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Research on text comprehension in relation to the reading medium (paper or screen) has mainly involved undergraduate or high school students. To advance current knowledge on the effects of reading medium, this longitudinal study focused on beginner readers, specifically, the role of precursors in first graders’ comprehension of narrative and expository linear texts from reading on paper and computer screen. Working memory and inference skills as cognitive precursors and basic digital skills were measured at the end of preschool (T1); reading text comprehension and word reading, as a control variable, were measured at the end of the first grade (T2). Sixty-three children participated in total. The first graders read four texts, one narrative and one expository, on both paper and computer screen, in a counterbalanced order. Results showed no main effects of the reading medium or text genre, but the interactive effect of these variables was significant. At T2, the children had higher comprehension scores after reading narrative than descriptive texts from paper. In addition, reading from the screen was preferred at post-test, after all texts were presented. As precursors, working memory and inference skills predicted both printed and digital text comprehension. In contrast, basic digital skills predicted only digital text comprehension after controlling for medium, text genre, and word reading.

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.

Within the framework of studies concerning the importance of European manuscript collections for Ibadi history, this article aims at retracing the history of an archive put together by the French scholar Auguste Bossoutrot (1856-1937). This archive gathered a quantity of materials on the Arabic and Berber languages collected during his life. In particular, some of the manuscripts contain parts of a long religious work in Berber (Kitāb al-Barbariyya), discovered in the island of Djerba (Tunisia) among the Ibadi community of the island towards the end of the nineteenth century. This text was firstly discovered and reported to the scientific community by another French scholar, A. De Calassanti-Motylinski (1854-1907), but his untimely death prevented him from publishing it and the whereabouts of the manuscripts that contained it remained unknown until the discovery of Bossoutrot's papers, which contained the longest extant copy of the work (about 900 pages).

This paper uses the case of Italy during the Covid-19 pandemic to critically assess the EU legal framework on third-country national migrants’ equal access to social benefits. In Italy, migrants are structurally excluded from core social protections, a situation that during the pandemic led to a worsening of existing patterns of inequality; migrants have been more exposed than citizens to poverty, unemployment, and destitution. The first part of the paper looks for the EU legal root of this situation: it examines the EU legal framework in the migration field, showing that it is affected by fragmentation and inconsistencies. These problems become even more acute at the national level, where the Italian legislature mis-transposed the EU migration directives, thus affecting the use of discretionary clauses therein and severely curtailing migrants’ equal treatment rights. Then, the second part of the paper asks whether adopting a mainstreaming approach to enhance equality could improve the situation of migrants. The paper argues that equality mainstreaming in the migration field shows good potential, while also encountering some structural limits. Therefore, it can hardly be considered a silver bullet against the problem of migrants’ discrimination.

Prussian blue (PB) is a coordination polymer based on the Fe2+…C[tbnd]N…Fe3+ sequence. It is an FDA-approved drug, intended for oral use at the acidic pH of the stomach and of most of the intestine track. However, based on FDA approval, a huge number of papers proposed the use of PB nanoparticles (PBnp) under “physiological conditions”, meaning pH buffered at 7.4 and high saline concentration. While most of these papers report that PBnp are stable at this pH, a small number of papers describes instead PBnp degradation at the same or similar pH values, i.e. in the 7–8 range. Here we give a definitively clear picture: PBnp are intrinsically unstable at pH ≥ 7, degrading with the fast disappearance of their 700 nm absorption band, due to the formation of OH- complexes from the labile Fe3+ centers. However, we show also that the presence of a polymeric coating (PVP) can protect PBnp at pH 7.4 for over 24 h. Moreover, we demonstrate that when “physiological conditions” include serum, a protein corona is rapidly formed on PBnp, efficiently avoiding degradation. We also show that the viability of PBnp-treated EA.hy926, NCI-H1299, and A549 cells is not affected in a wide range of conditions that either prevent or promote PBnp degradation.

In their seminal paper Artemov and Protopopescu provide Hilbert formal systems, Brower–Heyting–Kolmogorov and Kripke semantics for the logics of intuitionistic belief and knowledge. Subsequently Krupski has proved that the logic of intuitionistic knowledge is PSPACE-complete and Su and Sano have provided calculi enjoying the subformula property. This paper continues the investigations around sequent calculi for Intuitionistic Epistemic Logics by providing sequent calculi that have the subformula property and that are terminating in linear depth. Our calculi allow us to design a procedure that for invalid formulas returns a Kripke model of minimal depth. Finally we also discuss refutational sequent calculi, that is sequent calculi to prove that formulas are invalid.

