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In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

In Italy, recent legislation (Law No. 10/2020) has tuned regulations concerning the donation of one's postmortem body and tissues for study, training, and scientific research purposes. This study discusses several specific issues to optimise the applicability and effectiveness of such an important, novel regulatory setting. Critical issues arise concerning the learners, the type of training and teaching activities that can be planned, the position of academic anatomy institutes, the role of family members in the donation process, the time frame of the donation process, the eligibility of partial donation, or the simultaneous donation of organs and tissues to patients awaiting transplantation. In particular, a universal time limit for donations (i.e., one year) makes it impossible to plan the long-term use of specific body parts, which could be effectively preserved for the advanced teaching and training of medical students and surgeons. The abovementioned conditions lead to the limited use of corpses, thus resulting in the inefficiency of the whole system of body donation. Overall, the donors’ scope for the donation of their body could be best honoured by a more flexible and tuneable approach that can be used on a case-by-case basis. Furthermore, it is deemed necessary to closely monitor the events scheduled for corpses in public nonacademic institutions or private enterprises. This paper presents useful insights from Italian anatomists with the hope of providing inspiration for drafting the regulations. In conclusion, this paper focuses on the critical issues derived from the recently introduced Italian law on the donation and use of the body after death and provides suggestions to lawmakers for future implementations.

Direct oral anticoagulants (DOAC) are mostly prescribed to prevent cardioembolic stroke in patients with non-valvular atrial fibrillation (AF). An increasing number of guidelines recommend DOAC in AF patients with preserved renal function for the prevention of thromboembolism and an increased use of DOAC in daily practice is recorded also in elderly patients. Aging is associated with a reduction of glomerular filtration rate and impaired renal function, regardless of the cause, increases the risk of bleeding. Multiple medication use (polypharmacy) for treating superimposed co-morbidities is common in both elderly and chronic kidney disease (CKD) patients and drug-drug interaction may cause accumulation of DOAC, thereby increasing the risk of bleeding. There is uncertainty on the safety profile of DOAC in patients with CKD, particularly in those with severely impaired renal function or end stage renal disease, due to the heterogeneity of studies and the relative paucity of data. This document reports the position of three Italian scientific societies engaged in the management of patients with atrial fibrillation who are treated with DOAC and present with CKD.

This book constitutes the refereed proceedings of the 3rd International Workshop, SAVE-SD 2017, held in Perth, Australia, in April 2017, and the 4th International Workshop, SAVE-SD 2018, held in Lyon, France, in April 2018. The 6 full, 2 position and 4 short papers were selected from 16 submissions. The papers describe multiple ways in which scholarly dissemination can be approved: Creating structured data, providing methods for semantic computational analysis and designing systems for navigating. This allows a variety of stakeholders to understand research dynamics, predict trends and evaluate the quality of research.

This is a position paper of the Radiopharmacy Committee of the EANM (European Association of Nuclear Medicine) addressing toxicology studies for application of new diagnostic and therapeutic radiopharmaceuticals (RP) that are not approved (i.e., not having a marketing authorization or a monograph in the European Pharmacopoeia), excluding endogenous and ubiquitous substances in humans. This paper discusses the requirements for clinical trials with radiopharmaceuticals for clinical research applications, not necessarily intended to aim at a marketing authorization. If marketing authorization is intended, scientific advice of the competent authorities is mandatory and cannot be replaced by this position paper. The position paper reflects the view of the Radiopharmacy Committee of the EANM and can be used as a basis for discussions with the responsible authorities.

This is a position paper of the Radiopharmacy Committee of the EANM (European Association of Nuclear Medicine) addressing toxicology studies for application of new diagnostic and therapeutic radiopharmaceuticals (RP) that are not approved (i.e., not having a marketing authorization or a monograph in the European Pharmacopoeia), excluding endogenous and ubiquitous substances in humans. This paper discusses the requirements for clinical trials with radiopharmaceuticals for clinical research applications, not necessarily intended to aim at a marketing authorization. If marketing authorization is intended, scientific advice of the competent authorities is mandatory and cannot be replaced by this position paper. The position paper reflects the view of the Radiopharmacy Committee of the EANM and can be used as a basis for discussions with the responsible authorities.

This book constitutes the refereed proceedings of the Second International Workshop on Semantics, Analytics, Visualization,- Enhancing Scholarly Data,- SAVE-SD 2016, held in Montreal, QC, Canada, in April 2016.

