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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.

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.

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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

In this paper, we provide novel definitions of clustering coefficient for weighted and directed multilayer networks. We extend in the multilayer theoretical context the clustering coefficients proposed in the literature for weighted directed monoplex networks. We quantify how deeply a node is involved in a cohesive structure focusing on a single node, a single layer or the entire system, respectively. The coefficients convey several characteristics inherent to the complex topology of the multilayer network. We test their effectiveness applying them to a particularly complex structure such as the international trade network. The trade data integrate different aspects and they can be described by a directed and weighted multilayer network, where each layer represents import and export relationships between countries for a given sector. The proposed coefficients find successful application in describing the interrelations of the trade network, allowing to disentangle the effects of countries and sectors and jointly consider the interactions between them.

The way agents form their expectations, and which equilibria are consequently selected, are issues that come over macroeconomic growth models that study the transition between states characterized by different levels of capital. Existing theoretical works in this area have been mostly dominated by perfect foresight models although alternative expectations schemes have been proposed in the last decades. In the present paper we propose an OLG model with capital accumulation assuming that agents employ simple yet heterogeneous rules to forecast the future course of the interest rate and the real wage. The forecasting rules are selected according to an evolutionary mechanism that, on the basis of a fitness measure, evaluates the performance of the heuristics agents are currently adopting. We analytically show that, in the heterogeneous setting we consider, multiple equilibria are possible, but the evolutionary heuristic switching allows for a decrease in the number of possible steady states, drives their selection, and reduces the actual possibility that the economy might be locked into a poverty trap. (c) 2022 Elsevier B.V. All rights reserved.

Outcomes observed in laboratory experiments on contests are often not consistent with the results expected by theoretical models, with phenomena that frequently occur like overbidding and, less frequently, underbidding, and persisting oscillations in strategic choices. Several explanations have been suggested to understand such phenomena, dealing primarily with equilibrium analysis. We propose a dynamical model based on the coevolution of strategic choices and agent preferences. Each agent can have non self-interested preferences, which influence strategic choices and in turn evolve according to them. We show that multiple coexisting steady states characterized by non self-interested preferences can exist, and they lose stability as the prize increases, leading to endogenous oscillating dynamics. Finally, with an emphasis on two specific kinds of agents, we explain how overbidding can emerge. The numerical results show a good qualitative agreement with the experimental data. The model and the analysis of this paper are interesting not only to explain real life phenomena observed in experimental settings, but also as a methodological contribution to analyze the interplay between preferences and choices in conflict situations.

DARTWARS (Detector Array Readout with Traveling Wave AmplifieRS) is a three years project that aims to develop high-performing innovative Traveling Wave Parametric Amplifiers (TWPAs) for low temperature detectors and qubit readout (C-band). The practical development follows two different promising approaches, one based on the Josephson junctions (TWJPA) and the other one based on the kinetic inductance of a high-resistivity superconductor (KITWPA). This paper presents the advancements made by the DARTWARS collaboration to produce a first working prototype of a KITWPA.

In this paper, we consider policy interventions for international migrant flows and quantify their ramifications. In particular, we further develop a recent equilibrium model of international human migration in which some of the destination countries form coalitions to establish a common upper bound on the migratory flows that they agree to accept jointly. We also consider here a scenario where some countries can leave or join an initial coalition and investigate the problem of finding the coalitions that maximize the overall social welfare. Moreover, we compare the social welfare at equilibrium with the one that a supranational organization might suggest in an ideal scenario. This research adds to the literature on the development of mathematical models to address pressing issues associated with problems of human migration with insights for policy and decision-makers.