Introduction: Contact lens (CL) practice is an ever-changing field with clinical knowledge, techniques and equipment continuously evolving. These new developments are backed with clinical trials and research to ensure that practitioners feel confident that there is an evidence base to support these advances. Evidence-based practice is now a crucial part of CL practice, and its importance also filters down to CL education. For example, lectures are one of the most popular tools for an educator but, is standing at the front of a lecture theatre full of students a more effective way of teaching than providing the same material for students to read by themselves? What evidence exists specific to CL education? Method: An expert panel of educators completed a comprehensive literature review of current evidence of teaching methods in CL training, or if not available then what can be learnt from other health care professional training that could be potentially applicable to CL education. Results: Due to the amount of evidence available in the overall subject area relating to healthcare education, the initial plan of compiling evidence into one narrative review paper was discarded in favour of producing two linked papers. Here, the first paper details definitions of terminology, and also teaching methods. The second paper focuses on assessment and specific clinical training required to attain CL practice competency. In this first paper, no direct evidence of the spreading and benefit of new education strategies evidence such as flipped classrooms, spaced learning, test-enhanced learning, group work, CBL, PBL, TBL, and reflective practice in CL education was found. The only technique that was widely used in the CL field was case reports and the group discussion of them. Nevertheless, the authors found a consensus of opinion from other disciplines that are transferable to CL teaching and could help students meet the intended learning outcomes. Conclusion: There is a small amou

Although locating a company in a tax haven is not illegal per se, it is likely to be part of a scheme purported to erode the tax base or to shift profits to less-taxed jurisdictions. For this reason, this type of location decision is usually targeted by anti-avoidance laws, that can take the form either of specific rules or general standards that, ex-post, sanction or limit the location decision. However, rules entail higher drafting costs and are easy to circumvent whereas standards entail more uncertainty costs. The goal of this paper is to illustrate that the risk of aggressive location decisions can be predicted ex-ante using publicly available data and that this prediction can be used by tax authorities. In the paper, we do two things. First, we use publicly available accounting data for the period 2015–2019 on 4031 group ultimate owners (GUO) of active listed companies resident in one of the 27 European Union countries to predict the probability that these companies would have at least a subsidiary in a tax haven, by spring 2021, as well as the intensity in the use of tax havens. Second, we discuss how this prediction can be used by tax authorities in the context of a new administrative preventive approach that complements the traditional legal approach. This approach can increase welfare by reducing uncertainty, thus increasing investments and economic growth.

This paper aims to identify the major determinants of the structural differences in gender equality for EU countries, using tools from partial order theory on a system of 6 indicators, pertaining to the Sustainable Development Goal 5 “Achieve gender equality and empower all women and girls”. The paper analyzes the gender equality partial orders, among EU countries, for years 2006, 2010 and 2017. Performing a sensitivity analysis, the pay gap indicator was disclosed as the most relevant source of differences across European nations, for each of the three years considered. Subsequently the paper focuses on the temporal evolution of gender equality in three specific countries, i.e., Denmark, Germany and the Czech Republic, studying which elementary dimensions has the strongest impact on its dynamics. Interestingly it turns out that, in different countries, different indicators play the major role (related to women’s political representation, for Denmark and Germany, and to care giving, for Czech Republic). These sensitivity analyses are performed using posetic tools, to avoid the possible information loss of an aggregative procedures, involving the computation of composite indicators. This constitutes a major methodological contribution of the paper.

In application domains such as routing, network analysis, scheduling, and planning, directed graphs are widely used as both formal models and core data structures for the development of efficient algorithmic solutions. In these areas, graphs are often evolving in time: for example, connection links may fail due to temporary technical issues, meaning that edges of the graph cannot be traversed for some time interval and alternative paths have to be followed. In classical computation graphs have been implemented both explicitly through adjacency matrices/lists and symbolically as ordered binary decision diagrams. Moreover, ad-hoc visit procedures have been developed to deal with dynamically evolving graphs. Quantum computation, exploiting interference and entanglement, has provided an exponential speed-up for specific problems, e.g., database search and integer factorization. In the quantum framework everything must be represented and manipulated using reversible operators. This poses a challenge when one has to deal with traversals of dynamically evolving directed graphs. Graph traversals are not intrinsically reversible because of converging paths. In the case of dynamically evolving graphs also the creation/destruction of paths comes into play against reversibility. In this paper we propose a novel high level graph representation in quantum computation supporting dynamic connectivity typical of real-world network applications. Our procedure allows to encode any multigraph into a unitary matrix. We devise algorithms for computing the encoding that are optimal in terms of time and space and we show the effectiveness of the proposal with some examples. We describe how to react to edge/node failures in constant time. Furthermore, we present two methods to perform quantum random walks taking advantage of this encoding: with and without projectors. We implement and test our encoding obtaining that the theoretical bounds for the running time are confirmed by the empirical

