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A composite, capillary-driven microfluidic system suitable for transmitted light microscopy of cells (e.g., red and white human blood cells) is fabricated and demonstrated. The microfluidic system consists of a microchannels network fabricated in a photo-patternable adhesive polymer on a quartz substrate, which, by means of adhesive bonding, is then connected to a silicon microfluidic die (for processing of the biological sample) and quartz die (to form the imaging chamber). The entire bonding process makes use of a very low temperature budget (200 °C). In this demonstrator, the silicon die consists of microfluidic channels with transition structures to allow conveyance of fluid utilizing capillary forces from the polymer channels to the silicon channels and back to the polymer channels. Compared to existing devices, this fully integrated platform combines on the same substrate silicon microfluidic capabilities with optical system analysis, representing a portable and versatile lab-on-chip device.

The current trend in scaling transistor gate length below 60 nm is posing great challenges both related to process technology and circuit/system design. From the process technology point of view it is becoming increasingly difficult to continue scaling in traditional way due to fundamental limitations like resolution, quantum effects or random fluctuations. In turn, this has an important impact on electrical device specifications especially leakage current and the circuit power dissipation.

Abstract Background Ritonavir-boosted nirmatrelvir (N/r) is an antiviral which targets the main viral protease, administered to prevent the progression of SARS-CoV-2 infection in patients at high risk for severe COVID-19. We present a real-life case-control study evaluating the efficacy of N/r therapy in SARS-CoV-2 omicron variants positive outpatients in Campania region, Italy, with the aim of assessing the occurrence of three outcomes (hospital admission, admission in ICU and death) in cases and controls. Methods We enrolled SARS-CoV-2 positive subjects that came to our attention in Early antiviral treatment ambulatory of Infectious Diseases ward of University Federico II of Naples, Italy from January 1st, 2022, to December 31st, 2022, during the first five days from symptoms occurrence. Patients were enrolled as cases or controls if they were treated with N/r or not treated at all, respectively. Results 1064 patients were included (cases: 423, controls: 1184). Cases showed a lower mortality compared with controls while no differences were observed for other outcomes. Vaccinated patients showed a lower mortality compared with non-vaccinated ones (0.5% vs. 7.8%, p

Abstract The Italian legislator has adopted several instruments to discourage undeclared work and exploitative labour in agriculture, mostly of a penal-repressive nature. Among the direct and indirect policy measures, the ‘Quality Agricultural Work Network’ represents an interesting approach to producing a ‘whitelist’ of farmers compliant with labour regulations. A law proposal intends to establish the ‘Quality Agricultural Work’ (QAW) ethical label to incentivise farmers to join the network, to which a limited percentage of farms have signed up. This study aims to investigate consumer preferences for the QAW label in the Italian fruit sector. We conducted a choice experiment on a sample of 324 consumers. Willingness to pay for ethical labels was estimated before and after information treatment was administered to evaluate the prospective effects of promotional and information campaigns. The information treatment conveyed a clear and concise message about the QAW project and its ethical label. The results show that consumers would pay a high price premium for fruit produced under fair working conditions, indicating that there may be a market space for the QAW label. Moreover, consumers perceive environmental and social sustainability claims as complementary contexts where both dimensions of sustainability are relevant. Graphical abstract

Durum wheat is essential for global food security. Nevertheless, its cultivation is susceptible to hazards, including unpredictability in yield and grain quality. This systematic review and bibliometric analysis identify factors influencing durum wheat yield and quality, assessing the degree of control farmers have over these factors. The goal is to understand their impact on production risks. Peer-reviewed studies in English from 1990 to April 2024 that focused on the yield or quality of durum wheat were included, while those lacking specific data or not peer-reviewed were excluded. Data were acquired via the Web of Science (WoS), with the concluding search conducted in April 2024. Results were synthesized from 2131 studies selected from an initial pool of 5159, using a bibliometric approach to categorize findings into standard, biotic, abiotic, and other factors. Analysis revealed that practices like irrigation and nitrogen fertilization improve yields, while genetic advancements boost stress resilience. These insights support targeted agronomic strategies. Despite potential biases and inconsistencies, the review underscores key strategies to enhance durum wheat risk management and bolster food security. This study was funded by the Italian Ministry of University and Research (CURSA, D.I.Ver.So.) and PRIN—2020 Call.

