Publications

Coronavirus disease (COVID-19) has been a source of fear around the world. We asked whether the measurement of this fear is trustworthy and comparable across countries. In particular, we explored the measurement invariance and cross-cultural replicability of the widely used Fear of COVID-19 scale (FCV-19S), testing community samples from 48 countries (N = 14,558). The findings indicate that the FCV-19S has a somewhat problematic structure, yet the one-factor solution is replicable across cultural contexts and could be used in studies that compare people who vary on gender and educational level. The validity of the scale is supported by a consistent pattern of positive correlations with perceived stress and general anxiety. However, given the unclear structure of the FCV-19S, we recommend using latent factor scores, instead of raw scores, especially in cross-cultural comparisons.

This paper discusses historical-critical thought to address the problems of restoration and preservation of tape music, proposing viable solutions to the matter of digitizing the historically valuable data that exists on and is represented by magnetic tapes. A detailed program of research and restoration and some software for helping in creation of critical editions of the musical works are proposed. We also present some of the issues and controversies that must be considered and approaches we have applied in the preservation of tape music, highlighting how these interpretations can impact later performances (playback) of these tape documents. Fundamentally, we argue that the act of tape music restoration has a parallel with the interpretation of the “Urtext score” in the performance of music from the Common Practice Era.

Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) is the first standards organization to develop data coding standards that have artificial intelligence (AI) as their core technology. MPAI believes that universally accessible standards for AI-based data coding can have the same positive effects on AI as standards had on digital media. Elementary components of MPAI standards-AI modules (AIMs)-expose standard interfaces for operation in a standard AI framework (AIF). As their performance may depend on the technologies used, MPAI expects that competing developers providing AIMs will promote horizontal markets of AI solutions that build on and further promote AI innovation. Finally, the MPAI framework licences (FWLs) provide guidelines to intellectual property right (IPR) holders facilitating the availability of compatible licenses to standard users.

In November 2020 a mortality episode (30%) in juvenile Siberian and Russian sturgeons (Acipenser baerii, Brandt, and A. gueldenstaedtii, Brandt & Ratzeburg) and GUBA hybrid sturgeons (A. gueldenstaedtii × A. baerii) occurred in a hatchery in Northern Italy, associated with severe coelomic distension and abnormal reverse surface swimming. The fish were reared in concrete tanks supplied by well water, fed at 0.4% of body weight (b.w.) per day. Thirty sturgeon specimens were collected for necropsy, histological, bacteriological and virological examination. Macroscopic findings included diffuse and severe bloating of gastrointestinal tracts due to foamy contents with thinning and stretching of the gastrointestinal walls. Histological analysis revealed variable degrees of sloughing and necrosis of the intestinal epithelium, and the presence of bacterial aggregates. Anaerobic Gram-positive bacteria were investigated, and Clostridium perfringens was isolated from the gut. Specific PCRs identified the toxinotype A and the β2 toxin gene. The daily feed administration was increased to 1.5% b.w. and after 5 days, the mortality ceased. A new animal cohort from the same groups was examined after 12 weeks, showing neither gut alterations nor isolation of C. perfringens. The imbalance of intestinal microbiota, presumably caused by underfeeding, favoured C. perfringens overgrowth and severe gas formation. The diet increase possibly restored the normal microbiota.

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes a hybrid approach that combines geometric and appearance features for training Deep Encoder-Decoder architectures to detect the traversability score in real urban contexts. The proposed approach has been tested with two Deep Learning architectures on a public dataset of outdoor driving scenarios. Thanks to our approach, we are able to reach high levels of accuracy in detecting the correct traversability score in environments of highly variable complexity. This demonstrates the effectiveness and robustness of the proposed method.

Oral archives and digital technologies have gone hand-in-hand for a very long time. Both sides benefit from this interdisciplinary junction: technology enhances the preservation and diffusion of oral materials, while exploiting them to develop cutting-edge tools for their treatment. This chapter deals with an Italian instantiation of this mutual relationship: the Archivio Vi.Vo. project. Offering innovative solutions concerning metadata, audio restoration, description, and access, Archivio Vi.Vo. aims to build an online platform to host the oral archives from Tuscany. The project is powered by CLARIN-IT, which guarantees its compliance with standards and offers resources for data access and discoverability. Archivio Vi.Vo. has not been built from scratch: it is instead a crossfertilization of previous initiatives and research projects (e.g., the Gra.fo project). Moreover, the chapter presents the related, contemporary work of a multidisciplinary group striving to synthesize a Vademecum for future generations of oral archive researchers. Lastly, a brief list of tentative ideas for future developments of the Archivio Vi.Vo. platform will be presented.

