Development of Neural Network Prediction Models for the Energy Producibility of a Parabolic Dish: A Comparison with the Analytical Approach

Solar energy is one of the most widely exploited renewable/sustainable resources for electricity generation, with photovoltaic and concentrating solar power technologies at the forefront of research. This study focuses on the development of a neural network prediction model aimed at assessing the energy producibility of dish–Stirling systems, testing the methodology and offering a useful tool to support the design and sizing phases of the system at different installation sites. Employing the open-source platform TensorFlow, two different classes of feedforward neural networks were developed and validated (multilayer perceptron and radial basis function). The absolute novelty of this approach is the use of real data for the training phase and not predictions coming from another analytical/numerical model. Several neural networks were investigated by varying the level of depth, the number of neurons, and the computing resources involved for two different sets of input variables. The best of all the tested neural networks resulted in a coefficient of determination of 0.98 by comparing the predicted electrical output power values with those measured experimentally. The results confirmed the high reliability of the neural models, and the use of only open-source IT tools guarantees maximum transparency and replicability of the models

Bedside communication and management of vital parameters and alarms in care-intensive environments: Simulation model development for the clinical effectiveness analysis of an innovative technology

Background and Objective. The deliberation n.7301 of 31/12/2001 provides for the inclusion of a call system with acoustic and luminous signalling within the minimum equipment of the recovery ward. However, traditional call systems are inefficient since they are based on the following incorrect assumptions: patients and staff are unmoving, information sources are static and assistance is unidirectional. Taking care of a patient involves different figures who should be dynamic and should be able to exchange information. Furthermore, the high number of clinical calls and alarms might be an issue, because on one hand they are essential to fulfil patients' needs, but on the other hand they could cause stress and additional workload on medical staff. Indeed, they sometimes ignore some calls or waste a lot of time on non-urgent requests. In addition, the identification of an alarm and the prompt intervention seems to be more difficult during travelling. An ideal alarm system should have 100% sensitivity and specificity. Nevertheless, the alarms are designed to be extremely sensitive, at the expense of specificity. The alarm fatigue, that is the work overload due to an excessive alarms number exposition, is a critical problem in terms of safety in the current clinical practice because it involves desensitization and alarm loss, causing sometimes even the patient's death. Material and Methods. Therefore, appropriate approaches to notifications should be evaluated, including the effectiveness of mobile wireless technologies: linking patients, staff, data, services and medical devices simplifies communications and workflows. Several issues related to the communication among staff members, between patient and caregiver and to the alarms and vital parameters distribution in care-intensive environments have been analysed, focusing on the clinical effectiveness analysis of an innovative technology to support the Emergency Department of the Azienda Ospedaliera dei Colli activities. Afterwards, we have created a simulation model with Simul8, so that a digital twin reproduces direct and indirect activities in two cases: with and without (What If and As Is model) the aid of the technology. Results and conclusions. The model provides a set of Key Performance Indicators (number of performing activities, average alarm resolution time, waiting time) on which the compensatory aggregation method is applied to elaborate a single final score in both cases. This score is 52,5 in the As Is Model and 80 in the What If model. So, the clinical effectiveness has been demonstrated

Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification

The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffic Classification (TC). However, to tame the dependency from task-specific large labeled datasets we need to find better ways to learn representations that are valid across tasks. In this work we investigate this problem comparing transfer learning, meta-learning and contrastive learning against reference Machine Learning (ML) tree-based and monolithic DL models (16 methods total). Using two publicly available datasets, namely MIRAGE19 (40 classes) and AppClassNet (500 classes), we show that (i) using large datasets we can obtain more general representations, (ii) contrastive learning is the best methodology and (iii) meta-learning the worst one, and (iv) while ML tree-based cannot handle large tasks but fits well small tasks, by means of reusing learned representations, DL methods are reaching tree-based models performance also for small tasks.Comment: to appear in Traffic Measurements and Analysis (TMA) 202

Assessing the Energy-Saving Potential of a Dish-Stirling Concentrator Integrated into Energy Plants in the Tertiary Sector

Energy consumed for air conditioning in residential and tertiary sectors accounts for a large share of global use. To reduce the environmental impacts burdening the covering of such demands, the adoption of renewable energy technologies is increasing. In this regard, this paper evaluates the energy and environmental benefits achievable by integrating a dish-Stirling concentrator into energy systems used for meeting the air conditioning demand of an office building. Two typical reference energy plants are assumed: (i) a natural gas boiler for heating purposes and air-cooled chillers for the cooling periods, and (ii) a r reversible heat pump for both heating and cooling. For both systems, a dish-Stirling concentrator is assumed to operate first in electric-mode and then in a cogenerative-mode. Detailed models are adopted for plant components and implemented in the TRNSYS environment. Results show that when the concentrator is operating in electric-mode the electricity purchased from the grid decreases by about 72% for the first plant, and 65% for the second plant. Similar reductions are obtained for CO₂ emissions. Even better performance may be achieved in the case of the cogenerative-mode. In the first plant, the decrease in natural gas consumption is about 85%. In the second plant, 66.7% is the percentage increase in avoided electricity purchase. The integration of the dish-Stirling system allows promising energy-saving and reduction in CO₂ emissions. However, both a reduction in capital cost and financial support are needed to encourage the diffusion of this technology