Background: Liquid biopsy (LB) is a rapidly evolving diagnostic tool for precision oncology that has recently found its way into routine practice as an adjunct to tissue biopsy (TB). The concept of LB refers to any tumor-derived material, such as circulating tumor DNA (ctDNA) or circulating tumor cells that are detectable in blood. An LB is not limited to the blood and may include other fluids such as cerebrospinal fluid, pleural effusion, and urine, among others. Patients and methods: The objective of this paper, devised by international experts from various disciplines, is to review current challenges as well as state-of-the-art applications of ctDNA mutation testing in metastatic non-small-cell lung cancer (NSCLC). We consider pragmatic scenarios for the use of ctDNA from blood plasma to identify actionable targets for therapy selection in NSCLCs. Results: Clinical scenarios where ctDNA mutation testing may be implemented in clinical practice include complementary tissue and LB testing to provide the full picture of patients’ actual predictive profiles to identify resistance mechanism (i.e. secondary mutations), and ctDNA mutation testing to assist when a patient has a discordant clinical history and is suspected of showing intertumor or intratumor heterogeneity. ctDNA mutation testing may provide interesting insights into possible targets that may have been missed on the TB. Complementary ctDNA LB testing also provides an option if the tumor location is hard to biopsy or if an insufficient sample was taken. These clinical use cases highlight practical scenarios where ctDNA LB may be considered as a complementary tool to TB analysis. Conclusions: Proper implementation of ctDNA LB testing in routine clinical practice is envisioned in the near future. As the clinical evidence of utility expands, the use of LB alongside tissue sample analysis may occur in the patient cases detailed here.

In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a preliminary hyperspectral superpixels segmentation together with the spectral pixel information. The proposed method does not require the number of segmentation classes as input parameter, and it does not exploit any a-priori knowledge about the type of land-cover or land-use to be segmented (e.g. water, vegetation, building etc.). Experiments on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried out. Performance are measured in terms of normalized mutual information, adjusted Rand index and F1-score. Results demonstrate the validity of the proposed method in comparison with the state of the art.

A total coloring of a graph G=(V,E) is an assignment of colors to vertices and edges such that neither two adjacent vertices nor two incident edges get the same color, and, for each edge, the end-points and the edge itself receive different colors. Any valid total coloring induces a partition of the elements of G into total matchings, which are defined as subsets of vertices and edges that can take the same color. In this paper, we propose Integer Linear Programming models for both the Total Coloring and the Total Matching problems, and we study the strength of the corresponding Linear Programming relaxations. The total coloring is formulated as the problem of finding the minimum number of total matchings that cover all the graph elements. This covering formulation can be solved by a Column Generation algorithm, where the pricing subproblem corresponds to the Weighted Total Matching Problem. Hence, we study the Total Matching Polytope. We introduce three families of nontrivial valid inequalities: vertex-clique inequalities based on standard clique inequalities of the Stable Set Polytope, congruent-2k3 cycle inequalities based on the parity of the vertex set induced by the cycle, and even-clique inequalities induced by complete subgraphs of even order. We prove that congruent-2k3 cycle inequalities are facet-defining only when k=4, while the vertex-clique and even-cliques are always facet-defining. Finally, we present preliminary computational results of a Column Generation algorithm for the Total Coloring Problem and a Cutting Plane algorithm for the Total Matching Problem.

As echoed in the European Green Deal and Farm to Fork strategy, the EU agricultural policy has clear objectives of improving the sustainability (environmental, economic and social) of farm productions. The environmental goals are supported by promotion of organic farming and the responsible management of inputs such as fertilisers and pesticides. In this framework, there is a need is to provide farmers with tools to evaluate the best options for reducing environmental impact and measure performances in improving the sustainability of their productions. This paper introduces MIMERA, a GIS easy-to-use tool for the sustainable use of pesticides at farm level. MIMERA was developed in the framework of the Train-Agro project funded by the Lombardia Region (Italy), in the framework of the European Agricultural Fund for Rural Development Programme (EAFRD). The main goal is to help professional pesticide users to identify the best pest management strategies by comparing the environmental risk of authorized commercial formulations of pesticides, identifying parcel most at risk within the farm boundaries and suggesting the most appropriate risk mitigation measures for the prevention of surface water contamination. Finally, MIMERA offers the opportunity to track the risk trend of the selected pest management strategies utilized during years. The proposed methodology and structure of MIMERA are described along with underlying data and algorithms are described. An example of its application by considering different pest management strategies adopted by farmers in an area characterized by the intensive presence of maize crop in Lombardia Region is presented.