Nowadays, computing on encrypted data seems to be more practical than a few years ago, thanks to the emergence of new Homomorphic Encryption schemes. In this paper, an algorithm based on Homomorphic Encryption for Arithmetic of Approximate Numbers (Cheon et al., in: Takagi, Peyrin (eds) Advances in cryptology—ASIACRYPT 2017, Springer, Cham, pp 409–437, 2017) (HEAAN, or also CKKS) scheme, that is able to perform a secure k-means algorithm which processes encrypted data, has been studied and presented. The performance of the classifier running on encrypted data has been evaluated using a standard k-means algorithm that works on plain data as a supervised structure, since the results are obtained by approximated computations. The main point of this paper is to take existent theoretical techniques (for example approximations of sgn (x)), to use them and to observe if they are valid in practical applications. The output of the algorithm is a set of k encrypted masks that can be applied to the original dataset in order to obtain different clusters. The setting is a standard client–server one. The workload is heavily server-centric, as the client only has to execute a light masking algorithm at the end of each iteration, which, excluding the decryption, is faster than a plain k-means iteration; the main disadvantage concerns the accuracy of the results. Experiments show that the algorithm can be executed fairly quickly: the execution time of the training phase is on the order of seconds, while classification is on the order of tenths of a second.

Effective management of data is essential for successful ocean alkalinity enhancement (OAE) research, as it guarantees the long-term preservation, interoperability, discoverability, and accessibility of data. OAE research generates various types of data, such as discrete bottle measurements, autonomous measurements from surface underway and uncrewed platforms (e.g., moorings, Saildrones, gliders, Argo floats), physiological response studies (e.g., laboratory, mesocosm, and field experiments, and natural analogues), and model outputs. This paper addresses data and metadata standards for all these types of OAE data. As part of this study, existing data standards have been updated to accommodate OAE research needs, and a completely new physiological response data standard has been introduced. Additionally, an existing ocean acidification metadata template has been upgraded to be applicable to OAE research. This paper also presents controlled vocabularies for OAE research, including types of OAE studies, source materials for alkalinization, platforms, and instruments. These guidelines will aid OAE researchers in preparing their metadata and data for submission to permanent archives. Finally, the paper provides information about available data assembly centers that OAE researchers can utilize for their data needs. The guidelines outlined in this paper are applicable to ocean acidification research as well.

Social media platforms provide opportunities for users across the world to connect and communicate between them and engage into acts of social support and entertainment. Yet it can also bring negative consequences as it has been associated with poor mental health and life dissatisfaction. This underlines the importance of delivering social media literacy (SML) interventions that raise awareness of the dangers and threats that are hidden within. To this date, SML initiatives have shown their benefits towards the acquisition of SML skills through the forms of school interventions and mini-games. However, studies on promoting SML through social media platforms need to be also encouraged and innovative approaches to provide interactive scenarios with hands-on experiences need to be formulated. Hence, the project COURAGE introduces a new approach towards SML by proposing the integration of educational opportunities within a controlled social media platform. To provide students the opportunity to learn whilst they naturally explore social media we propose the integration of virtual learning companions. In this paper we report seven empirical approaches towards SML skills acquisition powered by virtual learning companions. The paper concludes with a discussion towards the benefits and limitations of using this type of SML interventions.

In this paper, we are interested in the asymptotic enumeration of Cayley graphs. It has previously been shown that almost every Cayley digraph has the smallest possible automorphism group: that is, it is a digraphical regular representation (DRR). In this paper, we approach the corresponding question for undirected Cayley graphs. The situation is complicated by the fact that there are two infinite families of groups that do not admit any graphical regular representation (GRR). The strategy for digraphs involved analysing separately the cases where the regular group R has a nontrivial proper normal subgroup N with the property that the automorphism group of the digraph fixes each N-coset setwise, and the cases where it does not. In this paper, we deal with undirected graphs in the case where the regular group has such a nontrivial proper normal subgroup.

Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

Background: Sustainable production and consumption are two important issues, which mutually interact. Whereas individuals have little direct influence on the former, they can play a key role on the latter. This paper describes the subject matter of sustainable consumption and outlines its key features. It also describes some international initiatives in this field. Results: By means of an international survey, the study explores the emphasis given to sustainable consumption during the second wave of the COVID-19 pandemic, and the degree of preparedness in individuals to engage in the purchase of green and sustainably manufactured products. The main results indicate that the pandemic offered an opportunity to promote sustainable consumption; nevertheless, the pandemic alone cannot be regarded as a ‘game changer’ in this topic. Conclusions: Apart from an online survey with responses from 31 countries, which makes it one of the most representative studies on the topic, a logit model was used to analyse the main variables that affect the probability of pro-environmental consumption behaviour because of the COVID-19 pandemic. The paper lists some of the technological and social innovations that may be needed, so as to guide more sustainable consumption patterns in a post-pandemic world.

Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

The European Biological Variation Study (EuBIVAS), which includes 91 healthy volunteers from five European countries, estimated high-quality biological variation (BV) data for several measurands. Previous EuBIVAS papers reported no significant differences among laboratories/population; however, they were focused on specific set of measurands, without a comprehensive general look. The aim of this paper is to evaluate the homogeneity of EuBIVAS data considering multivariate information applying the Principal Component Analysis (PCA), a machine learning unsupervised algorithm. The EuBIVAS data for 13 basic metabolic panel linked measurands (glucose, albumin, total protein, electrolytes, urea, total bilirubin, creatinine, phosphatase alkaline, aminotransferases), age, sex, menopause, body mass index (BMI), country, alcohol, smoking habits, and physical activity, have been used to generate three databases developed using the traditional univariate and the multivariate Elliptic Envelope approaches to detect outliers, and different missing-value imputations. Two matrix of data for each database, reporting both mean values, and "within-person BV"(CVP) values for any measurand/subject, were analyzed using PCA. A clear clustering between males and females mean values has been identified, where the menopausal females are closer to the males. Data interpretations for the three databases are similar. No significant differences for both mean and CVPs values, for countries, alcohol, smoking habits, BMI and physical activity, have been found. The absence of meaningful differences among countries confirms the EuBIVAS sample homogeneity and that the obtained data are widely applicable to deliver APS. Our data suggest that the use of PCA and the multivariate approach may be used to detect outliers, although further studies are required.

The aim of the paper is to analyze the state of the art of bottom-up actions that lead to cultural and social regeneration using an in-depth case study in the city of Rome: the MAAM (Museum of the Other and the Elsewhere), a converted factory that has been occupied and it has a twofold use: it is a social housing and a contemporary art museum. The objectives of this paper are: a) a literature review of bottom up organizations that are able to transforms from illegal to institutional (still remaining illegal from a juridical point of view) b) a qualitative descriptive analysis of the case study MAAM enriched by interviews ad hoc to the “pioneers” on cultural processes and experiences in the studied areas c) a documental analysis of the case study. In particular, the authors have organized the parameters into eight specific categories through the information analyzed in the documents, that help to have a mapping of the practices, their similarities and distinctions. The scheme is useful to replicate the study in other Urban areas and compare the results.

The issue of vulnerability and robustness in networks have been addressed by several methods. The goal is to identify which are the critical components (i.e., nodes/edges) whose failure impairs the functioning of the network and how much this impacts the ensuing increase in vulnerability. In this paper we consider the drop in the network robustness as measured by the increase in vulnerability of the perturbed network and compare it with the original one. Traditional robustness metrics are based on centrality measures, the loss of efficiency and spectral analysis. The approach proposed in this paper sees the graph as a set of probability distributions and computes, specifically the probability distribution of its node to node distances and computes an index of vulnerability through the distance between the node-to-node distributions associated to original network and the one obtained by the removal of nodes and edges. Two such distances are proposed for this analysis: Jensen–Shannon and Wasserstein, based respectively on information theory and optimal transport theory, which are shown to offer a different characterization of vulnerability. Extensive computational results, including two real-world water distribution networks, are reported comparing the new approach to the traditional metrics. This modelling and algorithmic framework can also support the analysis of other networked infrastructures among which power grids, gas distribution and transit networks.

In the financial framework, the concepts of connectedness and diversification have been introduced and developed respectively in the context of systemic risk and portfolio theory. In this paper we propose a theoretical approach to bring to light the relation between connectedness and diversification. Starting from the respective axiomatic definitions, we prove that a class of proper measures of connectedness verifies, after a suitable functional transformation, the axiomatic requirements for a measure of diversification. The core idea of the paper is that connectedness and diversification are so deeply related that it is possible to pass from one concept to the other. In order to exploit such correspondence, we introduce a function, depending on the classical notion of rank of a matrix, that transforms a suitable proper measure of connectedness in a measure of diversification. We point out general properties of the proposed transformation function and apply it to a selection of measures of connectedness, such as the well-known Variance Inflation Factor.

