor imagery BMI, already available online with source codes, tutorials and documentations. Furthermore, we show two practical case scenarios where the implemented BMI is used to control a computer game or a telepresence robot with ROS-Neuro. We evaluated the performance of ROS-Neuro by ensuring comparable results with respect to a previous BMI software already validated. Results demonstrated the correct behavior of the provided packages.

Melanoma is the deadliest form of skin cancer. It mainly requires a visual diagnosis by dermatologists. However, a dermatologist’s recognition of melanoma may be subject to errors and may take some time to diagnose correctly it. To this aim, in the last twenty years, Computer-Aided Diagnosis systems based on artificial vision are increasingly adopted to support dermatologists in the early diagnosis of melanoma. However, these systems exploits only a reduced set of parameters or they implement a melanoma classifier that tries to substitute the dermatologists, without supporting their experience in the classification of skin lesions. This paper proposes a mobile application for supporting the clinician decision in the diagnosis of melanoma directly in the dermatologist environment by using Augmented Reality technology. In particular, computer-generated perceptual information is added to the image of patient skin reporting the values of various parameters and the lesion classification based on deep learning approach for analyzing skin lesions and identifying melanoma.

The detection of critical patients in Emergency Departments is often a critical task, especially in situations in which the number of patients to be monitored is high with respect to the available medical personnel. To this end, IoT data analytics can provide a useful support in automatically monitoring the status of patients, and detect the most critical ones. This paper presents a knowledge representation frame-work enabling the intelligent video surveillance of patients, which can be used in combination with IoT-based systems to enhance the detection of critical patients in emergency departments, and alert medical personnel. We also describe a clinical scenario related to the early treatment of sepsis in the emergency department, and show how the proposed framework can enhance the detection of such critical disease.

The COVID-19 disease caused by the coronavirus SARS-nCoV2 is currently a global public health threat and Italy is one of the countries mostly suffering from this epidemic. It is therefore important to analyze epidemic data, considering also that the government deployed laws limiting the societal activities. We model COVID-19 dynamics with a SIQR (susceptible - infectious - quarantined - recovered) model, where we take into account the temporal variability of its parameters. Particle Swarm Optimization is used to find out the best parameters in the case of Italy and of Italian regions where the epidemic has the greatest impact. The basic reproductive number is estimated by a novel approach that averages out different PSO fits computed considering different temporal time-windows and reducing possible noise in the data. The results on data collected from February 24 to April 24 show that our approach is able to fit the data with low errors and that the basic reproductive number is characterized by a descending trend in time from 3.5 to a value below 1.

The high deformability and compliance of soft robots allow safer interaction with the environment. On the other hand, these advantages bring along controllability and predictability challenges which result in loss of force and stiffness output. Such challenges should be addressed in order to improve the overall functional performance and to meet the requirements of real-scenario applications. In this paper, we present a bidirectional in-plane manipulator which consists of two unidirectional fiber-reinforced actuators (FRAs) and a hybrid soft-rigid stiffness control structure (SCS), all of them controlled by air pressure. Both controllability and predictability of the manipulator are enhanced by the hybrid soft-rigid structure. While the FRAs provide positioning and position dependent stiffness, the SCS increases the stiffness of the manipulator without position dependency. The SCS is able to increase the manipulator stiffness by 35%, 30%, and 18%, when one FRA is pressurized at 150 kPa, 75 kPa, and 0 kPa, respectively. Experiments are carried out to present the feasibility of the proposed manipulator.