In this paper we present a benchmark dataset generated as part of a project for automatic identification of misogyny within online content, which focuses in particular on memes. The benchmark here described is composed of 800 memes collected from the most popular social media platforms, such as Facebook, Twitter, Instagram and Reddit, and consulting websites dedicated to collection and creation of memes. To gather misogynistic memes, specific keywords that refer to misogynistic content have been considered as search criterion, considering different manifestations of hatred against women, such as body shaming, stereotyping, objectification and violence. In parallel, memes with no misogynist content have been manually downloaded from the same web sources. Among all the collected memes, three domain experts have selected a dataset of 800 memes equally balanced between misogynistic and non-misogynistic ones. This dataset has been validated through a crowdsourcing platform, involving 60 subjects for the labelling process, in order to collect three evaluations for each instance. Two further binary labels have been collected from both the experts and the crowdsourcing platform, for memes evaluated as misogynistic, concerning aggressiveness and irony. Finally for each meme, the text has been manually transcribed. The dataset provided is thus composed of the 800 memes, the labels given by the experts and those obtained by the crowdsourcing validation, and the transcribed texts. This data can be used to approach the problem of automatic detection of misogynistic content on the Web relying on both textual and visual cues, facing phenomenons that are growing every day such as cybersexism and technology-facilitated violence.

In this paper we performed the deposition and self-assembly of a Pd-cyclometallated compound on Ag(1 1 0) surface for the first time. The system is investigated from the morphological and chemical point of view by scanning tunneling microscopy and x-ray photoemission spectroscopy, respectively, and the results are validated by ab-initio calculations. Our combined experimental and theoretical study aims at elucidating the atomistic details of the chemical steps following Pd cyclometallate deposition on the metallic substrate. To do that, we analyze the electronic and chemical properties of the species present on the surface at the end of the preparation process at room temperature and at 150 °C. We observe an unexpected complex chemistry: on one side, the organometallic molecules are found to dissociate into fragments, forming a well-ordered metal–carbon network; on the other side, Pd atoms become buried in the bulk of the metal substrate following metal exchange with surface Ag atoms. The details of this mechanistic study reveal the active role played by the metal substrate in promoting the chemistry of the deposited Pd cyclometallates and could open new perspectives for the application of this class of materials in heterogeneous catalysis.

This paper investigates the causal impact of working conditions on mental health in the UK, combining new longitudinal data on working conditions from the European Working Conditions Survey with microdata from the UK Household Longitudinal Survey (Understanding Society). Our empirical strategy accounts for the endogenous sorting of individuals into occupations by including individual fixed effects. We address the potential endogeneity of occupational change over time by focusing only on individuals who remain in the same occupation (ISCO 3-digit), exploiting the variation in working conditions within each occupation over time. This variation, determined primarily by general macroeconomic conditions, is likely to be exogenous from the individual point of view. Our results indicate that, for female workers, improvements in working conditions such as skills and discretion, working time quality, and work intensity improve mental health outcomes such as loss of confidence, anxiety, social dysfunction, and risk of clinical depression. These effects are clinically relevant and substantial for younger and older female workers and larger for workers in occupations characterised by an inherently higher level of job strain. We detail how different dimensions of job quality impact different mental health outcomes for different age groups. Our results have important implications for public policies and firms which aim to improve workers’ wellbeing and productivity through workplace interventions focused on mental health.

In recent years, a multitude of e-commerce websites arose. Product Search is a fundamental part of these websites, which is often managed as a traditional retrieval task. However, Product Search has the ultimate goal of satisfying specific and personal user needs, leading users to find and purchase what they are looking for, based on their preferences. To maximize users’ satisfaction, Product Search should be treated as a personalized task. In this paper, we propose and evaluate a simple yet effective personalized results re-ranking approach based on the fusion of the relevance score computed by a well-known ranking model, namely BM25, with the scores deriving from multiple user/item representations. Our main contributions are: (1) we propose a score fusion-based approach for personalized re-ranking that leverages multiple user/item representations, (2) our approach accounts for both content-based features and collaborative information (i.e. features extracted from the user–item interactions graph), (3) the proposed approach is fast and scalable, can be easily added on top of any search engine and it can be extended to include additional features. The performed comparative evaluations show that our model can significantly increase the retrieval effectiveness of the underlying retrieval model and, in the great majority of cases, outperforms modern Neural Network-based personalized retrieval models for Product Search.

