1,721,249 research outputs found
Un modello sistemico sulla motivazione al lavoro: la verifica empirica del "Modello Calendario"
No full textLa presente ricerca mira a verificare empiricamente il Modello Calendario della motiovazione al lavoro elaborato da Roe (1999), che suddivide il processo di azione volto al raggiungimento degli obiettivi in cinque differenti fasi sequenziali: generazione, filtraggio, programmazione, mantenimento ed esecuzione. Ad un campione di 235 soggetti, aventi ruoli dirigenziali e gestionali è stato somministrato un questionario costruito ad hoc, costituito da un insieme di item strutturati volti ad operazionalizzare ed indagare ciascuna delle cinque fasi. Il Modello Calendario è stato testato attraverso un modello di equazione strutturale i cui indici hanno rilevato un buon livello di attendibilità e parsimoniosità del modello teorico ipotizzato. Lo studio del modello è stato integrato, analizzando la rilevanza e l'influenza esercitata da due caratteristiche personali sulle cinque fasi del modello. L'introduzione delle dimensioni: proattività e self efficacy ha permesso di verificare un secondo modello teorico con buoni risultati statistici che testimoniano l'influenza specifica di queste due dimensioni di personalità su alcune fasi del modello, in particolare su generazione, filtraggio, mantenimento ed esecuzione degli obiettivi
The Ninapro database: A resource for sEMG naturally controlled robotic hand prosthetics
No full textThe dexterous natural control of robotic prosthetic hands with non-invasive techniques is still a challenge: surface electromyography gives some control capabilities but these are limited, often not natural and require long training times; the application of pattern recognition techniques recently started to be applied in practice. While results in the scientific literature are promising they have to be improved to reach the real needs. The Ninapro database aims to improve the field of naturally controlled robotic hand prosthetics by permitting to worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark database. Currently, the Ninapro database includes data from 67 intact subjects and 11 amputated subject performing approximately 50 different movements. The data are aimed at permitting the study of the relationships between surface electromyography, kinematics and dynamics. The Ninapro acquisition protocol was created in order to be easy to be reproduced. Currently, the number of datasets included in the database is increasing thanks to the collaboration of several research groups
Control capabilities of myoelectric robotic prostheses by hand amputees: A scientific research and market overview
Full text linkHand amputation can dramatically affect the capabilities of a person. Cortical reorganization occurs in the brain, but the motor and somatosensorial cortex can interact with the remnant muscles of the missing hand even many years after the amputation, leading to the possibility to restore the capabilities of hand amputees through myoelectric prostheses. Myoelectric hand prostheses with many degrees of freedom are commercially available and recent advances in rehabilitation robotics suggest that their natural control can be performed in real life. The first commercial products exploiting pattern recognition to recognize the movements have recently been released, however the most common control systems are still usually unnatural and must be learned through long training. Dexterous and naturally controlled robotic prostheses can become reality in the everyday life of amputees but the path still requires many steps. This mini-review aims to improve the situation by giving an overview of the advancements in the commercial and scientific domains in order to outline the current and future chances in this field and to foster the integration between market and scientific research
Conversational Agents for Energy Awareness and Efficiency: A Survey
Full text linkThe need to reduce greenhouse gas emissions and promote energy efficiency is crucial to achieve the energy transition and sustainable development goals. The availability of tools that provide clear information on energy consumption plays a key role in this transition, enabling users to monitor, manage, and optimize their energy use. This process, commonly referred to as energy feedback or eco-feedback, involves delivering information regarding energy usage and potentially suggesting more sustainable practices. Within the range of available tools, conversational agents can represent a valuable channel to receive detailed information about energy consumption and tailored advice for improving energy efficiency. The aim of this article is thus to explore the application of conversational agents, focusing on eco-feedback, as these tools are primarily devised to foster user awareness of energy usage and enhance more participatory conservation strategies. To this end, we conducted a keyword-based search of major scientific article databases, applying strict criteria to select relevant studies. The results of the collection showed that there is a very diverse landscape with respect to this topic. The surveyed works exhibit a high versatility in feedback goals. Furthermore, while predominantly applied domestically, they also show potential in commercial and industrial settings. Implementation choices also vary to a great extent, while evaluation practices lack a systematic approach and highlight the need for greater consistency. In light of these remarks, we also outline possible future extensions of this type of application, exploring in particular the emerging challenges associated with the increased use of renewable sources and the rise of local decentralized energy communities