Increasing road traffic levels in urban areas require actions and policies to manage and control the number of road users. Travelers' choices of transport modes, particularly private cars, that generate the main share of road traffic levels, depend on many factors, which include both personal preferences and level-of-service variables. Understanding how travelers choose transport modes according to the above factors is an important challenge in order to adopt the most suitable policies and facilitate a sustainable mobility. In the literature, behavioral models have been mainly proposed in order to both estimate mode choice percentages and capture travel behaviors by suitable estimation of some parameters associated to the above factors. However, behavior is complex in itself and the mechanisms underlying user behavior might be difficult to be captured by traditional models. In this paper, a neuro-fuzzy approach is proposed to extract mode choice decision rules by evaluating different sets of rules and different membership functions of the neuro-fuzzy model. Particularly, to determine which inputs are the most relevant in such decision process, fuzzy curves and surfaces have been considered in order to take into account nonlinear effects. The neuro-fuzzy model proposed in this paper has been thought to be embedded in an agent-based methodological framework where user agents - representing travelers - make travel choices based on the rules learnt by means of the neuro-fuzzy system.

Increasing road traffic levels in urban areas require actions and policies to manage and control the number of road users. Travelers' choices of transport modes, particularly private cars, that generate the main share of road traffic levels, depend on many factors, which include both personal preferences and level-of-service variables. Understanding how travelers choose transport modes according to the above factors is an important challenge in order to adopt the most suitable policies and facilitate a sustainable mobility. In the literature, behavioral models have been mainly proposed in order to both estimate mode choice percentages and capture travel behaviors by suitable estimation of some parameters associated to the above factors. However, behavior is complex in itself and the mechanisms underlying user behavior might be difficult to be captured by traditional models. In this paper, a neuro-fuzzy approach is proposed to extract mode choice decision rules by evaluating different sets of rules and different membership functions of the neuro-fuzzy model. Particularly, to determine which inputs are the most relevant in such decision process, fuzzy curves and surfaces have been considered in order to take into account nonlinear effects. The neuro-fuzzy model proposed in this paper has been thought to be embedded in an agent-based methodological framework where user agents - representing travelers - make travel choices based on the rules learnt by means of the neuro-fuzzy system.

The aim of this paper is to explore how debt contracts are affected by investment in asset specialization and by the dynamics of the secondary market for collateralized productive assets. Before applying for a loan, financially constrained firms face a specificity trade-off: asset specialization increases firms’ project returns, but decreases the liquidation value of assets in the secondary market if the firm is in financial distress. To study this trade-off, the paper uses a theoretical model in which the choice of asset specificity and the outcome of the secondary market for distressed firms’ assets are endogenous. High redeployability costs and a small number of participants in the secondary market are associated to low recovery values and to a high cost of debt. The paper shows the conditions under which financial constraints reduce firms’ incentive to invest in asset specificity.

After some promise in the 1990s, European unions have grown increasingly disillusioned with regard to the results of EU social policy and EU social dialogue. The paper analyses the extent and reasons of this disillusion by looking at the impact on social dialogue of the Active Inclusion Recommendation launched by the European Commission at the outset of the economic crisis in 2008. The Recommendation led to a tripartite framework agreement at the EU level in 2010 (the only such agreement in a decade), which was then to be implemented at national and regional levels. With a multilevel governance approach, the paper looks at the extent to which social dialogue on Active Inclusion at the EU level, in six EU countries (France, Italy, Poland, Spain, Sweden, and the UK) and six regions (Rhône-Alpes, Lombardy, Lower Silesia, Catalonia, West Sweden and Greater Manchester) within those countries was somehow revitalised. The analysis, looking at both top-down and bottom-up processes and based on documentary analysis and interviews, shows that the initiative displays ambiguities similar to those of typical composite EU principles, such as famously the case of ‘flexicurity’. The multilevel governance of the EU, including the interaction between ‘soft’ employment policies and evolving ‘hard’ Eurogovernance tools, and with poor horizontal and vertical coordination, resulted in multiple distortions of the principle and, over time, to frustration. Unions’ engagement varies by level, country and region, reflecting both traditional national approaches and the local perception of ‘active inclusion’ as an opportunity. Although trade unions were more welcoming of ‘active inclusion’ than they had been for flexicurity, similar related threats and opportunities led to modest achievements and a gradual fading of the idea at the European and national levels, with some more opportunities however at the regional level. The paper concludes that, if trade unions want to engage with the idea of a

This paper deals with the existence of nontrivial solutions for critical possibly degenerate Kirchhoff fractional (p, q) systems. For clarity, the results are first presented in the scalar case, and then extended into the vectorial framework. The main features and novelty of the paper are the (p, q) growth of the fractional operator, the double lack of compactness as well as the fact that the systems can be degenerate. As far as we know the results are new even in the scalar case and when the Kirchhoff model considered is non-degenerate.