A large amount of policy support is spent to foster the development of rural areas in Europe. However, empirical evidence on the well-being differential between rural and urban areas in Europe is scant and incomplete. The present study develops a systematic literature review on this topic, bridging a gap in research as a systematic analysis on the subject has not so far been developed as far as we know. It uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) method. The review focuses on type of definition of rural-urban most used in the literature, on main dimensions of well-being that are analyzed, on nature of the data and finally on main evidence that emerged regarding the differences in the various dimensions of well-being between rural and urban populations. The analysis confirms that available evidence is controversial and provides advice on how to develop new and better empirical analyses on this topic.

Agriculture is one of the most concerned sectors, due to its exposure, to a plethora of risky phenomena such as weather, pests and diseases, changes in prices and government policies, instability of global markets. We review the literature on risk management in agriculture focusing on four key topics: i) why evidence on RM is often controversial; ii) how farmers behave in selecting among available RM instruments; iii) why some of these instruments are underutilised; iv) how to assess the impacts of innovative RM tools in order to (further) improve their design. These questions are answered transversally, and emphasis is posed on areas where further analyses are needed.

Abstract The Income Stabilization Tool, a risk management scheme introduced within the Common Agricultural Policy (CAP) 2014–2020, could help European Union farmers manage the income risks they face. This study assesses the potential impact of implementing this tool through the maximum level of contribution to the fund which determines an indifference to participate in the fund and its financial sustainability. The study relies on an expected utility approach and assesses the variability of loss ratios over time using a sample of Italian hazelnut farms as a case study. The participation depends on the level of farmers' contributions and their degree of risk aversion. However, the CAP public support makes the scheme financially sustainable.

Abstract This paper provides an ex ante assessment of the effects of the Income Stabilization Tool (IST), a new risk management tool proposed in the Common Agricultural Policy of the European Union. We investigate the effects of IST on income variability and levels as well as on income inequality in the farming population. We take Italian agriculture as an example as the introduction of IST is currently under discussion there. A rich panel of 2777 farms was studied over a period of 7 years. We use stochastic simulation to derive different income inequality estimates and apply Gini decomposition approaches to assess the distributional implications of IST. We compare the current income situation with that resulting from a hypothetical implementation of IST under different policy scenarios, also accounting for reduced levels of CAP direct payments. We find that IST not only stabilizes farm income but also enhances its level and reduces income inequality in Italian agriculture. IST is more effective in reducing income inequality when farmers pay contributions to mutual funds that are proportional to their income compared to the case of flat rate contributions. Finally, results do not support the hypothesis that the impact of IST will differ if the level of direct payments were to be reduced. Thus, results seem robust enough to accommodate future policy conditions.

Recent literature reviews of empirical models optimizing long-term investments in agriculture see gaps with regard to (i) separating investment and financing decisions, (ii) considering explicitly risk and temporal flexibility, and (iii) accounting for farm-level resource endowments and other constraints. Inspired by real options approaches, this paper therefore stepwise develops a model extending a simple net present value calculation to a farm-scale simulation model based on mathematical programming, which considers time flexibility, different financing options and downside risk aversion. We empirically assess the different model variants by analysing investments into hazelnut orchards in Italy outside of traditional producing regions. The variants suggest quite different optimal results with respect to scale and timing of the investment, its financing and the expected NPV. The stepwise approach reveals which aspects drive these differences and underlines that considering temporal flexibility, different financing options and riskiness can considerably improve traditional NPV analysis.

A central question in cancer research is how specific driver mutations are acquired and maintained during cancer development. Here Temko et al. use public sequencing data to infer the effect of mutation and selection on a set of driver mutations and suggest that selection frequently dominates.

Facioscapulohumeral muscular dystrophy is a myopathy linked to ectopic expression of the DUX4 transcription factor. The authors show that the suppression of targets genes of the myogenesis regulator PAX7 is a signature of FSHD, and might explain oxidative stress sensitivity and epigenetic changes.

