Publications

Background: Re-intubation secondary to post-extubation respiratory failure in post-operative patients is associated with increased patient morbidity and mortality. Non-invasive respiratory support (NRS) alternative to conventional oxygen therapy (COT), i.e., high-flow nasal oxygen, continuous positive airway pressure, and non-invasive ventilation (NIV), has been proposed to prevent or treat post-extubation respiratory failure. Aim of the present study is assessing the effects of NRS application, compared to COT, on the re-intubation rate (primary outcome), and time to re-intubation, incidence of nosocomial pneumonia, patient discomfort, intensive care unit (ICU) and hospital length of stay, and mortality (secondary outcomes) in adult patients extubated after surgery. Methods: A systematic review and network meta-analysis of randomized and non-randomized controlled trials. A search from Medline, Embase, Scopus, Cochrane Central Register of Controlled Trials, and Web of Science from inception until February 2, 2024 was performed. Results: Thirty-three studies (11,292 patients) were included. Among all NRS modalities, only NIV reduced the re-intubation rate, compared to COT (odds ratio 0.49, 95% confidence interval 0.28; 0.87, p = 0.015, I2 = 60.5%, low certainty of evidence). In particular, this effect was observed in patients receiving NIV for treatment, while not for prevention, of post-extubation respiratory failure, and in patients at high, while not low, risk of post-extubation respiratory failure. NIV reduced the rate of nosocomial pneumonia, ICU length of stay, and ICU, hospital, and long-term mortality, while not worsening patient discomfort. Conclusions: In post-operative patients receiving NRS after extubation, NIV reduced the rate of re-intubation, compared to COT, when used for treatment of post-extubation respiratory failure and in patients at high risk of post-extubation respiratory failure.

Neural Radiance Fields (NeRFs) have shown impressive results for novel view synthesis when a sufficiently large amount of views are available. When dealing with few-shot settings, i.e. with a small set of input views, the training could overfit those views, leading to artifacts and geometric and chromatic inconsistencies in the resulting rendering. Regularization is a valid solution that helps NeRF generalization. On the other hand, each of the most recent NeRF regularization techniques aim to mitigate a specific rendering problem. Starting from this observation, in this paper we propose CombiNeRF, a framework that synergically combines several regularization techniques, some of them novel, in order to unify the benefits of each. In particular, we regularize single and neighboring rays distributions and we add a smoothness term to regularize near geometries. After these geometric approaches, we propose to exploit Lipschitz regularization to both NeRF density and color networks and to use encoding masks for input features regularization. We show that CombiNeRF outperforms the state-of-the-art methods with few-shot settings in several publicly available datasets. We also present an ablation study on the LLFF and NeRF-Synthetic datasets that support the choices made. We release with this paper the open-source implementation of our framework.

Interactive multimedia art shows a complex nature as it is time- and process-based, interconnected with technology, derived by the participation of several authors (artists, technicians, performers, to name a few) and an audience, and it is strongly tied to the moment and space of the original exhibition. These characteristics make the preservation of these artworks a multifaceted process. Building on the foundations developed since the 2000s by international projects focused on preserving and restoring these new art forms, the paper proposes an original model for achieving dynamic preservation, called “the multilevel preservation model”. Since it is no longer possible to guarantee physical integrity for interactive multimedia artworks, dynamic preservation involves recording all the changes that occur to display the artworks in the future. In other words, it makes it possible to record the dynamic authenticity of artworks. The model proposes two fundamental properties: multiple layering, which allows to handle different levels of information about the artwork; multiple belongingness, which allows to represent the dynamic authenticity of the artwork at the archival level and achieving dynamic preservation.The paper demonstrates the high-level implementation of the model by presenting a case study: the reactivation and preservation of an analog video art performance from the 1970s. This case study proposes an interesting real scenario for testing the model, as the reactivation process involved the migration of the entire analog technological apparatus of the artwork into the digital domain.

