A social robot connected with chatGPT to improve cognitive functioning in ASD subjects.

Neurodevelopmental Disorders (NDDs) represent a significant healthcare and economic burden for families and society. Technology, including AI and digital technologies, offers potential solutions for the assessment, monitoring, and treatment of NDDs. However, further research is needed to determine the effectiveness, feasibility, and acceptability of these technologies in NDDs, and to address the challenges associated with their implementation. In this work, we present the application of social robotics using a Pepper robot connected to the OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After describing the general architecture of the system, we present two possible simulated interaction scenarios of a subject with Autism Spectrum Disorder in two different situations. Limitations and future implementations are also provided to provide an overview of the potential developments of interconnected systems that could greatly contribute to technological advancements for Neurodevelopmental Disorders (NDD).

Jewels from chaos: A fascinating journey from abstract forms to physical objects.

An intellectual journey that began with the discovery of strange attractors derived from Chua's circuit, their translation into physical shapes by means of 3D printers, and finally, to the production of jewelry is presented. After giving the mathematical characteristics of Chua's circuit, we explain the chaotic design process, used for creating jewels, providing specifications of the used methodological approach, for its reproduction. We discuss the feasibility of this approach and the transmission of scientific contents on chaos theory, usually restricted to university students, in a high school Science, Technology, Engineering, Art, and Mathematics course, for the realization of advanced educational processes, implemented both in computational and real environments. We think that the idea of transforming science into art forms can drive students in acquiring scientific knowledge and skills, allowing them to discover the inner beauty of chaos.

Modelling brain dynamics by Boolean networks.

Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial-temporal patterns. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.

A Project Based Learning Approach for Improving Students' Computational Thinking Skills.

An educational robotics lab has been planned for undergraduate students in an Electronic Engineering degree, using the Project Based Learning (PBL) approach and the NAO robot. Students worked in a research context, with the aim of making the functions of the NAO robot as social and autonomous as possible, adopting in the design process the Wolfram Language (WL), from the Mathematica software. Interfacing the programming environment of the NAO with Mathematica, they solved in part the problem of autonomy of the NAO, thus realizing enhanced functions of autonomous movement, recognition of human faces and speech for improving the system social interaction. An external repository was created to streamline processes and stow data that the robot can easily access. Self-assessment processes demonstrated that the course provided students with useful skills to cope with real life problems. Cognitive aspects of programming by WL have also been collected in the students' feedback.

SARS-CoV-2 emerging complexity and global dynamics.

The novel SARS-CoV-2 virus, prone to variation when interacting with spatially extended ecosystems and within hosts, can be considered a complex dynamic system. Therefore, it behaves creating several space-time manifestations of its dynamics. However, these physical manifestations in nature have not yet been fully disclosed or understood. Here we show 4D and 2D space-time patterns of the rate of infected individuals on a global scale, giving quantitative measures of transitions between different dynamical behaviors. By slicing the spatiotemporal patterns, we found manifestations of the virus behavior, such as cluster formation and bifurcation. Furthermore, by analyzing morphogenesis processes by entropy, we have been able to detect the virus phase transitions, typical of adaptive biological systems. Our results for the first time describe the virus patterning behavior processes all over the world, giving them quantitative measures. We know that the outcomes of this work are still partial and more advanced analyses of the virus behavior in nature are necessary. However, we think that the set of methods implemented can provide significant advantages to better analyze the viral behavior in the approach of system biology, thus expanding knowledge and improving pandemic problem solving.

On the temporal spreading of the SARS-CoV-2.

The behaviour of SARS-CoV-2 virus is certainly one of the most challenging in contemporary world. Although the mathematical modelling of the virus has made relevant contributions, the unpredictable behaviour of the virus is still not fully understood. To identify some aspects of the virus elusive behaviour, we focused on the temporal characteristics of its course. We have analysed the latency trends the virus has realized worldwide, the outbreak of the hot spots, and the decreasing trends of the pandemic. We found that the spatio-temporal pandemic dynamics shows a complex behaviour. As with physical systems, these changes in the pandemic's course, which we have called transitional stages of contagion, highlight shared characteristics in many countries. The main results of this work is that the pandemic progression rhythms have been clearly identified for each country, providing the processes and the stages at which the virus develops, thus giving important information on the activation of containment and control measures.

Mid-sagittal plane detection for advanced physiological measurements in brain scans.

OBJECTIVE: The process of diagnosing many neurodegenerative diseases, such as Parkinson's and progressive supranuclear palsy, involves the study of brain magnetic resonance imaging (MRI) scans in order to identify and locate morphological markers that can highlight the health status of the subject. A fundamental step in the pre-processing and analysis of MRI scans is the identification of the mid-sagittal plane, which corresponds to the mid-brain and allows a coordinate reference system for the whole MRI scan set. APPROACH: To improve the identification of the mid-sagittal plane we have developed an algorithm in Matlab® based on the k-means clustering function. The results have been compared with the evaluation of four experts who manually identified the mid-sagittal plane and whose performances have been combined with a cognitive decisional algorithm in order to define a gold standard. MAIN RESULTS: The comparison provided a mean percentage error of 1.84%. To further refine the automatic procedure we trained a machine learning system using the results from the proposed algorithm and the gold standard. We tested this machine learning system and obtained results comparable to medical raters with a mean absolute error of 1.86 slices. SIGNIFICANCE: The system is promising and could be directly incorporated into broader diagnostic support systems.