Abstract During the 52nd Conference of the Italian Society of Agricultural Economists (SIDEA), Italian agricultural economists have been given the opportunity to debate regarding internationalization, competition, and local development in agri-food systems. This special issue of the journal AFE contains four selected papers presented in this conference. These papers focus on three main issues that are strongly affecting the evolution of food supply-chains and their functioning mechanisms. These are the growing role of vertical integration between the farm sector and the downstream sectors, the possibility for farmers to increase their horizontal integration and the role of agricultural policies in affecting these phenomena. The reading of these papers allow for a better understanding of what is affecting the competitiveness of the EU agro-food system, the distribution of the bargaining power along the value-chain and, at the end, the efficiency and economic performances of the EU farm sector.

The exposure of farmers to different (and increasing) risks has been recognized by the EU policy, which supports several risk management tools through the Common Agricultural Policy (CAP). Despite the vulnerability of the agricultural sector, and the attention paid at the EU level, the uptake of such tools is generally low across EU countries. The Italian case is emblematic: the uptake of subsidized crop insurance contracts is low, limited to few products, and concentrated in few areas. Coherently, the interest of policy makers toward explaining these characteristics and in gaining insights on the interventions that may help promoting participation is intense. This contribution investigates behavioral aspects linked to choices under risk and ambiguity, and account for time preferences in order to mimic the scenario faced by the potential adopters of the subsidized crop insurance contracts in Italy. Data are collected through questionnaires submitted to students from agricultural colleges in three administrative regions located in northern, central and southern Italy. Results show that attitude toward risk, ambiguity, and impatience are correlated with the intrinsic characteristics of respondents. In addition, some of those attitudes may help explaining decisions under uncertainty. Despite the empirical analysis is preliminary and focused on students, it allowed to validate a promising methodological approach capable of explaining farmer’s willingness to adopt (or renew) insurance contracts. By accounting for (currently under-investigated) behavioral aspects, it is likely to prove useful to re-design or implementing, more effectively, the current policies.

There is a need for participatory methods that simultaneously assess agricultural sustainability and resilience at farming system level, as resilience is needed to deal with shocks and stresses on the pathways to more sustainable systems. We present the Framework of Participatory Impact Assessment for Sustainable and Resilient FARMing systems (FoPIA-SURE-Farm). FoPIA-SURE-Farm investigates farming system functioning, dynamics of main indicators, and specifies resilience for different resilience capacities, i.e., robustness, adaptability, and transformability. Three case studies with specialized farming systems serve as an example for the used methodology: starch potato production in Veenkoloniën, The Netherlands; dairy production in Flanders, Belgium; and hazelnut production in Lazio, Italy. In all three farming systems, functions that related to food production, economic viability, and maintaining natural resources were perceived as most important. Perceived overall performance of system functions suggest moderate sustainability of the studied farming systems. In the studied systems, robustness was perceived to be stronger than adaptability and transformability. This indicates that finding pathways to higher sustainability, which requires adaptability and transformability, will be a challenging process. General characteristics of farming systems that supposedly convey general resilience, the so-called resilience attributes, were indeed perceived to contribute positively to resilience. Profitability, having production coupled with local and natural resources, heterogeneity of farm types, social self-organization, and infrastructure for innovation were assessed as being important resilience attributes. The relative importance of some resilience attributes in the studied systems differed from case to case, e.g., heterogeneity of farm types. This indicates that the local context in general, and stakeholder perspectives in particular, are important when evaluating general resilience and policy options based on resilience attributes. Overall, FoPIA-SURE-Farm results seem a good starting point for raising awareness, further assessments, and eventually for developing a shared vision and action plan for improving sustainability and resilience of farming systems.

Techniques based on terminated cover crops are proposed in organic agriculture instead of traditional inter-row tillage for weed control in subsequent grain crops. We estimated the profitability and evaluated the riskiness of novel techniques compared to the traditional one. Two-year yield data from three on-farm trials for corn and one for soybean in northern Italy were combined with cost and revenue data to calculate and compare gross margin distributions of different techniques in each crop. The relative ranking of techniques and the associated riskiness was assessed by these distributions using the stochastic dominance principles. Corn yield response on cover crop-based treatments was very variable, making the adoption of cover crops overall less sustainable from an economic point of view than the traditional tillage-based technique. Further research in this sense was nonetheless warranted by observed exceptions. Hairy vetch cover crop tended to higher profitability and lower riskiness than crimson clover for subsequent corn cropping. Specific analyses suggested nil or slightly negative nitrogen fertilizer effects of legume residues on corn. Results indicated that profitability could be maintained with cover crops in soybean compared to the traditional practice. In particular, triticale tended to provide better economic performances than inter-row tillage.