Introduction: Oral chlorhexidine has been widely used for ventilator-associated pneumonia prevention in the critical care setting; however, previous studies and evidence synthesis have generated inconsistent findings. Our study aims to investigate if different concentrations of oral chlorhexidine may be effective in preventing such complication in intensive care unit patients. Methods: After pre-registration (Open Science Framework: 8CUKF), we conducted a network meta-analysis with the following PICOS: adult patients (age > 18 years old) undergoing invasive mechanical ventilation admitted in ICU (P); any concentration of chlorhexidine used for oral hygiene (I); placebo, sham intervention, usual care, or no intervention (C); rate of VAP (primary outcome), mechanical ventilation length, ICU length of stay (LOS), hospital LOS, mortality (secondary outcomes) (O); randomized controlled trials (S). We used the following database: PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, and EMBASE without any limitation in publication date or language. Results: Chlorhexidine did not demonstrate any significant advantage over the control group in preventing ventilator-associated pneumonia or reducing mortality, duration of mechanical ventilation, length of stay in the intensive care unit, or overall mortality. Conclusions: Chlorhexidine oral decontamination does not reduce the rate of ventilator-associated pneumonia in critically ill adult patients and its routine use could not be recommended. Trial registration: Registration number: Open Science Framework: 8CUKF.

The preservation of open-reel audio tapes is critical for maintaining valuable cultural and historical audio archives, yet current digitisation and analysis operations are often error-prone due to tape degradation and the long duration of the recordings. Considering the analog nature of this kind of recording, anomaly detection algorithms, applied to the video of the tape flowing on the playback head, can be used to detect errors and details with musicological value. This paper presents a new dataset of high-quality videos and a new algorithm for anomaly detection on audio tapes. Experimental results show notable improvements in detection performance, though false positives remain a challenge at higher speeds. Additionally, the new algorithm supports a wider range of playback speeds, improving its flexibility. This improvement is an important step towards a reliable implementation of the IEEE/MPAI CAE ARP standard (3302-2022).

This paper introduces a custom-built low-cost camera ring device designed for automatic cast synthesis, able to accurately and instantly scan body parts. The scanned mesh will be used as a backbone model for the cast design and 3D printing. The system is based on the multi-view active stereo principle and it is composed of a circular array of 16 synchronized cameras (Fig. 1) and 4 equally distributed IR pseudo-random laser pattern projectors. We employ a custom multi-view stereo reconstruction pipeline based on (Schönberger et al., 2016), which guarantees optimal results without the downsides of the supervised data-driven multi-view stereo algorithms, i.e. data collection and ground truth labeling. Additionally, inspired by (Duda and Frese, 2018), we propose a novel, automated calibration system to extract intrinsic and extrinsic camera parameters which are required to perform robust multi-view stereo reconstructions.

The ability to perceive traversable regions is a crucial prerequisite for truly autonomous mobile systems. In our previous work [8], we introduced P-SVM, a 3D LiDAR-based traversability analysis system that achieves real-time performance in CPU. However, by design P-SVM does not capture features other than geometric features, limiting its performance and generalizability to complex environments. In this work, we introduce an augmented point cloud descriptor that further improves the performance of P-SVM. By exploiting additional features extracted from remission and height information in the point cloud, our model is able to adapt robustly to strong scene changes. Additionally, in the proposed descriptor, remission radial distribution features are introduced to capture local information around keypoints. This addresses the limitation of P-SVM, which focuses only on global features within the cell. Our new P-SVM2 model demonstrates performance almost on par with state-of-the-art deep learning-based methods on the challenging SemanticKITTI [1] dataset in the traversability analysis task. Notably, P-SVM2 is real-time and relies solely on mobile-level CPUs. Moreover, surprising results have been obtained in robustness and generalizability experiments.

The precision livestock farming (PLF) has the objective to maximize each animal’s performance while reducing the environmental impact and maintaining the quality and safety of meat production. Among the PLF techniques, the personalised management of each individual animal based on sensors systems, represents a viable option. It is worth noting that the implementation of an effective PLF approach can be still expensive, especially for small and medium-sized farms; for this reason, to guarantee the sustainability of a customized livestock management system and encourage its use, plug and play and cost-effective systems are needed. Within this context, we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera. By leveraging the current state-of-the-art methods for real-time object detection, (i.e., YOLOv3) cattle’s face areas, we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker. The cow IDs are read by an Optical Character Recognition (OCR) algorithm for which, an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs. Thanks to the detection of the tag position, the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed. Activity times for the areas are outputted as cattle activity recognition results. Evaluation results demonstrate the effectiveness of our proposed method, showing a mAP@0.50 of 89%.