Palaeoenvironmental data are fundamental in determining the manifold impacts of climate change. This paper argues that sessile barnacles are an excellent palaeoenvironmental proxy: they are present in nearly all nearshore environments, and their shell consists of diagenetically stable low-magnesium calcite and grows fast enough to record short-term variations. To demonstrate their utility, specimens from several Western Mediterranean Pliocene and Pleistocene barnacle-rich deposits are analysed herein, using for the first time a combination of sedimentology, taphonomy, stable isotope geochemistry and detailed comparisons with modern counterparts. Within shelf carbonate systems, barnacle diversity is highest in the shallow, nearshore waters and decreases moving offshore, thus providing a good proxy for the reconstruction of water depth and distance from the coastline. Barnacle taphonomy is also informative. Well-preserved complete specimens are characteristic of protected areas, whereas less well-preserved specimens occur in high-energy areas. The presence/absence of opercular plates is also particularly relevant for evaluating hydrodynamic conditions. As regards the C and O stable isotope ratios, due to the porous and coarse-grained nature of the deposits in which barnacle remains are usually found, the shells are often exposed to meteoric water percolation during diagenesis. Among the analysed specimens, only those collected from fine-grained deposits display no evidence of alteration and have isotopic ratios in line with those of their modern counterparts. These fossils display intra-shell variations that in modern barnacles have been related to short-term environmental changes (e.g., seasonal cycles). These results demonstrate that barnacles can always be useful for detailed palaeoenvironmental reconstructions based on skeletal assemblages. Furthermore, with further research aimed at gathering more data and assessing potentially interfering signals, they could bec

This paper analyses some key issues related to EU secondary law provisions regarding extraterritorial exercise of criminal jurisdiction by Member States over EU-harmonised offences. Two main research objectives have been examined: firstly, a theoretical systematisation of the phenomenon at stake; secondly, an assessment of its actual reach. The analysis firstly deals with the classical taxonomy of prescriptive, adjudicative and enforcement jurisdiction. It is then suggested that the extraterritorial application of EU criminal law entails a peculiar overlap between original and vicarious jurisdiction, as well as an intersection between territorial extension and effects-based jurisdiction. The paper then analyses the criteria used by the EU legislature to stretch Member States’ criminal jurisdiction beyond the EU’s external borders in light of Scott’s taxonomy, with a view to highlighting the emerging trends. Secondly, the paper underlines the emergence of an EU-own scope for extraterritorial jurisdiction, wider than the one designed by both International Crime Control Treaties and pre-Lisbon Framework Decisions. Finally, the paper calls upon an in-depth assessment of the practice related to the prosecution of EU-harmonised offences. Such an analysis would reveal if potential deal-breaking conflicts of jurisdiction between Member States and third States could lead to a reconsideration of the EU’s extraterritorial approach in criminal law.

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.

This paper contains two major contributions. First we derive, following the discrete de Rham (DDR) and Virtual Element (VEM) paradigms, pressure-robust methods for the Stokes equations that support arbitrary orders and polyhedral meshes. Unlike other methods presented in the literature, pressure-robustness is achieved here without resorting to an H(div)-conforming construction on a submesh, but rather projecting the volumetric force onto the discrete H(curl) space. The cancellation of the pressure error contribution stems from key commutation properties of the underlying DDR and VEM complexes. The pressure-robust error estimates in hk+1 (with h denoting the meshsize and k≥0 the polynomial degree of the DDR or VEM complex) are proven theoretically and supported by a panel of three-dimensional numerical tests. The second major contribution of the paper is an in-depth study of the relations between the DDR and VEM approaches. We show, in particular, that a complex developed following one paradigm admits a reformulation in the other, and that couples of related DDR and VEM complexes satisfy commuting diagram properties with the degrees of freedom maps.