Software enforcers can be used to modify the runtime behavior of software applications to guarantee that relevant correctness policies are satisfied. Indeed, the implementation of software enforcers can be tricky, due to the heterogeneity of the situations that they must be able to handle. Assessing their ability to steer the behavior of the target system without introducing any side effect is an important challenge to fully trust the resulting system. To address this challenge, this paper presents Test4Enforcers, the first approach to derive thorough test suites that can validate the impact of enforcers on a target system. The paper also shows how to implement the Test4Enforcers approach in the DroidBot test generator to validate enforcers for Android apps.

Counting integer points in large convex bodies with smooth boundaries containing isolated flat points is oftentimes an intermediate case between balls (or convex bodies with smooth boundaries having everywhere positive curvature) and cubes (or convex polytopes). In this paper we provide a detailed description of several discrepancy problems in the particular planar case where the boundary is locally flat of finite order. We consider both integer points problems and irregularities of distribution problems. The paper is self-contained. The results on irregularities of distribution are new.

In early childhood education and care services, documentation is seen as an instrument for ‘giving children voice’ and for engaging children in their educational process (Formosinho and Pascal 2016; Kroll and Meier 2018; Robertson et al. 2017). At the same time, documentation plays a key role within the decision-making process in which children should be allowed to participate: it is the space where the adults’ decisions are made and shared, whereas within the child protection system, it remains more likely to reflect the adults’ voice (Caldwell et al. 2019). Thus, documentation, especially when in written alphabetic form, illustrates the power of writing and written text (Biffi 2019b). Given that alternative care services are institutional contexts in which the exercise of power and control is inevitable and determined by a given ‘dispositif’ as defined by Michael Foucault (1975), documentation can represent a strategy for giving voice to children or—on the contrary—a strategy for objectivating them, and thus preventing them from authentically engaging with their own care plans. This paper, by drawing on the different meanings held by documentation in ECEC contexts, in terms of viewing it as ‘equipped with agentic power’ (Alasuutari and Kelle 2015) reflects on the meanings of (pedagogical) documentation in alternative care settings, as a transitional space between ‘being spoken for’ and ‘speaking for oneself’, in light of a rights-based and pedagogical framework. A further aim of the paper is to explore possible approaches enabling children to participate in the documentation process, focusing on the practices able to involve children in the writing and reading of documentation concerning themselves.

The Proof-of-Stake (PoS) consensus algorithm has been criticized, in the literature and in several cryptocurrencies communities, due to the so-called compounding effect: who is richer has more tokens to put in stake, hence higher probability of being selected as a block validator and obtain the corresponding rewards, thus becoming even richer. In this paper we present a PoS simulator written in the R language, that allows to tweak several parameters of the consensus algorithm and observe how the distribution of tokens among the users evolves over time. This tool can be used to investigate which combinations of parameter values allow to obtain a "fair" consensus algorithm, in which no one gets richer or poorer by the mere act of validating blocks.

Artificial Neural Networks (NNs) have been recognized as a powerful tool for automatically learning complex relationships in data. In this paper, we propose to apply such a tool for modeling forest regeneration, a possibility not yet investigated in the literature. In order to evaluate the capability of NNs to simulate initial recruitment of pine species in Mediterranean forests, a feed-forward multi-layer neural network has been applied to seed germination and seedling survival of four pine species under three soil conditions, with or without seed protection, in Castilla La Mancha (Central-Eastern Spain). The experimental campaign has shown good performance in predicting the two pine initial recruitment stages. The proposed approach may help to predict the success of natural regeneration in Mediterranean pine forests under different basal areas and management strategies.

This paper provides new empirical evidence on the boundaries of the firm, as shaped by the ownership (make-or-buy) and location (domestic-or-foreign) decisions of sourcing. In particular, we draw on the Grossman–Hart–Moore framework to investigate the role of input characteristics, investment spillovers and firm productivity in ownership and location decisions. For the purpose of the empirical analysis, we rely on original survey data of a stratified sample of Italian manufacturing firms, headquartered in Lombardy. Our probit, multinomial probit and conditional mixed process estimations suggest a number of robust regularities. Some of them confirm so far unexplored theoretical predictions from the Grossman–Hart–Moore framework; others provide new insights on specific relationships on which the theory is silent. As for ownership, we find that reliance on specific inputs and intangible inputs fosters integration over non-integration; moreover, firms acknowledging cross spillover effects are more likely to opt for joint-venture than non-integration. As for location, domestic sourcing prevails over foreign sourcing in presence of investment spillovers, whereas input characteristics play no role. Lastly, productivity is a major driver of the boundaries of the firm in that productive firms are more likely to source abroad than domestically. Holding across different econometric models and a number of robustness checks, our results contribute to the property rights theory of the firm and its recent developments.