Pawfe: Fast signal feature extraction using parallel time windows
Full text linkMotivation: Hand amputations can dramatically affect the quality of life of a person. Researchers are developing surface electromyography and machine learning solutions to control dexterous and robotic prosthetic hands, however long computational times can slow down this process. Objective: This paper aims at creating a fast signal feature extraction algorithm that can extract widely used features and allow researchers to easily add new ones. Methods: PaWFE (Parallel Window Feature Extractor) extracts the signal features from several time windows in parallel. The MATLAB code is publicly available and supports several time domain and frequency features. The code was tested and benchmarked using 1,2,4,8,16,32, and 48 threads on a server with four Xeon E7-4820 and 128 GB RAM using the first 5 datasets of the Ninapro database, that are recorded with different acquisition setups. Results: The parallel time window analysis approach allows to reduce the computational time up to 20 times when using 32 cores, showing a very good scalability. Signal features can be extracted in few seconds from an entire data acquisition and in 100ms from a single time window, easily reducing of up to over 15 times the feature extraction procedure in comparison to traditional approaches. The code allows users to easily add new signal feature extraction scripts, that can be added to the code and on the Ninapro website upon request. Significance: The code allows researchers in machine learning and biosignals data analysis to easily and quickly test modern machine learning approaches on big datasets and it can be used as a resource for real time data analysis too
Applications of Self-Supervised Learning to Biomedical Signals: A Survey
Full text linkOver the last decade, deep learning applications in biomedical research have exploded, demonstrating their ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models for biomedical applications requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive. Self-Supervised Learning (SSL) has emerged as a prominent solution for such problems, as it allows learning powerful representations from vast unlabeled data by producing supervisory signals directly from the data. The high number of recent works employing the self-supervised learning paradigm for the analysis of biomedical signals (biosignals) can make it difficult for researchers to have a complete picture of the current research state. Therefore, this paper aims at outlining and clarifying the state-of-the-art in the domain. The article: briefly summarizes the nature and acquisition modality of the main biosignals; introduces the self-supervised learning method, focusing on the different pretraining strategies; provides a concise overview of the works employing SSL for the analysis of different types of biosignals; provides an overall analysis of critical aspects to consider when employing SSL to biosignals, also highlighting current open challenges. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve models' performance and robustness, and to promote the integration of deep learning into clinical tasks
Credenze e riti magici in Sardegna. Dalla religione alla magia
No full textSuperstizioni e credenze magiche
funzionavano, in un contesto agro pastorale, come il completamento di una fede che non era riuscita a realizzarsi in un modello persuasivo di vita sociale
Postsynaptic induction of mossy fibre long term depression in developing rat hippocampus
No full textThe whole cell configuration of the patch clamp technique was used to study the mechanisms of induction of long term depression (LTD) occurring at the mossy fibre-CA3 synapse between postnatal (P) day 6 and P13. In control conditions, when two pulses were delivered to the mossy fibres with an interval of 50 ms a potentiation of the EPSC evoked by the second pulse associated with a reduction in the number of failures was observed. Tetanization of the mossy fibres induced LTD of the responses to the first and second stimulus without affecting the paired pulse facilitation. Loading the postsynaptic cell with BAPTA prevented the induction of LTD but did not modify the paired pulse facilitation, suggesting that LTD induction occurs at the postsynaptic site
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