This paper analyses some key issues related to EU secondary law provisions regarding extraterritorial exercise of criminal jurisdiction by Member States over EU-harmonised offences. Two main research objectives have been examined: firstly, a theoretical systematisation of the phenomenon at stake; secondly, an assessment of its actual reach. The analysis firstly deals with the classical taxonomy of prescriptive, adjudicative and enforcement jurisdiction. It is then suggested that the extraterritorial application of EU criminal law entails a peculiar overlap between original and vicarious jurisdiction, as well as an intersection between territorial extension and effects-based jurisdiction. The paper then analyses the criteria used by the EU legislature to stretch Member States’ criminal jurisdiction beyond the EU’s external borders in light of Scott’s taxonomy, with a view to highlighting the emerging trends. Secondly, the paper underlines the emergence of an EU-own scope for extraterritorial jurisdiction, wider than the one designed by both International Crime Control Treaties and pre-Lisbon Framework Decisions. Finally, the paper calls upon an in-depth assessment of the practice related to the prosecution of EU-harmonised offences. Such an analysis would reveal if potential deal-breaking conflicts of jurisdiction between Member States and third States could lead to a reconsideration of the EU’s extraterritorial approach in criminal law.

This study investigates Italian blending from a synchronic perspective, based on data extracted from a dictionary of neologisms attested over the past two decades. The first part of the paper addresses the boundaries between blends and other morphological units. Neologisms in which at least one of the two constituents has undergone a shortening have been classified into three categories (i.e., compounds with combining forms, secreted affixation, and blends), and described according to a set of semantic and formal parameters. The second part of the paper provides an updated description of Italian blending by analyzing a sample of 200 blends. The analysis reveals that Italian blends exhibit a wide variety of structures, according to the parameters of linearization, shortening, and overlap. Overlap, along with the phonological resemblance, proved to be a factor that favors blend formation. Moreover, it is shown that although blending in Italian is an irregular mechanism overall, in rare cases (such as that of -iota from idiota ‘idiot’), a blend's part may acquire regularity and affix-like properties.

Context: A landscape is defined as a “system of ecosystems” and this is a model in which karst areas can easily be integrated. In karst areas, much of the connectivity between the units of the landscape is underground, with aquifers and caves forming a continuous layered tissue. However, underground environments are among the least studied landscapes on Earth because of limited accessibility and the difficulty of performing surveys. Objectives: The aim of this paper is to provide a conceptual framework for applying principles of landscape ecology to research on karst environments. Methods: By adapting the standard patch-corridor-matrix model to a 3d model, the main issues that need to be addressed were identified. These include identifying the main morphological (surface and underground) karst features; determining the landscape structure through its features, composition, and configuration; and developing adequate indices. Results: The landscape spatial structure of different karst areas influences fundamental ecological functions and biodiversity patterns. Determining how structure, biodiversity, and functions relate reveals important insights into the functioning of karst systems. Emphasizing the provisioning of ecosystem services is essential in supporting the concept that karst regions are vital for human well-being because they host valuable resources and fundamental ecosystem processes. The paper discusses how this framework helps address anthropogenic impacts and conservation issues on karst. Conclusions: The potential of applying a landscape approach to karst systems lies in developing models that provide ecological information relevant to understanding karst systems and understanding their implications for natural resources management.

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 the last decade, the research literature has reached an enormous volume with an unprecedented current annual increase of 1.5 million new publications. As research gets ever more global and new countries and institutions, either from academia or corporate environments, start to contribute, it is important to monitor this complex phenomenon and understand its dynamics and equilibria. We present a study on a conference proceedings dataset extracted from Springer Nature SciGraph that illustrates insightful geographical trends and highlights the unbalanced growth of competitive research institutions worldwide in the 1996–2016 period. The main contribution of this work is fourfold. In the first instance, we found that the distributions of institutions and publications among countries follow a power law, consistently with previous literature, i.e., very few countries keep producing most of the papers accepted by high-tier conferences. Secondly, we show how the turnover rate of country rankings is extremely low and steadily declines over time, suggesting an alarmingly static landscape in which new entries struggle to emerge. We also performed an analysis of the venue locations and their effect on the distribution of countries involved in the publications, underlining the central role of Europe and China as knowledge hubs. Finally, we evidence the presence of an increasing gap between the number of institutions initiating and overseeing research endeavours (i.e. first and last authors’ affiliations) and the total number of institutions participating in research. The paper also discusses our experience in working with authors’ affiliations: an utterly simple matter at first glance, that is instead revealed to be a complex research and technical challenge.

