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]

High discharge rate characteristics of nickel-cadmium batteries for pulse load filtering

Several tests of specially fabricated nickel-cadmium batteries having circular disk type electrodes were considered. These batteries were evaluated as filter elements between a constant current power supply and a five hertz pulsed load demanding approximately twice the power supply current during the load on portion of the cycle. Short tests lasting 10,000 cycles were conducted at up to a 21 C rate and an equivalent energy density of over 40 Joules per pound. In addition, two batteries were subjected to 10 to the 7 charge/discharge cycles, one at a 6.5 C rate and the other at a 13 C rate. Assuming an electrode to battery weight ratio of 0.5, these tests represent an energy density of about 7 and 14 Joules per pound respectively. Energy density, efficiency, capacitance, average voltage, and available capacity were tracked during these tests. After 10 to the 7 cycles, capacity degradation was negligible for one battery and about 20% for the other. Cadmium electrode failure may be the factor limiting lifetime at extremely low depth of discharge cycling. The output was examined and a simple equivalent circuit was proposed

Does alexithymia have a mediating effect between impulsivity and emotional-behavioral functioning in adolescents with binge eating disorder?

Objective: Binge eating Disorder (BeD) has recently been included in the Dsm-5. like many other eating disorder symptoms, BED is often present in adolescence. No studies have specifically investigated the influence of impulsivity and alexithymia on the emotional-behavioural functioning of adolescents diagnosed with BeD. Method: in this study, we recruited n = 162 adolescents (age range: 14-18) and divided them into two groups: 78 adolescents diagnosed with BeD, according to the Dsm-5 criteria (group A), and 84 healthy controls (group B). participants completed the youth self-report (ysr/11-18), the toronto Alexithymia scale (tAs-20) and the Barratt impulsiveness scale (Bis-11). Results: the results showed that group A had higher scores of alexithymia, impulsivity and maladaptive emotionalbehavioural functioning than Group B. Furthermore, alexithymia had a mediating effect on the relationship between impulsivity and emotional-behavioural functioning. Conclusions: These results allow us to hypothesise that alexithymia is a key variable influencing the emotional and behavioural problems of adolescents affected by BED. Our data confirms the previous studies underlining the association of impulsivity and alexithymia with the diagnosis of BeD in adolescents, and our study contributes to the previous literature, emphasising the central role of alexithymia in the mediating effect between impulsivity and emotional and behavioural problems. these results suggest the importance of promoting prevention and treatment polices focused on alexithymia

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

SEY and low-energy SEY of conductive surfaces

Abstract The study of Secondary Electron Yield (SEY) is widely performed to address important properties of materials to be used in a very wide spectrum of applications. It is, therefore, extremely important to understand the SEY dependence on material type, surface contaminants, structural quality and surface damage. We review here our recent studies of such items performed by looking at some representative conductive materials as noble metals and carbon based surfaces. Polycrystalline Ag, Au and Cu samples have been studied as introduced in the ultra-high vacuum chamber (therefore with an significant surface contamination) and after having been cleaned by ion sputtering. The comparison between the curves confirms that the SEY behavior is strongly influenced by the chemical state of the metal surfaces. We demonstrate the ability of SEY to determine work function values with high accuracy if the experimental system allows using very slow primary electrons. We also investigated, for the Cu sample, the effect on SEY of minimal amount of contaminants in the sub-monolayer regime showing that SEY is highly sensitive to the presence of adsorbates even at such very low coverages, specially for low energy primary electrons. In the case of C surfaces we summarize here the effect that the structural ordering of the C lattice has on the macroscopic SEY properties of ultrathin C layers. In particular we followed the SEY evolution during the thermal graphitization of thin amorphous carbon layers and during the amorphization of highly oriented pyrolytic graphite by means of Ar+ bombardment. In the first case the SEY decrease observed with the progressive conversion of sp3 hybrids into six-fold aromatic domains was related to the electronic structure of the C-films close to the Fermi level. We found that a moderate structural quality of the C layer, corresponding to aromatic clusters of limited size, is sufficient to obtain a SEY as low as ∼1. For the bombarded graphite, the strong lattice damage remains limited to the near surface layer, where the high density of defects reduces the transport of incoming and secondary electrons. Then, the SEY curves resulted differently modified in the low and high primary energy regions, but their maximal values remained favorably low. Our findings demonstrate that SEY, besides being an indispensable mean to qualify technical materials in many technological fields, can be also used as a flexible and advantageous diagnostics to probe surfaces and interfaces

Are Audience Response Systems Worth the Cost? Comparing Question-Driven Teaching Strategies for Emergency Medical Technician Education

Purpose: As Emergency Medical Technician educators develop curricula to meet new national educational standards, effective teaching strategies validated for course content and unique student demographics are warranted. Three methods for answering multiple choice questions presented during lectures were compared: a) Audience Response System (ARS, clickers), b) hand-raising-with-eyes-closed (no-cost option), and c) passive response (no-cost option). The purpose was to determine if using the ARS resulted in improved exam scores. Method: 113 Emergency Medical Technician (EMT) students participated in this cross-over, block randomized, controlled trial, which was incorporated into their Cardiac Emergencies and Pulmonary Emergencies course lectures. Students took pretests, immediate post-tests, and delayed post-tests composed of multiple choice questions that targeted either lower or higher order thinking. Results: For both lectures, there were significant improvements on all immediate post-test scores compared to all pretest scores (p Conclusions: In this cohort, incorporation of no-cost question-driven teaching strategies into lectures was as effective as an ARS at encouraging significant, immediate and sustained improvements in answering multiple choice questions