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.

The last decades saw a great innovation in computer vision. Recently, the field has been fundamental in the development of autonomous navigation systems. Modern assistive technologies, like smart wheelchairs, could employ autonomous navigation to assist users during operation. A prerequisite for such systems is to recognise the navigable space in real-time. The current research features an off-the-shelf powered wheelchair customised into an intelligent robot, which perceives the environment using Point Cloud Semantic Segmentation (PCSS). The implemented algorithm is used to distinguish between two conditions, traversable and non-traversable space, in real-time, using the aforementioned conditions as the two labelled classes. The accuracy of traversable space detection resulted as 99.64% while the accuracy of non-traversable space detection was 91.79%. The performance of the suggested method was invariant to changes in wheelchair velocity indicating that the latency of the suggested algorithm is within the tolerable limits for real-time operation.

Neuromuscular fatigue could negatively affect postural balance, but its effects on dynamic postural regulation are still debated. This study aimed to investigate whether a fatigue protocol on calf muscle could affect muscle activation strategies and dynamic balance performance. Seventeen male adults (age 24.1 ± 4.6 years; height 183.9 ± 7.2 cm; weight 80.2 ± 7.2 kg) volunteered in the study. They performed a dynamic test on an instrumented platform, which provided anterior-posterior oscillations on the sagittal plane, before and after a localized fatigue protocol. High-density surface electromyographical (EMG) signals were recorded bilaterally from the soleus and the medial gastrocnemius muscles. The fatigue protocol, consisting of two quasi-isometric tiptoe standing exercise to failure with a fixed load, did not affect the global dynamic balance performance. Conversely, the frequency value corresponding to 95% of the total power spectrum density of the angular displacement signal increased after fatigue (from 1.03 ± 0.42 to 1.31 ± 0.42 Hz; p < 0.05). The EMG analysis showed a significant difference in the PRE/POST fatigue ratio of the root-mean-square (RMS) between the soleus and the gastrocnemius medialis muscles. No differences were detected for the coefficient of variation and the barycenter coordinates of the RMS EMG values between muscles and sides. The variations in the frequency content of the angular displacement and EMG activity across muscles may be related to an increase in the calf muscles stiffness after fatigue. The role of neuromechanical calf muscle properties seems to be relevant in maintaining the dynamic postural performance after a quasi-isometric fatigue protocol until failure.

An accurate diagnosis of the disorder of consciousness (DOC) is essential for generating tailored treatment programs. Accurately diagnosing patients with a vegetative state (VS) and patients in a minimally conscious state (MCS), however, might be very complicated, reaching a misdiagnosis of approximately 40% if clinical scales are not carefully administered and continuously repeated. To improve diagnostic accuracy for those patients, tools such as electroencephalography (EEG) might be used in the clinical setting. Many linear indices have been developed to improve the diagnosis in DOC patients, such as spectral power in different EEG frequency bands, spectral power ratios between these bands, and the difference between eyes-closed and eyes-open conditions (i.e. alpha-blocking). On the other hand, much less has been explored using nonlinear approaches. Therefore, in this work, we aim to discriminate between MCS and VS groups using a nonlinear method called Higuchi’s Fractal Dimension (HFD) and show that HFD is more sensitive than linear methods based on spectral power methods. For the sake of completeness, HFD has also been tested against another nonlinear approach widely used in EEG research, the Entropy (E). To our knowledge, this is the first time that HFD has been used in EEG data at rest to discriminate between MCS and VS patients. A comparison of Bayes factors found that differences between MCS and VS were 11 times more likely to be detected using HFD than the best performing linear method tested and almost 32 times with respect to the E. Machine learning has also been tested for HFD, reaching an accuracy of 88.6% in discriminating among VS, MCS and healthy controls. Furthermore, correlation analysis showed that HFD was more robust to outliers than spectral power methods, showing a clear positive correlation between the HFD and Coma Recovery Scale-Revised (CRS-R) values. In conclusion, our work suggests that HFD could be used as a sensitive marker to discriminate between MCS and VS patients and help decrease misdiagnosis in clinical practice when combined with commonly used clinical scales.