In this paper, we report a mechanistic study of RuO4-catalyzed oxidation on the 2-methylisoxazolidine through computational methods. The investigation was performed taken into consideration that the oxidation could take place on different sites of the substrate. This reaction occurs in two steps, involving a double H-transfer. In particular, the rate-determining one implies a [3 + 2] one-step, but asynchronous mechanism. In the first step, when methyl propanoate is used as solvent, the formation of an ion pair, which affords to the product, is involved. Furthermore, the study highlights that all carbon atoms of the isoxazolidine system, near to the heteroatoms, can undergo the oxidation process. The detected selectivity is correlated to the stability of the corresponding carbocations, leading to the N-methylisoxazolidin-3-one as preferred product.

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys

This paper deals with mineralogical-petrographic, microstructural and geochemical analyses for the archaeometric characterization of silicate-bearing granoblastic marbles from the so-called Waldensian Valleys of Piedmont (i.e. Pellice, Germanasca and lower Chisone valleys, Cottian Alps, Italy). These stones, belonging to the Dora-Maira Massif of the Penninic domain (pre-Carboniferous), are often associated with a talc-bearing mineralization. Since the XIV century, “Waldensian” marbles have been subjected to a relatively intense extractive activity (nowadays almost completely abandoned) and have been widely used, especially during XVI-XIX century, for decoration purposes and as building materials in the Duchy of Savoy, as well as later, in Italy and abroad. Fifteen samples representing four lithofacies and commercial types (named “Statuary white”, “Bardiglio gray”, “Zebrato gray” and “Cipollino green” marbles) from four different quarries (named Cabitto, La Majera, Rocca Corba, and Caugis), were studied through a multianalytical approach. The isotopic signature, combined with the mineral chemistry, allowed tracing marbles origin uniquely. The resulting database can be employed either in the cultural heritage, or in the conservation and restoration science, to identify each “Waldensian” marble from different decorative stones with similar microstructural and/or compositional features, but different origin.

In this paper, we describe a 5500-year long core obtained from a lake in the Prealps of northern Italy to investigate the impact of human communities on the lacustrine environment. Specifically, we report micro-botanical proxies (upland and aquatic pollen, algae, cyanobacteria, dung spores, microcharcoal particles), sedimentary parameters (LOI steps, calcimetry, FTIR spectra) and nutrients (P forms, C/N and K) on paired samples in a high-resolution stratigraphy, dated by 14C AMS dating and tie point correlation. Independent archaeological evidence indicates the existence of lakeside settlements and farming in Bronze Age times from c. 4000 to 3300 cal yrs. BP. The impact of this Bronze Age village is recorded by an accumulation of plant detritus, pollen, charcoal and abundant wood and bone-derived ash. Intensive P mineralization by ash production was accompanied by the rapid burial of Pinorg, K, and microcharcoal. Correlated peaks of Porg and pasture plant pollen mark the contribution of animal husbandry to Porg lake sink. The nutrient flux produced by external catchment cropping contrasts with the internal flux discharged by prehistoric dwelling and in turn distinguishes domestic hearth production from a contribution by animal husbandry. Forest conditions and lake sedimentation returned to semi-natural baseline at the end of this 700-year long phase of Early to Middle Bronze Age settlement, but pollen proxies indicate that lake-side forests did not fully recover. Lake Lucone represents a key site, since it documents interactions between bronze Age settlements and related ecological impacts on both terrestrial environment and lacustrine sedimentation, as recorded in a continuous sequence, as well as the end of Bronze Age pile dwelling culture.

