1,720,999 research outputs found
Catalepton : Priapea et epigrammata
No full textMarca tip. na portNa portada Maecenas Priapeum Quid hoc novi es
Aeneidos
No full textMarca tip. nas portNas port. Editio altera ad exemplum editionis romanae (MCMXXX) emendataT. I XIX, 121 p.; T. II 134 p.; T. III 131 pAnte
Reti di sensori wireless per il monitoraggio di una zona in frana.
No full textLe recenti innovazioni nel campo dell’elettronica e della tecnologia dell’informazione hanno reso disponibili sensori avanzati e a basso costo finalizzati al monitoraggio del territorio. In particolare, le reti di sensori wireless (Wireless Sensor Network, WSN) ed i sensori per il telerilevamento Lidar e iperspettrale (Airborne Terrain Mapping, ATM) stanno attirando l’interesse degli enti territoriali per la loro vocazione a monitorare in modo distribuito ambienti naturali ostili quali siti contaminati, zone sismicamente attive e aree in frana (Polastre, 2003; Martinez et al., 2005; Werner-Allen et al., 2006).
Nell’ambito del progetto WISELAND (Ministero dell’Università e della Ricerca Scientifica, finanziamento PRIN2007; http://prin07.geomin.unibo.it/prin07/index.html) sono state combinate le tecnologie WSN e ATM per il monitoraggio di una zona franosa ad elevata pericolosità (frana di Silla-Montecchi, Gaggio Montano, BO). Il progetto prevede di restituire i dati della rete WSN (spostamento del corpo di frana, condizioni idrauliche del versante) su una piattaforma ArcSDE/ArcIMS utilizzando come base un DEM ad alta risoluzione ottenuto dal rilievo Lidar. L’integrazione tra sensori di nuova generazione e tecnologie GIS ha lo scopo di promuovere la diffusione dei dati di monitoraggio e di migliorarne la condivisione con i soggetti istituzionali
The need of raw physiological data for more comprehensive pain studies
No full textPain assessment and management are essential components of patient care, yet the relationship between physiological variables and pain levels is not fully explored. This study leverages the Medical Information Mart for Intensive Care (MIMIC-IV-ED) database to investigate the correlation between various physiological variables and pain levels using machine learning techniques. Our findings suggest that currently available aggregated data may not be sufficient to accurately predict pain levels. This highlights the critical need for incorporating raw physiological data, such as continuous waveforms, to capture the complex relationship between pain and physiological parameters. Specifically, our study emphasizes the potential value of analyzing raw heart rate variability (HRV) data, as well as detailed electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. By focusing on raw data, one can extract more nuanced features that may be lost in summarized or averaged measurements. For example, subtle changes in ECG morphology or beat-to-beat variability in PPG signals could provide valuable insights into pain levels that are not apparent in standard vital sign readings. The integration of raw physiological data in pain research is crucial to address the current scarcity of comprehensive pain studies. This approach has the potential to significantly improve pain assessment accuracy and develop more effective pain management strategies. Additionally, the analysis of raw data could lead to the discovery of novel biomarkers or patterns associated with pain, improving our understanding of pain mechanisms. While emphasizing the importance of raw data, our study also raises important ethical and philosophical questions about the use of technology in pain assessment and its potential impact on patient care, equity, and decision-making in healthcare. It explores the broader implications of advanced pain monitoring on society and individuals’ lifestyles, considering the existing bias in pain management within the medical field. In conclusion, we believe that is a need for a paradigm shift in pain research, advocating for the widespread collection and analysis of raw physiological data. This approach promises to provide a more comprehensive and accurate understanding of pain, ultimately leading to improved assessment tools and personalized pain management strategies
Atrial Fibrillation Detection by Means of Edge Computing on Wearable Device: A Feasibility Assessment
No full textCardio Vascular Diseases (CVDs) represent one of the main burden that affected world population in the last and in the current decades. The early detection by means of wide screening population-wide may represent a good path to avoid the worsening of pre-existent situation. In this arena, the use of wearable devices in combination with deep learning to deliver edge computing system seems to be the most viable pathway to follow in order to fight the CVDs burden. Despite the fact that many studies have concentrated on edge computing techniques for CVDs, there is a limited literature on Atrial Fibrillation (AF) detection directly on-device. Due to limited availability of research on this topic, the feasibility assessment of an on-device edge computing wearable system is described in this work. Starting with an examination of the features to be considered, the study progresses through the building of a Neural Network (NN), the training of the model, and the on-cloud testing process to completion. The NN is composed of 4 hidden layer made up of respectively 5, 30, 20 and 10 node. The learning rate is 0.005 and the number of training cycle is 30. The training set consists of 3362 windows, and the testing set consists of 796 windows. The findings of the test are encouraging, with an output F1-score of 0.94 for AF recognition as a result of the test. The model is then deployed on-device and evaluated offline, without the need for any additional devices or an internet connection, in order to run the inference process. Finally, the system that will be used for future human trials is presented, together with a description of the factors that led to the selection of this particular system and the major characteristic of the sensors
Localization of protein kinase C in skeletal muscle T-tubule membranes
No full textMembrane fractions enriched in transverse tubules, either predominantly free or junctional, sarcoplasmic reticulum subfractions and purified sarcolemmal preparations have been isolated from rabbit skeletal muscle and examined for their contents of protein kinase C. Using activity measurements and immunoblotting methods, we have been able to detect substantial amounts of endogenous protein kinase C in T-tubules membranes and to a lesser extent, in muscle sarcolemma. Protein kinase C was found to be highest in junctional T-tubules and to be virtually absent from sarcoplasmic reticulum-derived membrane fractions. Immunofluorescence staining of muscle fibers is consistent with a T-tubule localization of the kinase. The T-tubule-associated protein kinase C enzyme phosphorylates several potentially important membrane proteins
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