Onion under Microscope: An in-depth analysis of the Tor network

Tor is an anonymity network that allows offering and accessing various kinds of resources, known as hidden services, while guaranteeing sender and receiver anonymity. The Tor web is the set of web resources that exist on the Tor network, and Tor websites are part of the so-called dark web. Recent research works have evaluated Tor security, evolution over time, and thematic organization. Nevertheless, few information are available about the structure of the graph defined by the network of Tor websites. The limited number of Tor entry points that can be used to crawl the network renders the study of this graph far from being simple. In this paper we aim at better characterizing the Tor Web by analyzing three crawling datasets collected over a five-month time frame. On the one hand, we extensively study the global properties of the Tor Web, considering two different graph representations and verifying the impact of Tor's renowned volatility. We present an in depth investigation of the key features of the Tor Web graph showing what makes it different from the surface Web graph. On the other hand, we assess the relationship between contents and structural features. We analyse the local properties of the Tor Web to better characterize the role different services play in the network and to understand to which extent topological features are related to the contents of a service

Clinical impact of COVID-19 on tuberculosis

: During COVID-19 pandemic, a lot of diseases suffered from a limited access to health care services, owing to the use of resources, both technical and financial, mainly directed towards such a dramatic outbreak. Among these, tuberculosis (TB) has been one of the most penalized, with a huge delay both in diagnosis and in start of treatment, with a consequential dramatic increase in morbidity and mortality. COVID-19 and tuberculosis share similar common pathogenetic pathways, and both diseases affect primarily the lungs. About the impact of TB on COVID-19 severity and mortality, data are unclear and literature reports are often conflicting. Certainly, considering the management of coinfected patients, there are pharmacokinetic interactions between several drugs used for the therapy of SARS-CoV-2 infection and the treatment of TB

Inferring urban social networks from publicly available data

The emergence of social networks and the definition of suitable generative models for synthetic yet realistic social graphs are widely studied problems in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts -- including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. Given a territory of interest, and only based on widely available aggregated demographic and social-mixing data, we construct an age-stratified and geo-referenced synthetic population whose individuals are connected by "strong ties" of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size

Alexithymia. a facet of uncontrolled hypertension

Introduction: Proper control of blood pressure reduces the risk of developing cardiovascular and cerebrovascular complications in hypertensive people. However, this control remains mostly unsatisfactory. Although alexithymia has been associated with essential hypertension, no study has analysed the relationship between alexithymia and blood pressure control in drug-treated hypertension. This research aimed to analyse the presence and the characteristics of this relationship, considering both the pharmacological treatment and the achievement of adequate maintenance of blood pressure in a physiological range. Method: One thousand two hundred and forty-one people participated in the study. Eight hundred and ten were hypertensive patients, and four hundred and thirty-one were normotensive people. The Toronto Alexithymia Scale-20 was used to assess alexithymia. Results: Results show that hypertensive people are more alexithymic than normotensive people. According to the presence of pharmacological treatment, treated hypertensive patients are more alexithymic than normotensive and not treated hypertensive patients. Considering the blood pressure control associated with the drug-therapy, people with uncontrolled hypertension are more alexithymic than normotensive and untreated hypertensive people. Conclusions: These findings confirm a relationship between alexithymia and essential arterial hypertension, but they also highlight that alexithymia appears to be associated with higher severity of hypertension. Alexithymia could be a facet of uncontrolled hypertension

Pneumococcal and influenza vaccination rates and their determinants in children with chronic medical conditions

<p>Abstract</p> <p>Background</p> <p>To investigate the rates of pneumococcal and influenza vaccinations and their determinants in children with chronic medical conditions.</p> <p>Patients and Methods</p> <p>Children with HIV infection, cystic fibrosis, liver transplantation and diabetes mellitus were enrolled. Physicians of regional Reference Centres for each condition, primary care paediatricians and caregivers of children provided information through specific questionnaires. For diabetes, 3 Reference Centres were included.</p> <p>Results</p> <p>Less than 25% of children in each group received pneumococcal vaccination. Vaccination rates against influenza were 73% in patients with HIV-infection, 90% in patients with cystic fibrosis, 76% in patients with liver transplantation, and ranged from 21% to 61% in patients with diabetes mellitus. Reference Centres rather than primary care paediatricians had a major role in promoting vaccinations. Lack of information was the main reason for missing vaccination. Awareness of the severity of pneumococcus infection by key informants of at-risk children was associated with higher vaccination rate.</p> <p>Conclusions</p> <p>Vaccination rates in children with chronic conditions were poor for pneumococcus and slightly better for influenza. Barriers to vaccination include lack of awareness, health care and organization problems.</p