Digital raindrop removal is a branch of image restoration that aims at identifying adherent droplets on a glass surface and replacing them with plausible content. When successfully performed, raindrop removal was proven in the past to positively affect both the perceived appearance of the scene, and the performance of computer vision tasks such as semantic segmentation and object detection. In this paper, we design and implement a new encoder-decoder neural network for supervised raindrop removal. Our network, given a rainy input image, produces as output the Laplacian pyramid of a rain-free version of the input, making it possible to handle the variety of appearances of rain droplets by processing different frequency bands independently. To this end, we define and experimentally prove the effectiveness of a custom loss function that combines the errors of the different Laplacian frequency bands. We test our model for raindrop removal on a standard dataset, using multiple objective metrics to provide a detailed analysis of its performance. We confirm the superiority of our proposal in a comparison with other methods from the state of the art.

This paper reviews past evaluations of the Lyon–Turin High-Speed rail project undertaken since the project was designed. It investigates these evaluations and summarises their methodologies and main results. Moreover, it analyses in detail the latest official Cost–Benefit Analysis (CBA) carried out in 2019 in Italy and investigates the criticisms formulated on this evaluation. Our findings reveal that the tunnel project had been approved despite the lack of an evaluation that would reach the minimal requirements of being simultaneously documented, open to public scrutiny, computationally correct, based on parameters consistent with evidence, on a sufficiently detailed representation of supply and demand, and properly considering the risk of cost overruns. Regarding the 2019 CBA, the most salient criticisms focused on the Rule of Half and the inclusion of taxes. These criticisms appear, under review, ill-conceived, which suggests a possible misunderstanding of some fundamental outcomes of transport economics. From a methodological point of view, our results confirm that there is no inconsistency in the mainstream approach of CBA, in which taxation is included in the calculation and transferred user benefits are computed with the Rule of Half.

During the first wave of the COVID-19 pandemics in 2020, lockdown policies reduced human mobility in many countries globally. This significantly reduces car traffic-related emissions. In this paper, we consider the impact of the Italian restrictions (lockdown) on the air quality in the Lombardy Region. In particular, we consider public data on concentrations of particulate matters (PM10 and PM2.5) and nitrogen dioxide, pre/during/after lockdown. To reduce the effect of confounders, we use detailed regression function based on meteorological, land and calendar information. Spatial and temporal correlations are handled using a multivariate spatiotemporal model in the class of hidden dynamic geostatistical models (HDGM). Due to the large size of the design matrix, variable selection is made using a hybrid approach coupling the well known LASSO algorithm with the cross-validation performance of HDGM. The impact of COVID-19 lockdown is heterogeneous in the region. Indeed, there is high statistical evidence of nitrogen dioxide concentration reductions in metropolitan areas and near trafficked roads where also PM10 concentration is reduced. However, rural, industrial, and mountain areas do not show significant reductions. Also, PM2.5 concentrations lack significant reductions irrespective of zone. The post-lockdown restart shows unclear results.

This paper investigates the causal impact of working conditions on mental health in the UK, combining new longitudinal data on working conditions from the European Working Conditions Survey with microdata from the UK Household Longitudinal Survey (Understanding Society). Our empirical strategy accounts for the endogenous sorting of individuals into occupations by including individual fixed effects. We address the potential endogeneity of occupational change over time by focusing only on individuals who remain in the same occupation (ISCO 3-digit), exploiting the variation in working conditions within each occupation over time. This variation, determined primarily by general macroeconomic conditions, is likely to be exogenous from the individual point of view. Our results indicate that, for female workers, improvements in working conditions such as skills and discretion, working time quality, and work intensity improve mental health outcomes such as loss of confidence, anxiety, social dysfunction, and risk of clinical depression. These effects are clinically relevant and substantial for younger and older female workers and larger for workers in occupations characterised by an inherently higher level of job strain. We detail how different dimensions of job quality impact different mental health outcomes for different age groups. Our results have important implications for public policies and firms which aim to improve workers’ wellbeing and productivity through workplace interventions focused on mental health.