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.

This paper looks at decentralisation as an institutional solution for curbing secessionist pressures by making potentially seceding regions strictly better off by staying in the union. We show that a bottom-up decentralisation reform, where single regions that can opt to assume or not stronger fiscal responsibilities on the basis of bilateral negotiations with the central government, may be more successful in avoiding instability and a secessionist conflict than more standard top-down decentralisation, where the central government assigns identical fiscal powers to all regions. The example of the decentralisation process in Spain over the last 40 years illustrates the relevance of the institutional pattern we analyse in the paper.

The COVID-19 pandemic has caused a global crisis, one which also influences the ways sustainability is being taught at universities. This paper undertakes an analysis of the extent to which COVID-19 as a whole and the lockdown it triggered in particular, which has led to the suspension of presence-based teaching in universities worldwide and influenced teaching on matters related to sustainable development. By means of a worldwide survey involving higher education institutions across all continents, the study has identified a number of patterns, trends and problems. The results from the study show that the epidemic has significantly affected teaching practices. The lockdowns have led to a surge in the use of on-line communication tools as a partial replacement to normal lessons. In addition, many faculty teaching sustainability in higher education have strong competencies in digital literacy. The sampled higher education educations have—as a whole—adequate infrastructure to continue to teach during the lockdowns. Finally, the majority of the sample revealed that they miss the interactions via direct face-to-face student engagement, which is deemed as necessary for the effective teaching of sustainability content. The implications of this paper are two-fold. Firstly, it describes how sustainability teaching on sustainable development has been affected by the lockdown. Secondly, it describes some of the solutions deployed to overcome the problem. Finally, the paper outlines the fact that the COVID-19 pandemic may serve the purpose of showing how university teaching on sustainability may be improved in the future, taking more advantage of modern information technologies.

Indexing very large collections of strings, such as those produced by the widespread next generation sequencing technologies, heavily relies on multi-string generalization of the Burrows-Wheeler Transform (BWT): large requirements of in-memory approaches have stimulated recent developments on external memory algorithms. The related problem of computing the Longest Common Prefix (LCP) array of a set of strings is instrumental to compute the suffix-prefix overlaps among strings, which is an essential step for many genome assembly algorithms. In a previous paper, we presented an in-memory divide-and-conquer method for building the BWT and LCP where we merge partial BWTs with a forward approach to sort suffixes. In this paper, we propose an alternative backward strategy to develop an external memory method to simultaneously build the BWT and the LCP array on a collection of m strings of different lengths. The algorithm over a set of strings having constant length k has O(mkl) time and I/O volume, using O(k+m) main memory, where l is the maximum value in the LCP array.

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 current coronavirus pandemic has produced severe consequences on economic and health systems all over the world, with the governments being challenged in searching for containment solutions balancing virus diffusion and limitations to social and work activities. In this paper, we propose a framework for the real-time optimization of restrictions in epidemics, based on the use of a time-varying SIRD model. Despite their simplicity, this class of models is able to capture the essential features of the epidemic spread, with the inherent parameter variation allowing accurate adaptation to real data. An optimization problem is formulated, properly balancing health and economic costs, and is solved parametrically by following a receding-horizon approach, resulting in an optimal sequence of social contact restrictions, which are assumed to be actuated via governmental containment measures. Numerical simulations based on the real data of the Italian COVID-19 emergency highlight the potential of the proposed approach and can be possibly helpful for the decision makers in present and future pandemics.

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study.

The method of characteristics is extended to set-valued Hamilton-Jacobi equations. This problem arises from a calculus of variations' problem with a multicriteria Lagrangian function: through an embedding into a set-valued framework, a set-valued Hamilton-Jacobi equation is derived, where the Hamiltonian function is the Fenchel conjugate of the Lagrangian function. In this paper a method of characteristics is described and some results are given for the Fenchel conjugate.

