Linking green infrastructure deployment needs and agroecosystem conditions for the improvement of the Natura2000 network. Preliminary investigations in W Mediterranean Europe

Reconnecting natural habitats and improving agroecosystem conditions are strategic targets set by several European policies. In order to combine both of these needs, the European Biodiversity Strategy for 2030 has triggered new investments in Green Infrastructure (GI), which actually represents a valuable tool to increase ecological connectivity across natural and semi-natural habitats. In particular, GI may benefit the Natura2000 (N2K) network (i.e., the network of protected sites under the EU Habitats and Birds Directives) by reinforcing the node/site number, extent, and distribution and by improving connections between often small and isolated habitat patches. However, there is a lack of knowledge on what the actual needs of GI deployment are for improving the current Natura2000 network, on the distribution of these needs across Europe and on the potential role of agricultural areas in the improvement of the network functionality. Concurrently, especially in SW Europe, there is an ongoing trend toward the homogenisation and intensification of agricultural systems and the combined loss of associated landscape elements, such as natural and semi-natural Small Woody Features (SWF). Although a well-planned network of such elements could support biodiversity and landscape connectivity, thus effectively complementing the Natura2000 network, little evidence is available on their abundance and residual distribution, especially in agricultural areas and at continental/bioregional scales. Therefore, the present work is aimed at (i) identifying different types of territorial units (NUTS3) in W Mediterranean Europe according to current N2K network features, the overall composition of the actual landscape mosaic and the potential natural heterogeneity of the environment and (ii) identifying and spatialising N2K-related GI deployment needs according to a more specific network analysis in terms of nodes (extent of the total protected area) and links (density of residual woody elements in arable land) within the different types of NUTS3. By means of this wide-scale investigation, four different types of GI deployment needs were generalised across the W Mediterranean Europe NUTS3. Overall, the need for connection restoration prevails, followed by the need for the consolidation of node and link conservation, for the creation of new protected sites and for the enlargement of existing N2K sites. Although useful for a preliminary setting, the shortcomings related to summary data at the European level were also highlighted when compared to local-scale information, with the latter being more suitable for identifying and prioritising truly effective GI conservation and restoration actions

Integrated sources of entangled photons at telecom wavelength in femtosecond-laser-written circuits

Photon entanglement is an important state of light that is at the basis of many protocols in photonic quantum technologies, from quantum computing, to simulation and sensing. The capability to generate entangled photons in integrated waveguide sources is particularly advantageous due to the enhanced stability and more efficient light-crystal interaction. Here we realize an integrated optical source of entangled degenerate photons at telecom wavelength, based on the hybrid interfacing of photonic circuits in different materials, all inscribed by femtosecond laser pulses. We show that our source, based on spontaneous parametric down-conversion, gives access to different classes of output states, allowing to switch from path-entangled to polarization-entangled states with net visibilities above 0.92 for all selected combinations of integrated devices

Adaptive phase estimation through a genetic algorithm

Quantum metrology is one of the most relevant applications of quantum information theory to quantum technologies. Here, quantum probes are exploited to overcome classical bounds in the estimation of unknown parameters. In this context, phase estimation, where the unknown parameter is a phase shift between two modes of a quantum system, is a fundamental problem. In practical and realistic applications, it is necessary to devise methods to optimally estimate an unknown phase shift by using a limited number of probes. Here we introduce and experimentally demonstrate a machine learning-based approach for the adaptive estimation of a phase shift in a Mach-Zehnder interferometer, tailored for optimal performances with limited resources. The employed technique is a genetic algorithm used to devise the optimal feedback phases employed during the estimation in an offline fashion. The results show the capability to retrieve the true value of the phase by using few photons, and to reach the sensitivity bounds in such small probe regime. We finally investigate the robustness of the protocol with respect to common experimental errors, showing that the protocol can be adapted to a noisy scenario. Such approach promises to be a useful tool for more complex and general tasks where optimization of feedback parameters is required