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, which is applicable on near-term quantum devices as it fulfils the above criteria. We find that for the required gradient calculation on a noisy device a quantum circuit with a large number of parameters is disadvantageous. By introducing a smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We present a model showing that the main effect of the noise is to increase the overlap between the states as circuit gates are applied, hence making discrimination more difficult. Our findings demonstrate that noisy quantum computers can be used for state discrimination and other applications, such as classifiers of the output of quantum generative adversarial networks.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers. We consider classical algorithms for the approximation of Hamiltonian dynamics using subsampling methods from randomized numerical linear algebra. We derive a simulation technique whose runtime scales polynomially in the number of qubits and the Frobenius norm of the Hamiltonian. As an immediate application, we show that sample based quantum simulation, a type of evolution where the Hamiltonian is a density matrix, can be efficiently classically simulated under specific structural conditions. Our main technical contribution is a randomized algorithm for approximating Hermitian matrix exponentials. The proof leverages a low-rank, symmetric approximation via the Nyström method. Our results suggest that under strong sampling assumptions there exist classical poly-logarithmic time simulations of quantum computations.

This paper analyses the instability of farm income experienced by a constant sample of Italian farms over the period 2003-2012. It assesses the extent of the aggregation bias due to the use of aggregated vs. single farm data and estimates the level of farm income variability in several groups of farms for the whole period and for two sub-periods. Differences between groups and periods are assessed by means of non-parametric tests. Results suggest that analyses based on aggregated farm data are likely going to strongly underestimate the extent of income variability faced by farmers. Income variability levels differ among farm groups and have significantly increased over the considered time. This has policy implications regarding the risk management tools recently introduced within the Rural Development Policies and how these should be targeted on the farms that more need them.

European farmers face an increasing income uncertainty. The debate is growing on the role of the insurance schemes and on the support provided by the CAP in this field. Therefore, there is a need for the empirical analysis and the tools aimed at providing empirical evidences on this subject. This paper develops an innovative Positive Mathematical Programming model that takes into explicit consideration the risk aversion behaviour. This is used to investigate the implications of participating in the crop revenue insurance scheme. In particular, a quadratic mix integer program has been developed on a small group of crop farms in Italy and it has been used to simulate the impact of changes in the level of the insurance premium. The objective of this paper is to assess the soundness of the proposed approach and to identify its limitations. The obtained results suggest that this could be a useful tool to investigate the impact of participating in insurance schemes on production patterns, farm profitability and the role of public support.

Adversarial learning is one of the most successful approaches to modeling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: one tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.

Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce a neural network based approach, which has the mathematical simplicity of the Gorini–Kossakowski–Sudarshan–Lindblad master equation, but is able to model non-Markovian effects in different regimes. This is achieved by using recurrent neural networks (RNNs) for defining Lindblad operators that can keep track of memory effects. Building upon this framework, we also introduce a neural network architecture that is able to reproduce the entire quantum evolution, given an initial state. As an application we study how to train these models for quantum process tomography, showing that RNNs are accurate over different times and regimes.

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers.

This paper investigates the contribution of different off-farm income sources and Common Agricultural Policy direct payments on income inequality among farm households. The analysis uses the Gini coefficient concept and its decomposition on the whole sample of Farm Accountancy Data Network individual farms of Italy in 2011. A marginal increase in either off-farm incomes or direct payments reduces income concentration. This result could feed the current debate regarding the application of the new CAP in Italy. Deciding on a narrow definition of “active farmer” or not using the redistributive payment could increase DP and FHI concentration. Of the five considered off-farm income sources, only pensions reduce income concentration. Therefore, policies reducing the level of pensions will increase income inequality. Finally, if rural development policies have also to reduce income inequality, these should be aimed at increasing job opportunities for additional family members.