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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.

In the article “Engineering geology-A fifty year perspective”(Juang et al., 2016), which celebrated the 50th anniversary of Engineering Geology, the authors described Engineering Geology for Engineering Projects (EGEP) together with Geological Engineering and Geotechnical Engineering (GEGE) as important elements of Engineering Geology. For example, four-hundred and twelve papers were published in Engineering Geology from 1986–1995. Among which, one-hundred and twenty (about 30%) were related to EGEP. The research topics mainly included design and construction of foundations, tunnels, and slopes (eg dams, embankments, landslides). However, the papers published on EGEP and GEGE declined significantly from 2006 to 2015. It was speculated that the worldwide economic recession that began in 2008 might have contributed to the decline of infrastructure development and the number of …

Taking into account axial symmetry in the covariance function of a Gaussian random field is essential when the purpose is modelling data defined over a large portion of the sphere representing our planet. Axially symmetric covariance functions admit a convoluted spectral representation that makes modelling and inference difficult. This motivates the interest in devising alternative strategies to attain axial symmetry, an appealing option being longitudinal integration of isotropic random fields on the sphere. This paper provides a comprehensive theoretical framework to model longitudinal integration on spheres through a nonparametric Bayesian approach. Longitudinally integrated covariances are treated as random objects, where the randomness is implied by the randomised spectrum associated with the covariance function. After investigating the topological support induced by our construction, we give the posterior distribution a thorough inspection. A Bayesian nonparametric model for the analysis of data defined on the sphere is described and implemented, its performance investigated by means of the analysis of both simulated and real data sets.

On 12 September 2019, the global Patient Reported Outcome for Multiple Sclerosis (PROMS) Initiative was launched at the 35th Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS). The multi-stakeholder PROMS Initiative is jointly led by the European Charcot Foundation (ECF) and the Multiple Sclerosis International Federation (MSIF), with the Italian Multiple Sclerosis Society (AISM) acting as the lead agency for and on behalf of the global MSIF movement. The initiative has the ambitious mission to (i) maximize the impact of science with and of patient input on the life of people affected by MS, and (ii) to represent a unified view on Patient-Reported Outcomes for MS to people affected by MS, healthcare providers, regulatory agencies and Health Technologies Assessments agencies. Equipped with an innovative participatory governance of an international and interdisciplinary network of different stakeholders, PROMS has the potential to guide future breakthroughs in MS patient-focused research and care. In this paper we present the progresses of the global PROMS Initiative and discuss the open questions that we aim to address.

Evidences collected in the past few years have strengthened the concept that the sympathetic nervous system plays a primary role in the development and progression of the hypertensive state, starting from the early stage, and in the hypertension-related cardiovascular diseases. Several pathophysiological mechanisms are involved. Among them the genetic background, the immune system in conjunction with sympathetic activation. The present review will briefly discuss the importance of the above mentioned mechanisms in the development of hypertension. The paper will also examine the sympathetic mechanisms underlying attended vs unattended blood pressure measurements as well as their role in resistant vs pseudo-resistant hypertension. Finally evidence from recent meta-analysis on the relevance of sympathetic nerve traffic activation in the pathogenesis of hypertension will be briefly discussed.

In finitely repeated public goods games, contributions are initially high, and gradually decrease over time. Two main explanations are consistent with this pattern: (i) the population is composed of free-riders, who never contribute, and conditional cooperators, who contribute if others do so as well; (ii) strategic players contribute to sustain mutually beneficial future cooperation, but reduce their contributions as the end of the game approaches. This paper analyzes experimentally these explanations, by manipulating group composition to form homogeneous groups on both the preference and the strategic ability dimensions. Our results highlight the role of strategic ability in sustaining contributions, and suggest that the interaction between the two dimensions also matters: we find that groups that sustain high levels of cooperation are composed of members who share a common inclination toward cooperation and also have the strategic abilities to recognize and reap the benefits of enduring cooperation.