Deep reinforcement learning for quantum multiparameter estimation

Estimation of physical quantities is at the core of most scientific research, and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that resources are limited, and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise knowledge of the system model, retrieved with a fine calibration, with results that are often computationally and experimentally demanding. We introduce a model-free and deep-learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges, without relying on any a priori knowledge of the system. To overcome this need, a neural network is trained directly on experimental data to learn the multiparameter Bayesian update. Then the system is set at its optimal working point through feedback provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor. Notably, we prove experimentally the achievement of higher estimation performances than standard methods, demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit. Our work represents an important step toward fully artificial intelligence-based quantum metrology

179. Correcting the Bleeding Phenotype in Hemophilia Ausing Lentivirally FVIII-Corrected Endothelial Cells Differentiated from Hemophilic Induced Pluripotent Stem Cell (iPSC)

Hemophilia A (HA) is a bleeding disorder caused by factor VIII (FVIII) gene mutations.Somatic cells can be reprogrammed to generate autologous, disease-free iPSCs, then differentiated into cell targetsrelevant for gene and cell therapy. Our aim is to develop a novel HA treatment strategy generating FVIII-corrected patient-specific iPSCs from peripheral blood cells anddifferentiating them into functional endothelial cells (ECs), secreting FVIII after transplantation

Role of interface and morphology in the magnetic behaviour of perpendicular thin films based on L10 FePt

FePt L10 ordered alloy is a promising material for high-density magnetic recording, since it allows the ferromagnetic stability in particles of few nanometers. Here we present our recent studies on the correlation between magnetic and morphological/interfacial properties of FePt -based thin films, nanostructures, and nano-composite bilayers. L10 FePt (001) epitaxial thin films with high structural quality were grown on (100) MgO by sputtering r.f., using the alternate-layer deposition method. By playing with growth temperature on the one hand and post-annealing temperature and time on the other, we have been able to finely control epitaxy, structural order, and morphology from 3D laterally confined structures to continuous film, with desired grain size. In particular we have been able to decrease grain size and to optimise magnetic properties (increase of anisotropy/coercivity ratio) at the same time, by post-annealing in situ [1]. Laterally confined magnetic structures were also obtained by focused ion beam (FIB). We have shown that for suitable Ga+ doses (1?1014 ion/cm2), it is possible to transform the L10 ordered phase to the A1 disordered one, without affecting morphology, giving rise to substantial modifications of magnetic properties from hard to soft. Perpendicular 2D magnetic patterns (dots, stripes) in a soft easy-plane matrix were realized in films of continuous morphology [2]. FePt L10 has also been exploited as the hard layer of nanostructured hard-soft nanocomposite bilayers. The exploitation of the exchange-coupling between hard and soft layers in exchange-coupled media represents a possible approach to overcome the so-called "recording trilemma" [3]. The samples were prepared by growing a magnetically soft Fe layer (2 and 3.5 nm) over a hard FePt(001) layer (10 nm). Three bilayers series have been grown based on FePt epitaxial layers with high degree of chemical order (S≥0.76) and different morphologies, corresponding to different interface characteristics. The resulting hard layer anisotropy is high (K>1?107 erg/cm3), and the coercivity is increased by the grains separation (from 1.7 to 3 T). In the Fe/FePt bilayers the coercivity HC is strongly reduced compared to the hard layer value (HC/HChard down to 0.37), indicating that high anisotropy perpendicular systems with moderate coercivity can be obtained [4]. Moreover, the control of the interface morphology allows to modify the magnetic regime at fixed Fe thickness (Rigid Magnet to Exchange-Spring), due to the nanoscale structure effect on the hard/soft coupling, and to tailor the hysteresis loop characteristics

