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Overtopping Assessment of a Rubble Mound Breakwater with Innovative Armor Units: A Physical and Numerical Study
No full textExploring rhythmic pattern in blind runners: a pilot study
No full textBACKGROUND: This is an observational pilot study comparing kinematic variables of running between blind athletes and sighted athletes. Aim was to verify significant differences in the rhythmic pattern of fast running between male sighted and blind athletes, with similar anthropometric parameters and performance skills. METHODS: Four blind athletes (aged 25±4.25) classified in T11 blindness category and four sighted athletes (aged 24.2±1.29) were tested on 60 meters running, divided into two segments: the first 30 meters referred to the "acceleration phase," and the second 30 meters to the "maintenance phase" following the modified "Vittori model" to evaluate the running pace and stride length and comparing with those of sight athletes. RESULTS: Significant difference between groups on the number of steps in the maintenance phase, with the blinds showing a significant higher number of steps (P value=0.027), and a borderline significance over 60 meters between the two groups (P=0.063). CONCLUSIONS: Increased fatigue due to visual impairment or the need for more frequent ground contact and therefore proprioceptive input can have induced these adjustments. This strategy may be useful from both a safety and performance standpoint
A Computer-Aided Screening Solution for the Identification of Diabetic Neuropathy from Standing Balance by Leveraging Multi-Domain Features
No full textThe early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease. We considered a cohort of non-neuropathic (NN), asymptomatic neuropathic (AN), and symptomatic neuropathic (SN) diabetic patients. From the center of pressure (COP), a series of features belonging to different description domains were extracted. In order to exploit the whole information retrievable from COP, a majority voting ensemble was applied to the output of classifiers trained separately on different COP components. The ensemble of kNN classifiers provided over 86% accuracy for the first diagnosis pathway, made by a 3-class classification task for distinguishing between NN, AN, and SN patients. The second pathway offered higher performances, with over 97% accuracy in identifying patients with symptomatic and asymptomatic neuropathy. Notably, in the last case, no asymptomatic patient went undetected. This work showed that properly leveraging all the information that can be mined from COP trajectory recorded during standing balance is effective for achieving reliable DN identification. This work is a step toward a clinical tool for neuropathy diagnosis, also in the early stages of the disease
Stati di crisi
No full textLa presente raccolta di interventi che nell’arco di tre giornate hanno anima-
to il Seminario DSUS 2023 – promosso dalla Commissione Ricerca del di-
partimento e organizzato dalle ricercatrici e ricercatori che vi afferiscono –
esprime la necessità di trattare di “stati di crisi” senza limitarsi a registrarne
l’idea della ciclicità, il suo manifestarsi ed essersi manifestata, storicamente,
anche come una sorta di assestamento interno del sistema, in ambito eco-
nomico, giuridico, culturale, storico-politico.
L’idea di fondo è quella di provare a ricavare domande radicali da ciascuno
degli eventi che costellano ciò che siamo portati a definire gli attuali “stati di
crisi”. Domande cioè che puntino a individuare quale ruolo e quali forme può
assumere l’analisi critica oltre che specializzata dei dispositivi che costitui-
scono la premessa e l’occasione di innesco di uno “stato di crisi” oggi.
In altre parole, ciò che si impone, date le mutate condizioni spaziali e tempo-
rali della nostra tarda modernità neoliberale e tecnicizzata, è un’attenzione
particolare ai singoli avvenimenti e al loro contesto di significato che, se pure
non generalizzabile, deve poter essere oggetto di una valutazione critica che
possa anche suggerire strategie condivise di risposta. Sul piano storico, psi-
co-sociale, antropologico, filosofico, giuridico-economico, ma anche simboli-
co, in vista di mutamenti e nuove prospettive locali e globali per le istituzioni
e per il vivere comune
Exploring the complexity of EEG patterns in Parkinson’s disease
No full textParkinson’s disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13–20 Hz) and beta 2 (20–30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning
Counterfactual-Based Feature Importance for Explainable Regression of Manufacturing Production Quality Measure
No full textMachine learning (ML) methods need to explain their reasoning to allow professionals to validate and trust their predictions, and employ those in real-world decision-making processes. To do so, explainable artificial intelligence (XAI) methods based on feature importance can be employed, even though those can be very computationally expensive. Moreover, it can be challenging to determine whether an XAI technique might introduce bias into the explanation (e.g., overestimating or underestimating the feature importance) in the absence of some reference feature importance measure or even some domain knowledge from which deriving an expected importance level for each feature. We address both these issues by (i) employing a counterfactualbased strategy, i.e. deriving a measure of feature importance by checking if some minor changes in one feature’s values significantly affect the ML model’s regression outcome, and (ii) employing both synthetic and real-world industrial data coupled with the expected degree of importance for each feature. Our experimental results show that the proposed approach (BoCSoRr) is more reliable and way less computationally expensive than DiCE, a well-known counterfactual-based XAI approach able to provide a measure of feature importance
Frammentazione amministrativa e rischio ambientale: un’analisi del caso dell’isola di Ischia mediante WebGis MTE
No full textUnderestimating the pandemic: The impact of COVID-19 on income distribution in the U.S. and Brazil
No full textThe COVID-19 pandemic has exposed individuals to various risks, including job loss, income reduction, deteriorating well-being, and severe health complications and death. In Brazil and the U.S., as well as in other countries, the initial response to the pandemic was marked by governmental underestimation, leading to inadequate public health measures to curb the spread of the virus. Although progressively mitigated, this approach played a crucial role in the impacts on local populations. Therefore, the principal aim of this paper is to evaluate the impact of COVID-19 and, indirectly, of the policies adopted by the U.S. and the Brazilian governments to prevent pandemic diffusion on income distribution. Utilizing available microdata and employing novel econometric methods (RIF-regression for inequality measures) this study shows that growth in COVID-19 prevalence significantly exacerbates economic disparities. Furthermore, the impact of COVID-19 on inequality has increased over time, suggesting that this negative impact has been intensifying. In the U.S., results indicate that working from home, the inability to work, and barriers to job-seeking significantly increase inequalities. Although further data are necessary to validate the hypothesis, this preliminary evidence suggests that the pandemic has significantly contributed to increased inequality in these two countries already characterized by increasing polarization and significant social disparities
Using Adaptive Surface EMG Envelope Extraction for Onset Detection: A Preliminary Study on Upper Limb Amputees
No full textSurface electromyography is a valid and widely used tool for the characterization and the intention of human movement in healthy and pathological subjects; in particular, the thorough identification of the transient phase of EMG activity is an important problem to be solved for both myoelectric control applications for prosthetics and rehabilitation and in movement analysis in general. Among the possible algorithmic solutions, the ones based on the statistical properties of the signal have been considered able to yield stable performance in a variety of different scenarios. In this paper, an adaptive and statistically optimised algorithm for the extraction of the amplitude envelope is exploited for the onset detection from EMG data coming from the shoulder muscles of two upper limb amputees. In particular, onset events have been detected from the optimised point-by-point window length of the adaptive filter, as the instants in which this time series reaches a local minimum, and compared with those coming from visual inspection of accelerometer data from the shoulder. These preliminary results show how using such techniques can yield acceptable performance, supporting the hypothesis of exploiting such an algorithm for the improvement of the performance of myoelectric control algorithms to be applied in clinical contexts
