Towards Environmental Inclusion: Fostering inclusive mobility behaviours through Internet of Things

This poster describes our project about design for sustainable mobility behaviour, facilitated by the Internet of Things. In particular we have based our research on Schultz's integrated cognitive representation of self and other that confirms sustainable behaviours are the consequences of the ecological thinking. In order to foster ecological thinking in the home environment, we have designed a smart key hook that has three essential connected parts with which users interact: digital interface, touch bottoms and key hooks. The digital interface displays the transformations within the ecosystem based on the data and the related metrics, which are accessed from credible sources and are [near] real-time data. The device offers educational value by presenting the effect of the user's decisions in choosing different modes of transportation

Mobile awareness: Design for connectedness

This article describes our ongoing research project about design for behavior change, which is facilitated by Ubiquitous Computing technologies. In particular in this paper we discuss the potentiality of mobile devices to facilitate the mobility behavior change among people who are currently living at Turin, Italy. To this aim we illustrate our conceptual design of a mobile game, which is designed to facilitate mobility behavior change

SPINE: A domain-specific framework for rapid prototyping of WBSN applications

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Non-Intrusive Load Disaggregation of Industrial Cooling Demand with LSTM Neural Network

As the telecommunication industry becomes more and more energy intensive, energy efficiency actions are crucial and urgent measures to achieve energy savings. The main contribution to the energy demand of buildings devoted to the operation of the telecommunication network is cooling. The main issue in order to assess the impact of cooling equipment energy consumption to support energy managers with awareness over the buildings energy outlook is the lack of monitoring devices providing disaggregated load measurements. This work proposes a Non-Intrusive Load Disaggregation (NILD) tool that exploits a literature-based decomposition with an innovative LSTM Neural Network-based decomposition algorithm to assess cooling demand. The proposed methodology has been employed to analyze a real-case dataset containing aggregated load profiles from around sixty telecommunication buildings, resulting in accurate, compliant, and meaningful outcomes

Synthetic Ground Truth Generation of an Electricity Consumption Dataset

The training of supervised Machine Learning (ML) and Artificial Intelligence (AI) algorithms is strongly affected by the goodness of the input data. To this end, this paper proposes an innovative synthetic ground truth generation algorithm. The methodology is based on applying a data reduction with Symbolic Aggregate Approximation (SAX). In addition, a Classification And Regression Tree (CART) is employed to identify the best granularity of the data reduction. The proposed algorithm has been applied to telecommunication (TLC) sites dataset by analyzing their electricity consumption patterns. The presented approach substantially reduced the dispersion of the dataset compared to the raw dataset, thus reducing the effort required to train the supervised algorithms

Load Profiles Clustering and Knowledge Extraction to Assess Actual Usage of Telecommunication Sites

Deep awareness of a particular industry sector represents a fundamental starting point for its energy efficiency enhancement. In this perspective, a huge amount of industrial facilities' energy measurements are collected thanks to the widespread usage of monitoring systems and Internet-of-Things infrastructures. In this context, data mining techniques allows an effective exploitation of data for knowledge extraction to automatically analyse such enormous amount of data. This paper investigates a large data set including real telecommunication sites' aggregate electrical demand provided by the largest telecommunication service provider in Italy. The goal is the assessment of the actual usage category of telecommunication sites, aiming at supporting the facility management of the company and the energy knowledge discovery of each site category. A novel methodology is proposed that includes i) a proper normalisation method focused on energy Key Performance Indicators for telecommunication network energy management, ii) a time series decomposition tool to extract trends and periodical fluctuation of telecommunication sites' aggregated electric demand, and iii) the application of a k-Means clustering algorithm to assess sites' actual usage. The proposed methodology results in accurate outcomes, which witness the potential for practical application and discloses opportunities for further developments

Enabling personalised medical support for chronic disease management through a hybrid robot-cloud approach

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Therapeutic Potentials of Ketamine and Esketamine in Obsessive–Compulsive Disorder (OCD), Substance Use Disorders (SUD) and Eating Disorders (ED): A Review of the Current Literature