One of the goals of pathology is to standardize laboratory practices to increase the precision and effectiveness of diagnostic testing, which will ultimately enhance patient care and results. Standardization is crucial in the domains of tissue processing, analysis, and reporting. To enhance diagnostic testing, innovative technologies are also being created and put into use. Furthermore, although problems like algorithm training and data privacy issues still need to be resolved, digital pathology and artificial intelligence are emerging in a structured manner. Overall, for the field of pathology to advance and for patient care to be improved, standard laboratory practices and innovative technologies must be adopted. In this paper, we describe the state-of-the-art of automation in pathology laboratories in order to lead technological progress and evolution. By anticipating laboratory needs and demands, the aim is to inspire innovation tools and processes as positively transformative support for operators, organizations, and patients.

When analyzing data in contingency tables it is frequent to deal with sparse data, particularly when the sample size is small relative to the number of cells. Most analyses of this kind are interpreted in an exploratory manner and even if tests are performed, little attention is paid to statistical power. This paper proposes a method we call redundant procedure, which is based on the union–intersection principle and increases test power by focusing on specific components of the hypothesis. This method is particularly helpful when the hypothesis to be tested can be expressed as the intersections of simpler models, such that at least some of them pertain to smaller table marginals. This situation leads to working on tables that are naturally denser. One advantage of this method is its direct application to (chain) graphical models. We illustrate the proposal through simulations and suggest strategies to increase the power of tests in sparse tables. Finally, we demonstrate an application to the EU-SILC dataset.

Standard procedures for stability assessment of unstable trees are based, among other, on the interpretation of on-site, non-destructive static pulling tests. To this goal, a simple phenomenological equation is usually adopted in professional agronomic practice, and an estimation of the ultimate toppling resistance is extrapolated by fitting the test data, without taking root geometrical parameters and soil mechanical properties into account. From a geotechnical point of view, however, the root plate of a tree plays the role of a “living foundation”, and its behaviour under toppling actions (like those produced by intense wind gusts) conceptually corresponds to the mechanical response of shallow foundations under rocking loads. In the paper, several static pulling tests on real-scale trees (some of them have been run until the complete collapse, after some unloading–reloading cycles) and some tests taken from the literature are considered in order to investigate the toppling behaviour. A possible new interpretative equation is proposed and critically compared with the existing one against experimental results. The new equation allows for a mechanically meaningful description of the toppling curve of the tree and accounts for strength and deformability issues. It allows to introduce innovative “performance-based” approaches, which are commonly neglected by practitioners and professional agronomists in this field. Nevertheless, the experimental results show that tree toppling is a complex phenomenon, and capturing its failure condition requires more advanced multi-mechanism models and second-order effects to be accounted for. From a practical point of view, the proposed equation, employed within the same standard interpretative procedure currently adopted in practice for pulling tests, seems to provide conservative estimations of “operational” values of the ultimate toppling resistance, and in perspective, it could be used to significantly optimize—when needed—the des

In this paper, we study multiplicity results for double phase problems of Kirchhoff type with right-hand sides that include a parametric singular term and a nonlinear term of subcritical growth. Under very general assumptions on the data, we prove the existence of at least two weak solutions that have different energy sign. Our treatment is based on the fibering method in form of the Nehari manifold. We point out that we cover both the nondegenerate as well as the degenerate Kirchhoff case in our setting.

This paper studies the relationship between firms’ strategy and policies in regard to social media and their employees’ propensity to endorse them by using their personal social media accounts. In particular, the study investigates the effect of employees’ perception of firms’ social media strategy and initiatives aimed at influencing employees’ behavior on their personal social media profiles (communication of policies on the use of social media, training programs, and encouragement to join social media conversations regarding the firm). Based on the responses of 224 employees who use their personal accounts to talk about their firms, findings show that employees’ positive evaluation of firms’ social media strategy and firms’ explicit encouragement are positively associated with employees’ propensity to endorse their firms on social media. Moreover, results reveal the moderating effect of employees’ frequency of social media use on the relationship between communication of social media policies and the employees’ propensity to endorse their firm, as well as on the relationship between training programs and the propensity to endorse. This study provides evidence of the influence of firms’ social media activity and policies on the willingness of employees to promote and advocate their employers using their personal accounts, with theoretical and practical implications. The research also suggests that the effectiveness of firms’ policies may differ according to the frequency of social media usage by employees.