The reconstruction of individual geometries from medical imaging is quite a standard in the framework of patient-specific medicine. A major drawback in such a context is represented by noise inherent to the data acquisition. Low signal-to-noise ratios can negatively impact extraction algorithms, and result in artefacts or poor quality of the reconstructed meshes. Direct application of numerical methods on such meshes can yield misleading results. Indeed, artefacts and badly shaped elements may corrupt numerical simulations or induce relevant errors in the computation of meaningful geometrical quantities, such as the curvature or the geodesic surface distance. In this paper, we propose a denoising procedure to remove artefacts from a triangular mesh of a three-dimensional closed surface which represents a brain cortex. For this purpose, we combine a smoothing technique (i.e., the Taubin or the HC-Laplacian smoothing) with an edge-flipping algorithm. To control the denoising procedure, we introduce a stopping criterion that takes into account both the improvement of the mesh quality and the loss of volume enclosed by the surface. On a brain cortical surface reconstructed from Magnetic Resonance Imaging (MRI) data, we first perform a tuning analysis of the parameters involved in the smoothing algorithm, then we investigate the effectiveness of the denoising procedure. Finally, as an example of relevant geometrical feature, we study the improvement generated by the proposed algorithm on the computation of the cortical curvature.

In this paper we provide a non-clausal tableau calculus for the minimum satisfiability problem. Moreover we describe how to adapt it to some variants. Our starting point is a calculus for non-clausal maximum satisfiability problem.

In this paper we show that the rank of every chiral polytope having a Suzuki group as automorphism group is 3. This gives a positive answer to a conjecture of Isabel Hubard and Dimitri Leemans.

This study presents the findings from several field campaigns carried out in Lake Idro (Northern Italy), a deep (124 m) meromictic-subalpine lake, whose water column is subdivided in a mixolimnion (~0–40 m) and a monimolimnion (~40–124 m). Hydrochemical data highlight two main peculiarities characterizing the Lake Idro meromixis: a) presence of a high manganese/iron ratio (up to 20 mol/mol), b) absence of a clear chemocline between the two main layers. The high manganese content contributed to the formation of a stable manganese dominated deep turbid stratum (40–65 m), enveloping the redoxcline (~45–55 m) in the upper monimolimnion. The presence of this turbid stratum in Lake Idro is described for the first time in this study. The paper examines the distribution of dissolved and particulate forms of transition metals (Mn and Fe), alkaline earth metals (Ca and Mg), and other macro-constituents or nutrients (S, P, NO3-N, NH4-N), discussing their behavior over the redoxcline, where the main transition processes occur. Field measurements and theoretical considerations suggest that the deep turbid stratum is formed by a complex mixture of manganese and iron compounds with a prevalence of Mn(II)/Mn(III) in different forms including dissolved, colloidal, and fine particles, that give to the turbid stratum a white-pink opalescent coloration. The bacteria populations show a clear stratification with the upper aerobic layer dominated by the heterotrophic Flavobacterium sp., the turbid stratum hosting a specific microbiological pool, dominated by Caldimonas sp., and the deeper anaerobic layer dominated by the sulfur-oxidizing and denitrifier Sulfuricurvum sp. The occurrence in August 2010 of an anomalous lake surface coloration lasting about four weeks and developing from milky white-green to red-brown suggests that the upper zone of the turbid stratum could be eroded during intense weather-hydrological conditions with the final red-brown coloration resulting from the oxidatio

Natural gas in clathrate hydrates is regarded as a potential energy source that has received increased attention to optimize production strategies with controllable impacts on the environment. This paper investigates possible instability of a gently sloping reservoir of oceanic hydrates induced by gas production using the huff-puff method through a horizontal well. The geomechanical stability of the slope is analyzed within the framework of the limit equilibrium method by considering the dynamic change in the pore pressure and the strength parameters of the slope during gas production. The production process is simulated by a coupled analysis of heat and flow transport considering thermal effects of hydrate dissociation and formation, and the time-dependent pore pressure and strength parameters are attained from this analysis and passed to the slope stability analysis. Parametric studies are performed to screen the optimal production scenario under different site conditions. Being part of the huff-puff production process, thermal stimulation during the huff stage poses a risk of production-induced instability to the slope. Overpressure is the dominant cause for slope failure, and strength reduction due to hydrate dissociation plays a secondary role in the studied scenario. The production-induced slope failure likely takes place at a site with interbedded geological structures that promote overpressure expansion in a laterally extending band beneath the potential failure surface. Thus, the geological structures should be properly modelled in reservoir simulations, as they could impact the production effectiveness and geomechanical response of the reservoir remarkably. This study demonstrates a need for a multi-objective optimization procedure to seek the overall optimal production strategy, since the economically optimal option is not necessarily free of risk of production-induced geo-hazards.