Using longitudinal data from the Italian National Institute for the Evaluation of the Education System (INVALSI), this paper investigates whether the ability of peers affects the educational attainment of immigrant students. We not only focus on the average quality of peers in the class, but also further investigate which part of the ability distribution of peers drives the effect, by assessing the role played by the extreme tails of the ability distribution. Our empirical strategy addresses students’ endogenous sorting into classes by exploiting the within-student across-subjects variation in achievements and the simultaneity problem by using predetermined measures of peers’ ability. We show that peers’ ability matters. While native students are mostly influenced by the average quality of their peers, immigrant children are detrimentally affected by the share of very low achievers in the classroom. Our findings provide valuable guidance to policymakers concerning the allocation of students to classes in order to foster immigrant students’ integration and learning.

Cellular Automata have successfully been successfully applied to the modeling and simulation of pedestrian and crowd dynamics. In particular, the investigated scenarios have often been focused on the evaluation of medium–high population density situations, in which the motivation of pedestrians to reach a certain location overcomes their tendency to naturally respect proxemic distances. The global COVID-19 outbreak, though, has shown that sometimes it is crucial to contemplate how proxemic tendencies are emphasized and amplified by the affective state of the individuals involved in the scenario, representing an important factor to take into consideration when investigating the behaviour of a crowd. In this paper we present a research effort aimed at integrating results of quantitative analyses regarding the effects of affective states on the perception of distances maintained by different types of pedestrians with the modeling of pedestrian movement choices in a cellular automata framework.

With Angle Resolved XRF (AR-XRF) the sample is investigated at different angles of detection and irradiation. The geometry of analysis indeed affects the intensity of the characteristic elemental fluorescence signal of the sample, which depends on the element distribution inside the sample. This technique has already proved to be very useful in analyzing metallic bilayer samples. In this paper, we applied AR-XRF to study a real case in the field of Cultural Heritage, with the analysis of a glazed ceramic coming from Puebla, Mexico. The analysis of the decorations, which present different thicknesses and compositions, has shown the limits of the technique, especially in the case of thick and diffused layers, and the importance of a priori knowledge to retrieve structural information in the case of a complex system with a great number of variables.

Explaining how two machine learning classification models differ in their behaviour is gaining significance in eXplainable AI, given the increasing diffusion of learning-based decision support systems. Human decision-makers deal with more than one machine learning model in several practical situations. Consequently, the importance of understanding how two machine learning models work beyond their prediction performances is key to understanding their behaviour, differences, and likeness. Some attempts have been made to address these problems, for instance, by explaining text classifiers in a time-contrastive fashion. In this paper, we present MERLIN, a novel eXplainable AI approach that provides contrastive explanations of two machine learning models, introducing the concept of model-contrastive explanations. We propose an encoding that allows MERLIN to work with both text and tabular data and with mixed continuous and discrete features. To show the effectiveness of our approach, we evaluate it on an extensive set of benchmark datasets. MERLIN is also implemented as a python-pip package.

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.

Social scientists have been aiming to calculate a “subjective income Gini coefficient”of survey respondents that would describe their beliefs about income inequality in their country. Niehues (Subjective perceptions of inequality and redistributive preferences: an international comparison, Cologne Institute for Economic Research, IWTRENDS Discussion Paper, 2014) derives this estimate from respondents’ beliefs about the relative sizes of different social classes (answers to “shape of society” questions), while Kuhn (The individual perception of wage inequality: a measurement framework and some empirical evidence, Technical report, Institute of Labor Economics (IZA), 2015) estimates it using beliefs about the pay structure. We combine their efforts to calculate what we call a twofold subjective Gini coefficient, which incorporates both pieces of information independently from one another. We present the country-level distribution of perceived and desired twofold subjective Gini coefficients using the ISSP Social Inequality V survey (ISSP Research Group in International social survey programme: social inequalityv—issp 2019, 2019. https://doi.org/10.4225/13/511C71F8612C3). Accounting for both subjective class structure and pay structure yields much lower perceived and desired levels of inequality. At the country level the averages of the twofold subjective Gini coefficients are closer to actual income Gini coefficients than the previous measures. At the individual level the twofold subjective Gini coefficients are better predictors of the individual’s verbal assessment of inequality and their preferences towards redistribution.

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, NICHE (Neutron Imaging for Cultural HEritage), the new neutron imaging facility of the Italian National Institute of Nuclear Physics (INFN) is shortly presented. We report on the main features of the beamline, the specifications of the detection set-up, and the performances of the imaging system. NICHE is installed at the TRIGA reactor of LENA (Laboratorio Energia Nucleare Applicata) Pavia University laboratory (Italy). The imaging facility was designed during 2020, installed in the spring 2021, and has been in operation since May 2021. NICHE is the first Italian neutron imaging station open to national external users through the INFN-CHNet network application system. NICHE allows users to obtain neutron radiographies and tomographies, thus allowing for morphological characterisation of samples. First results to highlight the potential of the facility are reported. In particular, we show the first results about spatial resolution measurement and 3D reconstruction to provide morphological analysis capabilities.