Experimental multiparameter quantum metrology in adaptive regime

Relevant metrological scenarios involve the simultaneous estimation of multiple parameters. The fundamental ingredient to achieve quantum-enhanced performances is based on the use of appropriately tailored quantum probes. However, reaching the ultimate resolution allowed by physical laws requires non trivial estimation strategies both from a theoretical and a practical point of view. A crucial tool for this purpose is the application of adaptive learning techniques. Indeed, adaptive strategies provide a flexible approach to obtain optimal parameter-independent performances, and optimize convergence to the fundamental bounds with limited amount of resources. Here, we combine on the same platform quantum-enhanced multiparameter estimation attaining the corresponding quantum limit and adaptive techniques. We demonstrate the simultaneous estimation of three optical phases in a programmable integrated photonic circuit, in the limited resource regime. The obtained results show the possibility of successfully combining different fundamental methodologies towards transition to quantum sensors applications

Phosgene distribution derived from MIPAS ESA v8 data: intercomparisons and trends

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measured the middle-infrared limb emission spectrum of the atmosphere from 2002 to 2012 on board ENVISAT, a polar-orbiting satellite. Recently, the European Space Agency (ESA) completed the final reprocessing of MIPAS measurements, using version 8 of the level 1 and level 2 processors, which include more accurate models, processing strategies, and auxiliary data. The list of retrieved gases has been extended, and it now includes a number of new species with weak emission features in the MIPAS spectral range. The new retrieved trace species include carbonyl chloride (COCl2), also called phosgene. Due to its toxicity, its use has been reduced over the years; however, it is still used by chemical industries for several applications. Besides its direct injection in the troposphere, stratospheric phosgene is mainly produced from the photolysis of CCl4, a molecule present in the atmosphere because of human activity. Since phosgene has a long stratospheric lifetime, it must be carefully monitored as it is involved in the ozone destruction cycles, especially over the winter polar regions. In this paper we exploit the ESA MIPAS version 8 data in order to discuss the phosgene distribution, variability, and trends in the middle and lower stratosphere and in the upper troposphere. The zonal averages show that phosgene volume mixing ratio is larger in the stratosphere, with a peak of 40 pptv (parts per trillion by volume) between 50 and 30 hPa at equatorial latitudes, while at middle and polar latitudes it varies from 10 to 25 pptv. A moderate seasonal variability is observed in polar regions, mostly between 80 and 50 hPa. The comparison of MIPAS–ENVISAT COCl2 v8 profiles with the ones retrieved from MIPAS balloon and ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) measurements highlights a negative bias of about 2 pptv, mainly in polar and mid-latitude regions. Part of this bias is attributed to the fact that the ESA level 2 v8 processor uses an updated spectroscopic database. For the trend computation, a fixed pressure grid is used to interpolate the phosgene profiles, and, for each pressure level, VMR (volume mixing ratio) monthly averages are computed in pre-defined 10∘ wide latitude bins. Then, for each latitudinal bin and pressure level, a regression model has been fitted to the resulting time series in order to derive the atmospheric trends. We find that the phosgene trends are different in the two hemispheres. The analysis shows that the stratosphere of the Northern Hemisphere is characterized by a negative trend of about −7 pptv per decade, while in the Southern Hemisphere phosgene mixing ratios increase with a rate of the order of +4 pptv per decade. This behavior resembles the stratospheric trend of CCl4, which is the main stratospheric source of COCl2. In the upper troposphere a positive trend is found in both hemispheres.</p

Experimental investigation of Bayesian bounds in multiparameter estimation

Quantum parameter estimation offers solid conceptual grounds for the design of sensors enjoying quantum advantage. This is realised not only by means of hardware supporting and exploiting quantum properties, but data analysis has its impact and relevance, too. In this respect, Bayesian methods have emerged as an effective and elegant solution, with the perk of incorporating naturally the availability of a priori information. In this article we present an evaluation of Bayesian methods for multiple phase estimation, assessed based on bounds that work beyond the usual limit of large samples assumed in parameter estimation. Importantly, such methods are applied to experimental data generated from the output statistics of a three-arm interferometer seeded by single photons. Our studies provide a blueprint for a more comprehensive data analysis in quantum metrology.Comment: 8 pages, 6 figure