Well-preserved remains of a Posidonia oceanica meadow have been found in a Lower Pleistocene (Calabrian) succession cropping out near Fauglia (Tuscany, Italy). This paper analyses and describes the benthic foraminiferal community associated with this well-preserved Pleistocene P. oceanica meadow, with the purpose of testing the usefulness of foraminifera as an Indirect Palaeo-Seagrass Indicator (IPSI), providing both qualitative and quantitative parameters to recognize other meadow-related environments where fossil remains of seagrasses are not preserved. Despite being influenced by some diagenetic processes that might have affected the foraminiferal assemblage, the Fauglia succession represents a suitable setting for testing benthic foraminifera as IPSI in a temperate environment. Considering this limitation, together with other ecological constraints such as seawater temperature, this study provides evidence of the high potential of benthic foraminifera as IPSI. Several parameters such as the modified Foraminifera Index (FI’), the “long vs. short life-span index” (ILS), and the presence of permanently attached, encrusting foraminifera were tested. New indexes were also developed, namely the K/R ratio, consisting of the ratio between keeled Elphidium and the sum of rounded elphidiids (e.g., Elphidium translucens, Cribroelphidium, Porosononion) and Haynesina, and the K/REXT ratio, consisting of the ratio between keeled Elphidium and the sum of rounded elphidiids, Haynesina and other related genera that display a rounded periphery (i.e., Astrononion, Melonis, Nonion, Nonionella, Valvulineria). All these indexes were examined, suggesting their potential use for recognizing seagrass-related palaeoenvironments. The K/REXT ratio (and K/R) in association with the presence of permanently attached foraminifera revealed to be the most reliable palaeo-seagrass indicators, suggesting that this combination could be very useful also in other case studies where diagenesis altered

Lexical taxonomies are widely used to foster information retrieval and exchange in several domains and applications. When there are multiple taxonomies, heterogeneity among them is a severe problem for efficient collaboration processes. In this paper, we propose WETA, a domain-independent, knowledge-poor method for automatic taxonomy alignment via word embeddings. WETA associates all the leaf terms of the origin taxonomy to one or many concepts in the destination taxonomy, employing a scoring function, which merges the score of a hierarchical method based on cosine similarity and the score of a classification task. WETA is developed in the context of an EU Grant aiming at bridging the national taxonomies of EU countries towards the European Skills, Competences, Qualifications and Occupations taxonomy (ESCO) using AI Algorithms. The results, validated within the EU project activities for bridging the Italian occupation taxonomy CP and ESCO, confirm the usefulness of WETA in supporting the automatic alignment of national labor taxonomies. WETA reaches a 0.8 accuracy on recommending top-5 occupations and a wMRR of 0.72. WETA reduces the human effort needed for building a mapping from scratch: it would allow domain experts to concentrate on the validation task and decrease the incoherence due to multiple judgments. It would also make the approach reproducible and transparent to policymakers.

Micellar catalysis is having a dramatic impact in improving the sustainability of a wide variety of chemical transformations, including most of the popular palladium mediated carbon–carbon and carbon–nitrogen bond forming reactions. The Suzuki-Miyaura coupling is one of the earliest examples of micellar catalyzed reactions and it is still a very much active research field even at industrial scale. So far, micellar catalysis proved particularly efficient in enabling the high yield, low catalyst loading and mild reaction condition coupling of hydrophobic reagents in a water solution of selected surfactants. In this paper we show that the use of aqueous solutions of a mixture of food grade lecithin and the industrial surfactant Tween 80 not only enables efficient coupling in water of hydrophobic chemicals, but also provides a very resourceful tool for the functionalization of water and organic solvent insoluble pigments of interest for the dye and printed electronic industries.

Among the computational features that determine the computing power of polarizationless P systems with active membranes, the depth of the membrane hierarchy is one of the least explored. It is known that this model of P systems can solve [Formula presented]-complete problems when no constraints are given on the depth of the membrane hierarchy, whereas the complexity class P∥#P is characterized by monodirectional shallow P systems with minimal cooperation, whose depth is 1. No similar result is currently known for polarizationless systems without cooperation or other additional features. In this paper we show that these P systems, using a membrane hierarchy of depth 2, are able to solve at least all decision problems that are in the complexity class [Formula presented], the class of problems solved in polynomial time by deterministic Turing machines that are given the possibility to make a polynomial number of parallel queries to oracles for [Formula presented] problems.