Recently an algorithm to dualize a theory into its mirror dual has been proposed, both for 3d N = 4 linear quivers and for their 4d N = 1 uplift. This mimics the manipulations done at the level of the Type IIB brane setup that engineers the 3d theories, where mirror symmetry is realized as S-duality, but it is enirely field-theoretic and based on the application of genuine infra-red dualities that implement the local action of S-duality on the quiver. In this paper, we generalize the algorithm to the full duality group, which is SL(2, ℤ) in 3d and PSL(2, ℤ) in 4d. This also produces dualities for 3d N = 3 theories with Chern-Simons couplings, some of which have enhanced N = 4 supersymmetry, and their new 4d N = 1 counterpart. In addition, we propose three ways to study the RG flows triggered by possible VEVs appearing at the last step of the algorithm, one of which uses a new duality that implements the Hanany-Witten move in field theory.

The Engel graph of a finite group G is a directed graph encoding the pairs of elements in G satisfying some Engel word. Recent work of Lucchini and the third author shows that, except for a few well-understood cases, the Engel graphs of almost simple groups are strongly connected. In this paper, we give a refinement to this analysis.

University–industry collaborations are an important pathway through which academic scientists engage with industry and society, co-create new knowledge, and raise funds to carry out costly research endeavors. Nonetheless, the management of such collaborations is challenging and requires universities to protect their investments in intellectual property and to capture value from them. This paper examines how scientists’ motivations to undertake inventive activities shape the relationship between research partnerships, the ownership of academic patents resulting from such partnerships, and the licensing of university-owned patents. We examine the interactions between these factors using data on 501 research projects conducted by scientists affiliated with universities located in various countries. Our analysis indicates that the inventors’ motivations bear a direct effect on the ownership and commercialization of academic patents. Moreover, these motivations positively moderate the relationship between research partnerships and the management of academic patents. These findings have interesting implications for university administrators striving to enhance the effectiveness of the technology transfer process.

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 ± 0.6 % and 84.1 ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

It is a classical theorem of Sarason that an analytic function of bounded mean oscillation (BMOA) is of vanishing mean oscillation if and only if its rotations converge in norm to the original function as the angle of the rotation tends to zero. In a series of two papers, Blasco et al. have raised the problem of characterizing all semigroups of holomorphic functions .'t/ that can replace the semigroup of rotations in Sarason’s theorem. We give a complete answer to this question, in terms of a logarithmic vanishing oscillation condition on the infinitesimal generator of the semigroup .'t/. In addition, we confirm the conjecture of Blasco et al. that all such semigroups are elliptic. We also investigate the analogous question for the Bloch and the little Bloch spaces, and surprisingly enough, we find that the semigroups for which the Bloch version of Sarason’s theorem holds are exactly the same as in the BMOA case.

In this paper I explore processes of knowledge production and circulation within a specific research community: The self-identified community of mixed methods scholars—i.e., the group of researchers adopting a mixed methods approach and using the label mixed methods— during the phase of its emergence and institutionalization. I focus on citations within this community, considering that the act of citing is linked to the intention of scholars to position their work not only within a research area but also within a community contributing to that specific area. I employed strategies from citation network analysis (CNA) to understand the fields involved, as well as the structure of the community in relation to citation practices. I identified the most common subjects and methodological fields in which mixed methods are mentioned by isolating sub- communities and the most influential authors in the network. I discuss the implications of this network structure with regard to power relations and hegemony. This also includes the function of nodes which appear to be marginal, but are relevant in citation practices since these authors play a bridging role across the various sub-communities.

When a distributional model is chosen, the analytic relation between its shape parameters and the values taken by some kurtosis indexes, especially if they are unconventional, is rarely known. In addition, different indexes may provide contrasting evidence about the level of global kurtosis, when the parameters of the model are varied. That happens because just few parameters act “plainly” on kurtosis, namely so as to produce consistent modifications of the shape of the graph on both its sides. Many parameters, instead, affect kurtosis along with a change of the skewness of the distribution, that is by “inflating” a single side of the graph (usually a tail) at the expense of the other. Thanks to some relevant examples, this paper tries to provide general indications to recognize the two kinds of parameters above and to interpret their effect on the classical Pearson’s standardized fourth moment and on some lesser known kurtosis indexes. Specifically, it is shown that only a decomposed analysis of indexes can help to understand their apparent contradictions, especially when some of them are too sensitive to changes in the tails. Finally, some applications are provided.

Social media are offering new opportunities for communication and interaction way beyond what was possible only a few years ago. However, social media are also virtual spaces where young people are exposed to a variety of threats. Digital addiction, discrimination, hate speech, misinformation, polarization as well as manipulative influences of algorithms, body stereotyping, and cyberbullying are examples of challenges that find fertile ground on social media. Educators and students are not adequately prepared to face these challenges. To this aim, the COURAGE project, presented in this paper, introduces new tools and learning methodologies that can be adopted within higher education learning paths to train educators to deal with social media threats. The overarching principles of the COURAGE project leverage the most recent advances in the fields of artificial intelligence and in the educational domain paired with social and media psychological insights to support the development of the COURAGE ecosystem. The results of the experiments currently implemented with teachers and students of secondary schools as well as the impact of the COURAGE project on societal changes and ethical questions are presented and discussed.

