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In this paper, we consider integrating Visible Light Communication (VLC) and WiFi technologies in an ultra-dense (massive) Internet of Things scenario, where WiFi links are used for complementing VLC links and vice-versa. Indeed, in case of mobility and/or bidirectional communications, limited coverage areas and self-generated interference can be an issue. We consider hybrid VLC/WiFi nodes where the VLC link is used for the majority of (data) traffic and the WiFi link is employed as a feedback channel (e.g. transmitting TCP ACKs). In presence of intense WiFi traffic, the WiFi network may be congested and thus, to fully exploit the VLC bandwidth, we discuss possible priority mechanisms to fully exploit the VLC link. Simulations in NS-3 demonstrate the effectiveness of the proposed solutions &nbsp

In this paper we describe a deep learning model based on a Data Augmentation (DA) layer followed by a Convolutional Neural Network (CNN). The proposed model was developed by our team for the Profiling Irony and Stereotype Spreaders (ISSs) task proposed by the PAN 2022 organizers. As a first step, to classify an author as ISS or not (nISS), we developed a DA layer that expands each sample in the dataset provided. Using this augmented dataset we trained the CNN. Then, to submit our predictions, we apply our DA layer on the samples within the unlabeled test set too. Finally we fed our trained CNN with the augmented test set to generate our final predictions. To develop and test our model we used a 5-fold cross validation on the labelled training set. The proposed model reaches a maximum accuracy of 0.92 and an average accuracy of 0.89 over the five folds. Meanwhile, on the provided test set the proposed model reaches an accuracy of 0.9278.

Context: Tracheotomy is used to replace endotracheal intubation in patients requiring prolonged ventilation; however, there is considerable variability in the time considered optimal for performing tracheotomy. This is of clinical importance because timing is a key criterion for performing a tracheotomy and patients who receive one require a large amount of health care resources. Objective: To determine the effectiveness of early tracheotomy (after 6-8 days of laryngeal intubation) compared with late tracheotomy (after 13-15 days of laryngeal intubation) in reducing the incidence of pneumonia and increasing the number of ventilator-free and intensive care unit (ICU)-free days. Design, Setting, and Patients: Randomized controlled trial performed in 12 Italian ICUs from June 2004 to June 2008 of 600 adult patients enrolled without lung infection, who had been ventilated for 24 hours, had a Simplified Acute Physiology Score II between 35 and 65, and had a sequential organ failure assessment score of 5 or greater. Intervention: Patients who had worsening of respiratory conditions, unchanged or worse sequential organ failure assessment score, and no pneumonia 48 hours after inclusion were randomized to early tracheotomy (n=209; 145 received tracheotomy) or late tracheotomy (n=210; 119 received tracheotomy). Main Outcome Measures: The primary endpoint was incidence of ventilator-associated pneumonia; secondary endpoints during the 28 days immediately following randomization were number of ventilator-free days, number of ICU-free days, and number of patients in each group who were still alive. Results: Ventilator-associated pneumonia was observed in 30 patients in the early tracheotomy group (14%; 95% confidence interval [CI], 10%-19%) and in 44 patients in the late tracheotomy group (21%; 95% CI, 15%-26%) (P=.07). During the 28 days immediately following randomization, the hazard ratio of developing ventilator-associated pneumonia was 0.66 (95% CI, 0.42-1.04), remaining connected to the ventilator was 0.70 (95% CI, 0.56-0.87), remaining in the ICU was 0.73 (95% CI, 0.55-0.97), and dying was 0.80 (95% CI, 0.56-1.15). Conclusion: Among mechanically ventilated adult ICU patients, early tracheotomy compared with late tracheotomy did not result in statistically significant improvement in incidence of ventilator-associated pneumonia. Trial Registration: clinicaltrials.gov Identifier: NCT00262431. ©2010 American Medical Association. All rights reserved.

The paper deals with voltage stability assessment in radial distribution networks. The voltage collapse proximity index already utilized in literature for power transmission systems is assumed. Based on the optimal impedance solution of a two-bus equivalent system, this index indicates how far the load nodes of the actual network are from their voltage collapse points, allowing the weak node and its maximum load power beyond which voltage collapse takes place to be identified. The proposed method differs from the other methods on the determination of the parameters which define the two-bus equivalent of the network, allowing to better identify the maximum loading point beyond which the voltage collapse takes place in the network. Simulation results show that the proposed method is significantly more accurate than other existing methods on evaluating the critical power at a particular node (i.e. the weak node of the network) starting from any operating point, with smaller errors between actual and predicted values.

This paper deals with the command of inverters in DG (distributed generation) systems by use of linear neural networks in such a way that, with a slight upgrade of their control software, they can be used also to compensate for the harmonic distortion in the node where they are connected (local compensation), that is in the in the point of common coupling (PCC). To this purpose a neural estimator based on linear neurons (ADALINEs) has been developed which is able to act as a selective noise cancellers for each harmonic of the node voltage. The use of linear neurons permits the drawbacks of classical neural networks to be overcome and moreover the neural estimator is easy to implement, thus allowing the same inverter to be used also for active filtering. The paper presents and discusses the results obtained by simulation of a small size distribution network, which in itself contains the distorted voltages coming both from the utility and from a nonlinear load and an inverter supplied by an array of photovoltaic panels with its DC/DC internal converter.