An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization

Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a better assessment of the progressing levels. The proposed mechanism works if structural parameters are correctly tuned for the given historical context. Determining such correct parameters is not a simple task since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism based on differential evolution. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach, experimental setting and results.Comment: mail: [email protected]

Degradation stage classification via interpretable feature learning

Predictive maintenance (PdM) advocates for the usage of machine learning technologies to monitor asset's health conditions and plan maintenance activities accordingly. However, according to the specific degradation process, some health-related measures (e.g. temperature) may be not informative enough to reliably assess the health stage. Moreover, each measure needs to be properly treated to extract the information linked to the health stage. Those issues are usually addressed by performing a manual feature engineering, which results in high management cost and poor generalization capability of those approaches. In this work, we address this issue by coupling a health stage classifier with a feature learning mechanism. With feature learning, minimally processed data are automatically transformed into informative features. Many effective feature learning approaches are based on deep learning. With those, the features are obtained as a non-linear combination of the inputs, thus it is difficult to understand the input's contribution to the classification outcome and so the reasoning behind the model. Still, these insights are increasingly required to interpret the results and assess the reliability of the model. In this regard, we propose a feature learning approach able to (i) effectively extract high-quality features by processing different input signals, and (ii) provide useful insights about the most informative domain transformations (e.g. Fourier transform or probability density function) of the input signals (e.g. vibration or temperature). The effectiveness of the proposed approach is tested with publicly available real-world datasets about bearings' progressive deterioration and compared with the traditional feature engineering approach

Solving the scalarization issues of Advantage-based Reinforcement Learning algorithms

In this research, some of the issues that arise from the scalarization of the multi-objective optimization problem in the Advantage Actor–Critic (A2C) reinforcement learning algorithm are investigated. The paper shows how a naive scalarization can lead to gradients overlapping. Furthermore, the possibility that the entropy regularization term can be a source of uncontrolled noise is discussed. With respect to the above issues, a technique to avoid gradient overlapping is proposed, while keeping the same loss formulation. Moreover, a method to avoid the uncontrolled noise, by sampling the actions from distributions with a desired minimum entropy, is investigated. Pilot experiments have been carried out to show how the proposed method speeds up the training. The proposed approach can be applied to any Advantage-based Reinforcement Learning algorithm

Formal Derivation of Mesh Neural Networks with Their Forward-Only Gradient Propagation

This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information. In MNNs, information is propagated between neurons throughout a state transition function. State and error gradients are then directly computed from state updates without backward computation. The MNN architecture and the error propagation schema is formalized and derived in tensor algebra. The proposed computational model can fully supply a gradient descent process, and is potentially suitable for very large scale sparse NNs, due to its expressivity and training efficiency, with respect to NNs based on back-propagation and computational graphs

Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture

Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain activities, translate them into actions, and enable humans to directly communicate through them. In this context, the need for strong recognition performances results in increasingly sophisticated machine learning (ML) techniques, which may result in poor performance in a real application (e.g., limiting a real-time implementation). Here, we propose an ensemble approach to effectively balance between ML performance and computational costs in a BCI framework. The proposed model builds a classifier by combining different ML models (base-models) that are specialized to different classification sub-problems. More specifically, we employ this strategy with an ensemble-based architecture consisting of multi-layer perceptrons, and test its performance on a publicly available electroencephalography-based BCI dataset with four-class motor imagery tasks. Compared to previously proposed models tested on the same dataset, the proposed approach provides greater average classification performances and lower inter-subject variability

Educational interventions on pregnancy vaccinations during childbirth classes improves vaccine coverages among pregnant women in Palermo’s province

Maternal immunization is considered the best intervention in order to prevent influenza infection of pregnant women and influenza and pertussis infection of newborns. Despite the existing recommendations, vaccination coverage rates in Italy remain very low. Starting from August 2018, maternal immunization against influenza and diphtheria-tetanus-pertussis were strongly recommended by the Italian Ministry of Health. We conducted a cross sectional study to estimate the effectiveness of an educational intervention, conducted during childbirth classes in three general hospitals in the Palermo metropolitan area, Italy, on vaccination adherence during pregnancy. To this end, a questionnaire on knowledge, attitudes, and immunization practices was structured and self-administered to a sample of pregnant women attending childbirth classes. Then, an educational intervention on maternal immunization, followed by a counseling, was conducted by a Public Health medical doctor. After 30 days following the interventions, the adherence to the recommended vaccinations (influenza and pertussis) was evaluated. At the end of the study 326 women were enrolled and 201 responded to the follow-up survey. After the intervention, among the responding pregnant women 47.8% received influenza vaccination (+44.8%), 57.7% diphtheria-tetanus-pertussis vaccination (+50.7%) and 64.2% both the recommended vaccinations (+54.8%). A significant association was found between pregnant women that received at least one vaccination during pregnancy and higher educational level (graduation degree/master’s degree), employment status (employed part/full-time) and influenza vaccination adherence during past seasons (at least one during last five years). The implementation of vaccination educational interventions, including counseling by healthcare professionals (HCPs), on maternal immunization during childbirth courses improved considerably the vaccination adherence during pregnancy

