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In the past few decades, we saw a proliferation of scientific articles available online. This data-rich environment offers several opportunities but also challenges, since it is problematic to explore these resources and identify all the relevant content. Hence, it is crucial that they are appropriately annotated with their relevant concepts so to increase their chance of being properly indexed and retrieved. In this paper, we present R-Classify, a web tool that assists users in identifying the most relevant concepts according to a large-scale ontology of research areas in the field of Computer Science.

Science communication has a number of bottlenecks that include the rising number of published research papers and its non-machine-accessible and document-based paradigm, which makes the exploration, reading, and reuse of research outcomes rather inefficient. Recently, Knowledge Graphs (KG), i.e., semantic interlinked networks of entities, have been proposed as a new core technology to describe and curate scholarly information with the goal to make it machine readable and understandable. However, the main drawback of the use of such a technology is that researchers are asked to manually annotate their research papers and add their contributions within the KGs. To address this problem, in this paper we propose SCICERO, a novel KG generation approach that takes in input text from research articles and generates a KG of research entities. SCICERO uses Natural Language Processing techniques to parse the content of scientific papers to discover entities and relationships, exploits state-of-the-art Deep Learning Transformer models to make sense and validate extracted information, and uses Semantic Web best practices to formally represent the extracted entities and relationships, making the written content of research papers machine-actionable. SCICERO has been tested on a dataset of 6.7M papers about Computer Science generating a KG of about 10M entities. It has been evaluated on a manually generated gold standard of 3,600 triples that cover three Computer Science subdomains (Information Retrieval, Natural Language Processing, and Machine Learning) obtaining remarkable results.

Memory is defined as the capability of encoding, storing, and retrieving information and is a pillar of our cognitive functions. Memory is one the most investigated processes in people with Intellectual Disability (ID), and studies have documented severe deficits in its functioning. While there are several attempts to exploit GUIs (Graphical User Interfaces) to support memory training for children with ID, limited research explores Tangible User Interfaces (TUIs) for this purpose. The paper describes the design and technology of a novel TUI named VIC (VIsual spatial Cubes for memory training), a system composed by a set of digitally enhanced cubes that emit light and sound, a sensorized board and a mobile app. VIC enables children to perform multiple, configurable memory training activities that involve block manipulation and block placements. The research is based on a vast analysis of the state of the art and on validated methods adopted in memory rehabilitation contexts. From this analysis, and from the theories underlying TUIs, we distilled a set of design principles that informed the design of the multi-sensory affordances of VIC, and can be exploited for other researchers to develop TUIs in this field. The paper also reports an exploratory study that involved 12 children with ID and 3 therapists from a specialized daycare Center. The study focused on the evaluation of the quality of the system in terms of usability, likability and potential for adoption. Although preliminary, the results suggest that our design approach was sound and VIC has the potential to become a valid tool to complement existing practices in memory training and can be expanded to support also memory assessment for children with ID.

In this paper, experimental work is supported by multi-scale numerical modeling to investigate nanomechanical response of pristine and ion irradiated with Fe2＋ ions with energy 5 MeV high purity iron specimens by nanoindentation and Electron Backscatter Diffraction. The appearance of a sudden displacement burst that is observed during the loading process in the load–displacement curves is connected with increased shear stress in a small subsurface volume due to dislocation slip activation and mobilization of pre-existing dislocations by irradiation. The molecular dynamics (MD) and 3D-discrete dislocation dynamics (3D-DDD) simulations are applied to model geometrically necessary dislocations (GNDs) nucleation mechanisms at early stages of nanoindentation test; providing an insight to the mechanical response of the material and its plastic instability and are in a qualitative agreement with GNDs density mapping images. Finally, we noted that dislocations and defects nucleated are responsible the material hardness increase, as observed in recorded load–displacement curves and pop-ins analysis.

Presently under construction in Lund, Sweden, the European Spallation Source (ESS) will be the world's brightest neutron source. As such, it has the potential for a particle physics program with a unique reach and which is complementary to that available at other facilities. This paper describes proposed particle physics activities for the ESS. These encompass the exploitation of both the neutrons and neutrinos produced at the ESS for high precision (sensitivity) measurements (searches).

The European plan for a green transition includes the Fit for 55 package, designed to pave the way for climate neutrality. Despite its significant implications for cleaner technologies, it potentially correlates with high investment requirements, necessitating the pursuit of cost-effective environmental policies. Starting from the reference scenario previously envisaged in the Energy and Climate Plan, socioeconomic and environmental impacts are assessed using mixed methods. It is estimated that €1120 bn in investments are needed to meet decarbonization targets, while the total impact on public finance revenues to 2030 is projected at €529 bn. Additionally, the avoided costs of emissions amount to €36 bn, while those from energy savings are expected to reach €30 bn. This paper adds value by contributing to the literature on European climate policies, offering an in-depth appraisal of implications that integrates technoeconomic and environmental perspectives. Furthermore, it informs policymakers' public spending decisions for decarbonization.

The high energy physics community has recently identified an e+e− Higgs factory as one of the next-generation collider experiments, following the completion of the High Luminosity LHC program at CERN. The moderate radiation levels expected at such colliders compared to hadron colliders, enable the use of less radiation tolerant but cheaper technologies for the construction of the particle detectors. This opportunity has triggered a renewed interest in the development of scintillating glasses for the instrumentation of large detector volumes such as homogeneous calorimeters. While the performance of such scintillators remains typically inferior in terms of light yield and radiation tolerance compared to that of many scintillating crystals, substantial progress has been made over the recent years. In this paper we discuss the time resolution of cerium-doped Alkali Free Fluorophosphate scintillating glasses, read-out with silicon photo-multipliers in detecting single charged tracks and at different positions along the longitudinal development of an electromagnetic shower, using respectively 150 GeV pions and 100 GeV electron beams at the CERN SPS H2 beam line. A single sensor time resolution of 14.4 ps and 5–7 ps was measured respectively in the two cases. With such a performance the present technology has the potential to address an emerging requirement of future detectors at collider experiments: measuring the time-of-flight of single charged particles as well as that of neutral particles showering inside the calorimeter and the time development of showers.

Prediction of demand at different levels of aggregation is a crucial task in many business and industrial activities. This task may be extremely challenging when the number of time series increases together with the number of parameters governing the dynamics of the underlying model. This paper proposes theoretical and empirical contributions providing practical tools for managers needing efficient, flexible, and timely instruments. We first derive optimal results for predicting a system of time series following multivariate Exponentially Weighted Moving Average (EWMA) dynamics. Our results have relevant practical consequences. Indeed, we propose a fast EM algorithm that maximizes the Gaussian multivariate likelihood regardless of the model's dimension. Secondly, we show optimal results for the hierarchies, deriving closed-form results for the underlying parameters. Finally, using more than one hundred Walmart sales time series, we show that our approach is competitive with the optimal forecast reconciliation approach based on univariate forecasts.

In this paper we prove that a finite group of order r has at most 7.3722⋅r[Formula present]+1.5315 subgroups.

This paper investigates data correlation in remote sensing networks and how it can be characterized through diverse models quantifying the Value of Information (VoI), a metric that describes how informative the data transmitted by the sensors are. For each sensor, the VoI evaluations comprise the average node-specific Age of Information (AoI), the average cost spent for sending updates, and the AoI of neighbor nodes, assumed to be correlated sources of information and therefore benefiting the VoI of other sensors nearby. We discuss how this metric can be tracked through a two-dimensional Markov chain, but we also show how this representation can be simplified by including the impact of neighbor nodes within the transition probabilities, so as to obtain a simpler model that gives the same insight in terms of VoI evaluations.