Leather represents the principal industrial product derived from the skin of animals. Leather manufacturing has evolved from artisanal practice, making use of vegetable tannins, to industrial production, today mostly based on chromium salts. Chromium tanning accounts for the most efficient and affordable process available on the market, but the environmental pressure caused by heavy metal pollution has pushed towards the development of Metal-Free leather tanning agents. This paper aims at highlighting the potentialities and limits of analytical pyrolysis to characterise metal-free leather samples and to identify the tanning agents. To this aim, thirty-three bovine split leather samples tanned with various metal-free tanning agents (constituted by single or combined formulations of GRANOFIN® F90, glutaraldehyde, tetrakis(hydroxymethyl)phosphonium sulphate, oxazolidine and three different synthetic tannins), provided by bovine split suppliers (Tuscany, Italy) were analysed by flash Pyrolysis coupled with Gas Chromatography - Mass Spectrometry (Py-GC-MS). For most of the tanning formulations, Py-GC-MS was able to determine pyrolytic markers in reference materials, intermediates (wet-white metal-free and metal-free crust) and end-products (metal-free final products). Evolved Gas Analysis coupled with Mass Spectrometry (EGA-MS) was used to evaluate, from the molecular point of view, the thermodegradative profiles of metal-free leathers and compare them with that of chromium tanned leather.

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.

This special section contains the extended version of four papers from the 32th IEEE International Symposium on Software Reliability Engineering (ISSRE 2021).

The increased demand for high-quality Internet connectivity resulting from the growing number of connected devices and advanced services has put significant strain on telecommunication networks. In response, cutting-edge technologies such as Network Function Virtualization (NFV) and Software Defined Networking (SDN) have been introduced to transform network infrastructure. These innovative solutions offer dynamic, efficient, and easily manageable networks that surpass traditional approaches. To fully realize the benefits of NFV and maintain the performance level of specialized equipment, it is critical to assess the behavior of Virtual Network Functions (VNFs) and the impact of virtualization overhead. This paper delves into understanding how various factors such as resource allocation, consumption, and traffic load impact the performance of VNFs. We aim to provide a detailed analysis of these factors and develop analytical functions to accurately describe their impact. By testing VNFs on different testbeds, we identify the key parameters and trends, and develop models to generalize VNF behavior. Our results highlight the negative impact of resource saturation on performance and identify the CPU as the main bottleneck. We also propose a VNF profiling procedure as a solution to model the observed trends and test more complex VNFs deployment scenarios to evaluate the impact of interconnection, co-location, and NFV infrastructure on performance.

In this paper, we study human–AI collaboration protocols, a design-oriented construct aimed at establishing and evaluating how humans and AI can collaborate in cognitive tasks. We applied this construct in two user studies involving 12 specialist radiologists (the knee MRI study) and 44 ECG readers of varying expertise (the ECG study), who evaluated 240 and 20 cases, respectively, in different collaboration configurations. We confirm the utility of AI support but find that XAI can be associated with a “white-box paradox”, producing a null or detrimental effect. We also find that the order of presentation matters: AI-first protocols are associated with higher diagnostic accuracy than human-first protocols, and with higher accuracy than both humans and AI alone. Our findings identify the best conditions for AI to augment human diagnostic skills, rather than trigger dysfunctional responses and cognitive biases that can undermine decision effectiveness.

This paper looks at the national and international geographic mobility paths of young graduates in Italy and their educational and professional trajectories. By departing from the research fields of youth studies, mobility studies and higher education studies, we aim to highlight the multiple meanings and effects that mobility experiences may have in structuring graduates’ future projects and/or desires. On the other hand, we argue that their expectations for both their mobility experiences and careers are also shaped by family socialisation, considering furthermore that the building of embodied and scholastic cultural capitals is spatially differentiated depending on the places where they grow up. The empirical research is based on two rounds of semi-narrative interviews conducted with 51 Italian graduates between 2020 and 2021. For the analysis, we have outlined four types of mobility paths. For each type, we focus on how socio-structural and cultural variables influence the ways the interviewees framed their mobility and professional experiences, desires and projects, focusing furthermore on how the Covid-19 pandemic differently affected their objective possibilities and strategies of mobility between the first and second waves.