The obsessive–compulsive spectrum refers to disorders drawn from several diagnostic categories that share core features related to obsessive–compulsive disorder (OCD), such as obsessive thoughts, compulsive behaviors and anxiety. Disorders that include these features can be grouped according to the focus of the symptoms, e.g., bodily preoccupation (i.e., eating disorders, ED) or impulse control (i.e., substance use disorders, SUD), and they exhibit intriguing similarities in phenomenology, etiology, pathophysiology, patient characteristics and clinical outcomes. The non-competitive N-methyl-D-aspartate receptor (NMDAr) antagonist ketamine has been indicated to produce remarkable results in patients with treatment-resistant depression, post-traumatic stress disorder and OCD in dozens of small studies accrued over the past decade, and it appears to be promising in the treatment of SUD and ED. However, despite many small studies, solid evidence for the benefits of its use in the treatment of OCD spectrum and addiction is still lacking. Thus, the aim of this perspective article is to examine the potential for ketamine and esketamine in treating OCD, ED and SUD, which all involve recurring and intrusive thoughts and generate associated compulsive behavior. A comprehensive and updated overview of the literature regarding the pharmacological mechanisms of action of both ketamine and esketamine, as well as their therapeutic advantages over current treatments, are provided in this paper. An electronic search was performed, including all papers published up to April 2021, using the following keywords (“ketamine” or “esketamine”) AND (“obsessive” OR “compulsive” OR “OCD” OR “SUD” OR “substance use disorder” OR “addiction” OR “craving” OR “eating” OR “anorexia”) NOT review NOT animal NOT “in vitro”, on the PubMed, Cochrane Library and Web of Science online databases. The review was conducted in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The use and efficacy of ketamine in SUD, ED and OCD is supported by glutamatergic neurotransmission dysregulation, which plays an important role in these conditions. Ketamine’s use is increasing, and preliminary data are optimistic. Further studies are needed in order to better clarify the many unknowns related to the use of both ketamine and esketamine in SUD, ED and OCD, and to understand their long-term effectiveness.Peer reviewedFinal Published versio

Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review

Purpose: The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal. Methods: A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation of EOR, surgery at tumor progression and surgery in elderly patients. Results: A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B). Conclusions: A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity

Off-label long acting injectable antipsychotics in real-world clinical practice: a cross-sectional analysis of prescriptive patterns from the STAR Network DEPOT study

Introduction Information on the off-label use of Long-Acting Injectable (LAI) antipsychotics in the real world is lacking. In this study, we aimed to identify the sociodemographic and clinical features of patients treated with on- vs off-label LAIs and predictors of off-label First- or Second-Generation Antipsychotic (FGA vs. SGA) LAI choice in everyday clinical practice. Method In a naturalistic national cohort of 449 patients who initiated LAI treatment in the STAR Network Depot Study, two groups were identified based on off- or on-label prescriptions. A multivariate logistic regression analysis was used to test several clinically relevant variables and identify those associated with the choice of FGA vs SGA prescription in the off-label group. Results SGA LAIs were more commonly prescribed in everyday practice, without significant differences in their on- and off-label use. Approximately 1 in 4 patients received an off-label prescription. In the off-label group, the most frequent diagnoses were bipolar disorder (67.5%) or any personality disorder (23.7%). FGA vs SGA LAI choice was significantly associated with BPRS thought disorder (OR = 1.22, CI95% 1.04 to 1.43, p = 0.015) and hostility/suspiciousness (OR = 0.83, CI95% 0.71 to 0.97, p = 0.017) dimensions. The likelihood of receiving an SGA LAI grew steadily with the increase of the BPRS thought disturbance score. Conversely, a preference towards prescribing an FGA was observed with higher scores at the BPRS hostility/suspiciousness subscale. Conclusion Our study is the first to identify predictors of FGA vs SGA choice in patients treated with off-label LAI antipsychotics. Demographic characteristics, i.e. age, sex, and substance/alcohol use co-morbidities did not appear to influence the choice towards FGAs or SGAs. Despite a lack of evidence, clinicians tend to favour FGA over SGA LAIs in bipolar or personality disorder patients with relevant hostility. Further research is needed to evaluate treatment adherence and clinical effectiveness of these prescriptive patterns