Prevalence and correlates of depressive disorders in people with Type 2 diabetes: results from the International Prevalence and Treatment of Diabetes and Depression (INTERPRET‐DD) study, a collaborative study carried out in 14 countries

Aims To assess the prevalence and management of depressive disorders in people with Type 2 diabetes in different countries. Methods People with diabetes aged 18–65 years and treated in outpatient settings were recruited in 14 countries and underwent a psychiatric interview. Participants completed the Patient Health Questionnaire and the Problem Areas in Diabetes scale. Demographic and medical record data were collected. Results A total of 2783 people with Type 2 diabetes (45.3% men, mean duration of diabetes 8.8 years) participated. Overall, 10.6% were diagnosed with current major depressive disorder and 17.0% reported moderate to severe levels of depressive symptomatology (Patient Health Questionnaire scores >9). Multivariable analyses showed that, after controlling for country, current major depressive disorder was significantly associated with gender (women) (PPPPP<0.0001). The proportion of those with either current major depressive disorder or moderate to severe levels of depressive symptomatology who had a diagnosis or any treatment for their depression recorded in their medical records was extremely low and non-existent in many countries (0–29.6%). Conclusions Our international study, the largest of this type ever undertaken, shows that people with diabetes frequently have depressive disorders and also significant levels of depressive symptoms. Our findings indicate that the identification and appropriate care for psychological and psychiatric problems is not the norm and suggest a lack of the comprehensive approach to diabetes management that is needed to improve clinical outcomes

Status of COLDDIAG: A Cold Vacuum Chamber for Diagnostics

One of the still open issues for the development of superconducting insertion devices is the understanding of the beam heat load. With the aim of measuring the beam heat load to a cold bore and the hope to gain a deeper understanding in the beam heat load mechanisms, a cold vacuum chamber for diagnostics is under construction. The following diagnostics will be implemented: i) retarding field analyzers to measure the electron energy and flux, ii) temperature sensors to measure the total heat load, iii) pressure gauges, iv) and mass spectrometers to measure the gas content. The inner vacuum chamber will be removable in order to test different geometries and materials. This will allow the installation of the cryostat in different synchrotron light sources. COLDDIAG will be built to fit in a short straight section at ANKA. A first installation at the synchrotron light source Diamond is foreseen in June 2011. Here we describe the technical design report of this device and the planned measurements with beam.Comment: Presented at First International Particle Accelerator Conference, IPAC'10, Kyoto, Japan, from 23 to 28 May 201

Covid-19 lockdown impacts among patients with cystic fibrosis. an italian regional reference centre experience

Background: Coronavirus pandemic has influenced our society with social distancing and management of chronic disease such as cystic fibrosis (CF). During the Italian lockdown from March to May 2020, CF patients reduced the number of outpatient visits, limited social interactions and spent more time at home. The aim of this study is to evaluate the impact of the lockdown on body mass index (BMI) and lung function tests on CF patients. Methods: We retrospectively reviewed clinical data about 111 CF patients followed in our Regional Cystic Fibrosis Reference Centre (Policlinico Umberto I, Rome) according to two periods: pre-lockdown (from October 2019-March 2020) and post-lockdown (from May 2020-October 2020). We collected data on nutritional (BMI and body weight) and lung function status; we chose the best values of the 'pre-lockdown' and 'post-lockdown' period for each patient. Patients were divided into 3 groups according to FEV1 value (Forced Expiratory Volume in the 1st second): group 1 (FEV1 &lt;40%), group 2 (FEV1 40-70%), group 3 (FEV1 &gt;70%). All patients received a telephone interview asking for the number of hours per week devoted to physical activity, number of pulmonary acute exacerbations and subjective evaluation of adherence to medical therapy, respiratory physiotherapy and diet, during the two periods. Results: Comparing weight, BMI and respiratory function between pre and post lockdown periods, we noticed an increase in weight during among overall patients. Male patients improved weight, BMI, FEF 25-75% (Forced Expiratory flow between 25% and 75% of vital capacity) and Tiffenau index more than female patients. The most severely compromised patients (group 1), showed a significant loss of both weight and BMI. Instead, patients with moderate respiratory function (group 2) showed a significant increase of both weight and BMI and a slightly reduced CVF (Forced Vital capacity). We found no differences among patients with good respiratory function (group 3). Comparing each clinical sub-groups, we noticed a significative improvement of weight (p = 0.018) and BMI (p = 0.030) among patients with moderate respiratory function compared to patients with compromised respiratory function. During lockdown, patients reported less physical activity, no variation in food amount and composition, more adherence to therapy (43%) and more consistent daily respiratory physiotherapy (47.6%). Conclusions: Lockdown period had benefit among CF patients in terms of weight in particular in male patient. The greatest benefit on nutritional state was observed in patients with moderate reduction of respiratory function. In addition, we noted a stabilization and sometimes a slight improvement of lung function, instead of a continuous and steady decline that is normally observed in CF patients. These beneficial effects are slight but significative, bearing in mind the general worsening that CF patients experience annually