Investigation of the Effects of Chirped RZ Signals in Reducing the Transmission Impairments in R-SOA-Based Bidirectional PONs

Distributed and concentrated reflections represent the two main limitations in reflective-semiconductor optical amplifier (R-SOA)-based passive optical networks (PONs). In this paper, we experimentally discuss how the use of chirped signals in centralized light seeding bidirectional PON can increase the resilience of the system against those two types of reflections. An experimental comparison of the performance of a highly chirped return to zero (RZ) modulation format and the nonreturn to zero is given. Error-free operation is achieved down to 10 dB of signal to crosstalk ratio in presence of distributed reflection, when the upstream signal is highly chirped RZ signal. The same chirped modulation leads to a tolerance of more than dB network return loss due to concentrated reflections. Finally, we assess also the system feasibility of a R-SOA-based full-duplex PON where both the upstream and the downstream are modulated signals

Accurate Performance Estimationof high-speed Digital Optical Signals

A novel technique allows an easy and accurate estimation of the system BER by collecting the statistical distribution of the analog samples, i.e. before decision. The scheme is confirmed by both simulations and experimental measurements

System feasibility of using stimulated Brillouin scattering in self coherent detection schemes

We demonstrate the first self-coherent detection of 10 Gbit/s BPSK signals based on narrow-band amplification of the optical carrier by means of Stimulated Brillouin effect in a common fiber. We found that this technique is very effective only if it is combined with proper line coding and high-pass electrical filtering at the receiver. In this case we obtain OSNR-performance close to the ideal coherent receiver. (C) 2010 Optical Society of Americ

PLANiTS: a curated sequence reference dataset for plant ITS DNA metabarcoding

DNA metabarcoding combines DNA barcoding with high-throughput sequencing to identify different taxa within environmental communities. The ITS has already been proposed and widely used as universal barcode marker for plants, but a comprehensive, updated and accurate reference dataset of plant ITS sequences has not been available so far. Here, we constructed reference datasets of Viridiplantae ITS1, ITS2 and entire ITS sequences including both Chlorophyta and Streptophyta. The sequences were retrieved from NCBI, and the ITS region was extracted. The sequences underwent identity check to remove misidentified records and were clustered at 99% identity to reduce redundancy and computational effort. For this step, we developed a script called 'better clustering for QIIME' (bc4q) to ensure that the representative sequences are chosen according to the composition of the cluster at a different taxonomic level. The three datasets obtained with the bc4q script are PLANiTS1 (100\u2009224 sequences), PLANiTS2 (96\u2009771 sequences) and PLANiTS (97\u2009550 sequences), and all are pre-formatted for QIIME, being this the most used bioinformatic pipeline for metabarcoding analysis. Being curated and updated reference databases, PLANiTS1, PLANiTS2 and PLANiTS are proposed as a reliable, pivotal first step for a general standardization of plant DNA metabarcoding studies. The bc4q script is presented as a new tool useful in each research dealing with sequences clustering. Database URL: https://github.com/apallavicini/bc4q; https://github.com/apallavicini/PLANiTS

Environmental DNA assessment of airborne plant and fungal seasonal diversity

Environmental DNA (eDNA) metabarcoding and metagenomics analyses can improve taxonomic resolution in biodiversity studies. Only recently, these techniques have been applied in aerobiology, to target bacteria, fungi and plants in airborne samples. Here, we present a nine-month aerobiological study applying eDNA metabarcoding in which we analyzed simultaneously airborne diversity and variation of fungi and plants across five locations in North and Central Italy. We correlated species composition with the ecological characteristics of the sites and the seasons. The most abundant taxa among all sites and seasons were the fungal genera Cladosporium, Alternaria, and Epicoccum and the plant genera Brassica, Corylus, Cupressus and Linum, the latter being much more variable among sites. PERMANOVA and indicator species analyses showed that the plant diversity from air samples is significantly correlated with seasons, while that of fungi varied according to the interaction between seasons and sites. The results consolidate the performance of a new eDNA metabarcoding pipeline for the simultaneous amplification and analysis of airborne plant and fungal particles. They also highlight the promising complementarity of this approach with more traditional biomonitoring frameworks and routine reports of air quality provided by environmental agencies

Ocean literacy in European Oceanographic Agencies: EuroGOOS recomendations for the UN Decade of Ocean Science for Sustainable Development 2021-2030. EuroGOOS Policy Brief

Postprin

Pretty good state transfer in qubit chains-The Heisenberg Hamiltonian

Pretty good state transfer in networks of qubits occurs when a continuous-time quantum walk allows the transmission of a qubit state from one node of the network to another, with fidelity arbitrarily close to 1. We prove that in a Heisenberg chain with n qubits, there is pretty good state transfer between the nodes at the jth and (n − j + 1)th positions if n is a power of 2. Moreover, this condition is also necessary for j = 1. We obtain this result by applying a theorem due to Kronecker about Diophantine approximations, together with techniques from algebraic graph theory

Learning Quantum Systems

Quantum technologies hold the promise to revolutionise our society with ground-breaking applications in secure communication, high-performance computing and ultra-precise sensing. One of the main features in scaling up quantum technologies is that the complexity of quantum systems scales exponentially with their size. This poses severe challenges in the efficient calibration, benchmarking and validation of quantum states and their dynamical control. While the complete simulation of large-scale quantum systems may only be possible with a quantum computer, classical characterisation and optimisation methods (supported by cutting edge numerical techniques) can still play an important role. Here, we review classical approaches to learning quantum systems, their correlation properties, their dynamics and their interaction with the environment. We discuss theoretical proposals and successful implementations in different physical platforms such as spin qubits, trapped ions, photonic and atomic systems, and superconducting circuits. This review provides a brief background for key concepts recurring across many of these approaches, such as the Bayesian formalism or Neural Networks, and outlines open questions