In the effort to counteract problems associated with the current carbon intensive transport system, app-based tools persuading mobility behaviour change have emerged worldwide. Most of such apps adopt a gamified approach and motivate behaviour change through external extrinsic motivational factors such as real-life prizes, that are attributed based on the distance travelled by non-car transport modes. Despite this approach might be effective in promoting additional leisure trips by sustainable mobility, it might keep car-based commuting habits unaltered, or even stimulate unfair app behaviour to gain points. In this paper, we focus on the Bellidea persuasive app, that was co-designed with interested citizens in a Swiss-based living lab experiment, and present how we addressed the shortcomings of prize-based rewarding systems, while also dealing with the constraints imposed by current levels of accuracy in automatic transport mode detection. We illustrate and discuss our design choices and the related algorithmic solutions by referring to the following dilemmas: “single transport modes versus modal split”, “trust versus control”, “dynamism versus rigidity”, and “global versus local”. We conclude by analysing real-life mobility data-sets collected by the Bellidea app and discussing our design solutions against their capacity to attract its target user group, namely car driver individuals.

The availability of high-resolution data and accurate ground truth is essential to evaluate and compare methods and algorithms properly. Moreover, it is often difficult to acquire real data for a given application domain that is sufficiently representative and heterogeneous in terms of scene representation, acquisition conditions, point of view, etc. To overcome the limitations of available datasets, this paper presents a new synthetic, multi-purpose dataset called ENRICH for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images rendered with different lighting conditions, camera orientations, scales, and fields of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. We show the usefulness of the proposed dataset on several examples of photogrammetry and computer vision-related tasks such as: evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. We make ENRICH publicly available at: https://github.com/davidemarelli/ENRICH.

Digital tools, specifically smartphone apps, have emerged as enablers of social innovation for low-carbon transitions by using novel feedback to creatively engage people to act more sustainably, and thus capture the power of collective individual action. Such apps have increasingly been implemented in real-world experiments with positive results in the short-term. However critical reflection is required to look beyond this hype to understand the conditions for longer term impact, thus reaching a transformative social innovation potential. In this paper, we take two exemplary behaviour change apps and perform a cost-benefit analysis to assess the break-even point in number of users to achieve net-positive impact and discuss relevant technical, organisational, political and financial conditions that enable or impede this impact. We find that the required scale-up in users seems challenging, yet feasible. However, guaranteeing that the supportive conditions are available is necessary to warrant the focus on behaviour change apps by research and policy.

This paper dissects the dynamics of the hedge fund industry with four financial markets, including the equity market, commodities, currencies, and debt market by employing a large number of assets from these markets. We employ four main representative hedge fund strategy indices, and a cap-weighted global index to estimate an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model using daily data from April 2003 to May 2021. We break down the performance, riskiness, investing style, volatility, dynamic correlations, and shock transmissions of each hedge fund strategy thoroughly. Further, the impact of commodity futures basis on hedge funds' return is analyzed. Comparing the dynamic correlations during the 2008 global financial crisis (GFC) with COVID-19 pandemic reveals changing patterns in hedge funds' investing styles. There are strong and pervasive shock spillovers from hedge fund industry to other financial markets, especially to futures commodities. An increase in the futures basis of several commodities drives up hedge funds' performance. While hedge fund industry underperforms compared to equity market and commodities, the risk-reward measures show that hedge funds are superior to other markets, and safer than the bond market.

This paper is devoted to systematically extend f-mirror symmetry between families of hypersurfaces in complete toric varieties, as introduced in [17], to families of complete intersections subvarieties. Namely, f-mirror symmetry is induced by framed duality of framed toric varieties extending Batyrev-Borisov polar duality between Fano toric varieties. Framed duality has been defined and essentially well described for families of hypersurfaces in toric varieties in the previous [17]. Here it is developed for families of complete intersections, allowing us to strengthen some previous results on hypersurfaces. In particular, the class of projective complete intersections and their mirror partners are studied in detail. Moreover, a (generalized) Landau-Ginzburg/Complete-Intersection correspondence is discussed, extending to the complete intersection setup the LG/CY correspondence firstly studied Chiodo-Ruan and Krawitz.