Search Results (4)

Information and communication technologies (ICTs) are an integral part of our environment, and their uses vary across generations and among individuals. Today’s student population is made up of “digital natives” who have grown up under the ubiquitous influence of digital technologies, and for whom the use of ICT is common and whose daily activities are structured around media use. The aim of this study is to examine the impact of ICT use and digital skills on students’ academic performance and to explore the digital divide in France. Data were collected through face-to-face questionnaires administered to 1323 students enrolled in three French universities. Principal component analysis, a non-hierarchical k-means clustering approach and multilevel ordered logistic regression were used for data analysis and provide four main findings: first, poor investment in ICT affects students’ results; second, the ICT training offered by universities has little impact on students’ results; third, student performance improves with the innovative and collaborative use of ICTs; fourth, the acquisition of digital skills increases students’ academic performance. The results show that the digital divide still exists, and this raises questions about the effectiveness of education policies in France. They suggest also that organizational change in universities is essential to enable an exploitation of ICT. Full article

The study examined the impact of different factors on greenhouse gas (GHG) emissions, by applying the extended STIRPAT model and decoupling analysis for Tunisia for the period 1990–2018. Furthermore, the study utilizes Tapio decoupling model, and the Auto-Regressive Distributed Lag (ARDL) bounds test approach to examine the relationship between the variables of greenhouse gas (GHG) emissions, economic growth, energy consumption, urbanization, innovation, and trade openness. The findings validated an inverted U-shape relationship between GDP and GHG emissions. In addition, we find that the consumption of renewable energy contributes to the reduction of GHG emissions in the long run. The findings call authority for the adaption of the regulatory framework relating to energy management, energy efficiency and the development of renewable energies, as well as to initiate energy market reforms, implement mitigation strategies and encourage investments in clean energies. Full article

Detecting human motion and predicting human intentions by analyzing body signals are challenging but fundamental steps for the implementation of applications presenting human–robot interaction in different contexts, such as robotic rehabilitation in clinical environments, or collaborative robots in industrial fields. Machine learning techniques (MLT) can face the limit of small data amounts, typical of this kind of applications. This paper studies the illustrative case of the reaching movement in 10 healthy subjects and 21 post-stroke patients, comparing the performance of linear discriminant analysis (LDA) and random forest (RF) in: (i) predicting the subject’s intention of moving towards a specific direction among a set of possible choices, (ii) detecting if the subject is moving according to a healthy or pathological pattern, and in the case of discriminating the damage location (left or right hemisphere). Data were captured with wearable electromagnetic sensors, and a sub-section of the acquired signals was required for the analyses. The possibility of detecting with which arm (left or right hand) the motion was performed, and the sensitivity of the MLT to variations in the length of the signal sub-section were also evaluated. LDA and RF prediction accuracies were compared: Accuracy improves when only healthy subjects or longer signals portions are considered up to 11% and at least 10%, respectively. RF reveals better estimation performance both as intention predictor (on average 59.91% versus the 62.19% of LDA), and health condition detector (over 90% in all the tests). Full article

Human intentions prediction is gaining importance with the increase in human–robot interaction challenges in several contexts, such as industrial and clinical. This paper compares Linear Discriminant Analysis (LDA) and Random Forest (RF) performance in predicting the intention of moving towards a target during reaching movements on ten subjects wearing four electromagnetic sensors. LDA and RF prediction accuracy is compared to observation-sample dimension and noise presence, training and prediction time. Both algorithms achieved good accuracy, which improves as the sample dimension increases, although LDA presents better results for the current dataset. Full article