This paper proposes an M-quantile regression approach to address the heterogeneity of the housing market in a modern European city. We show how M-quantile modelling is a rich and flexible tool for empirical market price data analysis, allowing us to obtain a robust estimation of the hedonic price function whilst accounting for different sources of heterogeneity in market prices. The suggested methodology can generally be used to analyse nonlinear interactions between prices and predictors. In particular, we develop a spatial semiparametric M-quantile model to capture both the potential nonlinear effects of the cultural environment on pricing and spatial trends. In both cases, nonlinearity is introduced into the model using appropriate bases functions. We show how the implicit price associated with the variable that measures cultural amenities can be determined in this semiparametric framework. Our findings show that the effect of several housing attributes and urban amenities differs significantly across the response distribution, suggesting that buyers of lower-priced properties behave differently than buyers of higher-priced properties.

A Composite Indicator (CI) is a useful tool to synthesize information on a multidimensional phenomenon and make policy decisions. Multidimensional phenomena are often modeled by hierarchical latent structures that reconstruct relationships between variables. In this paper, we propose an exploratory, simultaneous model for building a hierarchical CI system to synthesize a multidimensional phenomenon and analyze its several facets. The proposal, called the Ultrametric Composite Indicator (UCI) model, reconstructs the hierarchical relationships among manifest variables detected by the correlation matrix via an extended ultrametric correlation matrix. The latter has the feature of being one-to-one associated with a hierarchy of latent concepts. Furthermore, the proposal introduces a test to unravel relevant dimensions in the hierarchy and retain statistically significant higher-level CIs. A simulation study is illustrated to compare the proposal with other existing methodologies. Finally, the UCI model is applied to study Italian municipalities’ behavior toward waste management and to provide a tool to guide their councils in policy decisions.

This paper examines the diachrony of two neoclassical prefixes in Italian, namely semi- (from Latin sēmi- ‘half’) and para- (from Ancient Greek parà close to’), based on data extracted from corpora. The diachronic analysis reveals that until the 20th century, both the prefixes semi- and para- were mostly used in scientific terminology, where they conveyed their original meanings: degree and spatial proximity respectively. A cursory synchronic analysis based on data extracted from two newspaper corpora shows that in the last decades, semi- and para- have been productively used to express evaluative values (i.e., attenuation and approximation). The present study enriches the literature on the sources of approximating morphological markers and the relationship between attenuation and approximation, considered here as two distinct values placed along a continuum.

Corporate reputation (CR) can support a business from different perspectives, significantly affecting its key outcomes; however, the relationship between CR and customer citizenship behaviour (CCB) has received little consideration in research so far. The aim of this paper is therefore dual: to study the direct effect of CR on three different dimensions of CCB; and to examine the moderating effects of personality differences (extroversion and neuroticism) and gender in relation to the respective relationships. This investigation employs structural equation modelling using a sample of 278 consumers of retail fashion in Italy. The results show that CR positively affects the three considered dimensions of CCB. Even though no significant moderating effect of the considered personality traits was found, the connection between CR and helping others appears to be significantly moderated by gender, suggesting that females are more willing to help others than males.

Child maltreatment has detrimental social and health effects for individuals, families and communities. The ERICA project is a pan-European training programme that equips non-specialist threshold practitioners with knowledge and skills to prevent and detect child maltreatment. This paper describes and presents the findings of a rapid review of good practice examples across seven participating countries including local services, programmes and risk assessment tools used in the detection and prevention of child maltreatment in the family. Learning was applied to the development of the generic training project. A template for mapping the good practice examples was collaboratively developed by the seven participating partner countries. A descriptive data analysis was undertaken organised by an a priori analysis framework. Examples were organised into three areas: programmes tackling child abuse and neglect, local practices in assessment and referral, risk assessment tools. Key findings were identified using a thematic approach. Seventy-two good practice examples were identified and categorised according to area, subcategory and number. A typology was developed as follows: legislative frameworks, child health promotion programmes, national guidance on child maltreatment, local practice guidance, risk assessment tools, local support services, early intervention programmes, telephone or internet-based support services, COVID-19 related good practices. Improved integration of guidance into practice and professional training in child development were highlighted as overarching needs. The impact of COVID-19 on safeguarding issues was apparent. The ERICA training programme formally responded to the learning identified in this international good practice review.