Archivio della ricerca della Scuola Superiore Sant'Anna
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Simulation of Direct Reduction Processes to be included in a process chain multipurpose simulation toolkit
No full textGrasping Task in Teleoperation: Impact of Virtual Dashboard on Task Quality and Effectiveness
No full textThis research study investigates the impact of a virtual dashboard on the quality of task execution in robotic teleoperation. More specifically, this study investigates how a virtual dashboard improves user awareness and grasp precision in a teleoperated pick-and-place task by providing users with critical information in real-time. An experiment was conducted with 30 participants in a robotic teleoperated task to measure their task performance in two different experimental conditions: a control group used conventional interfaces, and an experimental group utilized the virtual dashboard with additional information. Research findings indicate that integrating a virtual dashboard improves grasping accuracy, reduces user fatigue, and speeds up task completion, thereby improving task effectiveness and the quality of the experience
The implementation of the Decentralized Autonomous Organizations in the EU corporate governance system
No full textSynapNet: A Complementary Learning System Inspired Algorithm With Real-Time Application in Multimodal Perception
Get PDFCatastrophic forgetting is a phenomenon in which a neural network, upon learning a new task, struggles to maintain its performance on previously learned tasks. It is a common challenge in the realm of continual learning (CL) through neural networks. The mammalian brain addresses catastrophic forgetting by consolidating memories in different parts of the brain, involving the hippocampus and the neocortex. Taking inspiration from this brain strategy, we present a CL framework that combines a plastic model simulating the fast learning capabilities of the hippocampus and a stable model representing the slow consolidation nature of the neocortex. To supplement this, we introduce a variational autoencoder (VAE)-based pseudo memory for rehearsal purposes. In addition by applying lateral inhibition masks on the gradients of the convolutional layer, we aim at damping the activity of adjacent neurons and introduce a sleep phase to reorganize the learned representations. Empirical evaluation demonstrates the positive impact of such additions on the performance of our proposed framework; we evaluate the proposed model on several class-incremental and domain-incremental datasets and compare it with the standard benchmark algorithms, showing significant improvements. With the aim to showcase practical applicability, we implement the algorithm in a physical environment for object classification using a soft pneumatic gripper. The algorithm learns new classes incrementally in real time and also exhibits significant backward knowledge transfer (KT)
Assessing Sustainable and Healthy Diets in Large-Scale Surveys: Validity and Applicability of a Dietary Index Based on a Brief Food Group Propensity Questionnaire Representing the EAT-Lancet Planetary Health Diet
Get PDFBackground
Ensuring healthy diets within planetary boundaries is essential. However, current instruments measuring adherence to the EAT-Lancet planetary health diet are unsuitable for large-scale surveys. Simplified tools assessing consumption frequency can improve response rates, lower costs, and facilitate administration.
Objectives
This study aimed to develop a practical and concise index for evaluating relative adherence to the EAT-Lancet diet across large-scale multicountry surveys.
Methods
First, the EAT-Lancet Consumption Frequency Index (ELFI) was developed using a brief food propensity questionnaire of 14 food groups representing the planetary health diet from the Food systems that support transitions to hEalthy And Sustainable dieTs survey, which encompassed 27 European countries (n = 27,417). Subsequently, ELFI was further validated using 24-h dietary recalls from the Third French Individual and National Food Consumption Survey (n = 1645), correlating it with the valid EAT-Lancet Index (ELI), which evaluates absolute adherence, as well as with food group consumption, measures of nutritional health (nutrient adequacy and diet quality), and environmental impact. Analyses included assessment of reliability, structural validity, concurrent validity, and nomological validity.
Results
ELFI showed strong reliability (α > 0.80) and factor analysis revealed a 2-factor solution: “foods to encourage” and “foods to balance and to limit.” Confirmatory factor analysis demonstrated that ELFI is structurally valid. Concurrent validity was confirmed as it was associated with sex, age, education, income, household size, physical activity, and smoking habit (P < 0.05). ELFI correlated with ELI (0.44, P < 0.0001) and food group consumptions. Regarding nomological validity, the ELFI subscores for “foods to encourage” and “foods to balance and to limit” were associated with better nutritional health (β = 0.62 and 0.23, respectively; P < 0.0001) and a lower environmental impact (β = –0.16 and –0.36, respectively; P < 0.0001).
Conclusions
ELFI approach represents a valuable and easy-to-implement index for evaluating relative adherence to sustainable and healthy diets in large-scale multicountry studies
