Strongly-Driven One-Atom Laser and Decoherence Monitoring

We propose the implementation of a strongly-driven one-atom laser, based on the off-resonant interaction of a three-level atom in $\Lambda$-configuration with a single cavity mode and three laser fields. We show that the system can be described equivalently by a two-level atom resonantly coupled to the cavity and driven by a strong effective coherent field. The effective dynamics can be solved exactly, including a thermal field bath, allowing an analytical description of field statistics and entanglement properties. We also show the possible generation of Schr\"odinger cat states for the whole atom-field system and for the field alone after atomic measurement. We propose a way to monitor the system decoherence by measuring atomic population. Finally, we confirm the validity of our model through numerical solutions.Comment: 9 pages, 7 figures Accepted in Phys. Rev.

On the impact of controlled wall roughness shape on the flow of a soft-material

We explore the impact of geometrical corrugations on the near-wall flow properties of a soft-material driven in a confined rough microchannel. By means of numerical simulations, we perform a quantitative analysis of the relation between the flow rate $\Phi$ and the wall stress $\sigma_w$ for a number of setups, by changing both the roughness values as well as the roughness shape. Roughness suppresses the flow, with the existence of a characteristic value of $\sigma_w$ at which flow sets in. Just above the onset of flow, we quantitatively analyze the relation between $\Phi$ and $\sigma_w$. While for smooth walls a linear dependency is observed, steeper behaviours are found to set in by increasing wall roughness. The variation of the steepness, in turn, depends on the shape of the wall roughness, wherein gentle steepness changes are promoted by a variable space localization of the roughness

ReForeSt: Random forests in apache spark

Random Forests (RF) of tree classifiers are a popular ensemble method for classification. RF are usually preferred with respect to other classification techniques because of their limited hyperparameter sensitivity, high numerical robustness, native capacity of dealing with numerical and categorical features, and effectiveness in many real world classification problems. In this work we present ReForeSt, a Random Forests Apache Spark implementation which is easier to tune, faster, and less memory consuming with respect to MLlib, the de facto standard Apache Spark machine learning library. We perform an extensive comparison between ReForeSt and MLlib by taking advantage of the Google Cloud Platform (https://cloud.google.com). In particular, we test ReForeSt and MLlib with different library settings, on different real world datasets, and with a different number of machines equipped with different number of cores. Results confirm that ReForeSt outperforms MLlib in all the above mentioned aspects. ReForeSt is made publicly available via GitHub (https://github.com/alessandrolulli/reforest)

Tripartite quantum state mapping and discontinuous entanglement transfer in a cavity QED open system

We describe the transfer of quantum information and entanglement from three flying (radiation) to three localized (atomic) qubits via cavity modes resonantly coupled to the atoms, in the presence of a common reservoir. Upon addressing the full dynamics of the resulting nine-qubit open system, we find that once the cavities are fed, fidelity and transferred entanglement are optimal, while their peak values exponentially decrease due to dissipative processes. The external radiation is then turned off and quantum correlations oscillate between atomic and cavity qubits. For a class of mixtures of W and GHZ input states we deal with a discontinuous exchange of entanglement among the subsystems, facing the still open problem of entanglement sudden death and birth in a multipartite system.Comment: 7 pages, 6 figures, 2 table

Improving the entanglement transfer from continuous variable systems to localized qubits using non Gaussian states

We investigate the entanglement transfer from a bipartite continuous-variable (CV) system to a pair of localized qubits assuming that each CV mode couples to one qubit via the off-resonance Jaynes-Cummings interaction with different interaction times for the two subsystems. First, we consider the case of the CV system prepared in a Bell-like superposition and investigate the conditions for maximum entanglement transfer. Then we analyze the general case of two-mode CV states that can be represented by a Schmidt decomposition in the Fock number basis. This class includes both Gaussian and non Gaussian CV states, as for example twin-beam (TWB) and pair-coherent (TMC, also known as two-mode-coher ent) states respectively. Under resonance conditions, equal interaction times for both qubits and different initial preparations, we find that the entanglement transfer is more efficient for TMC than for TWB states. In the perspective of applications such as in cavity QED or with superconducting qubits, we analyze in details the effects of off-resonance interactions (detuning) and different interaction times for the two qubits, and discuss conditions to preserve the entanglement transfer.Comment: revised version, 11 pages, 7 figures (few of them low-res

Durum wheat grain yield and quality as affected by S rate under Mediterranean conditions

The decreased inputs of S from atmospheric acidic deposition, the use of low S-containing fertilisers, and the decrease of organic matter content in soil resulted in S deficiency in many agricultural regions of the world. Sulphur fertilisation significantly affects grain yield and protein composition of cereals, thus altering the technological quality of grain. Field experiments were conducted in central Italy in two subsequent seasons to investigate the effects ofNand S application on five commercial wheat cultivarsknownto differ in yield potential and grain N content. Fertiliser treatments were two levels of N fertiliser (120 kgNha−1 and 180 kgNha−1) and three levels of S fertiliser (not applied, 60 kg S ha−1, and 120 kg S ha−1). Analyzed characters were dry weight and N and S uptake of grain and vegetative plant part, and grain quality characters. Variations in weather pattern – and especially in rainfall – between years significantly influenced grain yield and N and S content of grain, but did not affect quality parameters. Nitrogen and S application also significantly affected grain yield and the quality characteristicsW, P/L, dry gluten and SDS, although no interactive effect between treatments was observed. The highest protein content and W in grain was obtained with the combination of the highest fertiliser rates: 180 kgNha−1 and 120 kg S ha−1. Genotypes differed for yield stability between years, in that grain production was decreased in the driest year only in the varieties Claudio and Creso, but they responded similarly to N and S fertilisation. Genotypes differed also for protein concentration and quality parameters, and, on average, the varieties Duilio, Simeto and Svevo gave better performances. Differences in grain quality parameters were maintained through years, indicating that these traits are under strong genetic control

Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm

Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems.</p

Post-anthesis dry matter and nitrogen dynamics in durum wheat as affected by nitrogen supply and soil water availability.

Durum wheat (Triticum durum Desf.) is commonly grown in dryland conditions, where environmental stress during grain filling can limit productivity and increase the dependency on stored assimilate.We investigated current assimilation and remobilization of dry matter and nitrogen during grain filling in N fertilized and unfertilized durum wheat subjected to different levels of water deficit during grain filling. Two durum wheat varieties, Appio and Creso, were grown in open-air containers with three rates of nitrogen fertilizer (not applied, N0; normal amount, NN; high amount, NH) and four water regimes during grain filling (fully irrigated treatment, FI; low, LWS, moderate, MWS and high water stress, HWS) across 2 years. Grain yield and dry matter and N accumulation and remobilization were positively affected by N availability and negatively by water stress during grain filling. The reduction of grain yield by severe post-anthesis water stress amounted to 27 and 37% for N0 and NN, respectively, and was associated with a decrease in kernel weight. There was also a small negative effect on the number of kernels per spike. Conversely, the duration of grain filling was not modified either by water stress or by nitrogen treatments. Severe water stress also reduced dry matter accumulation and remobilization by 36 and 14% in N0 plants and by 48 and 25% in NH plants. Similarly, N accumulation and N remobilization was reduced by 43% and by 16% in N0 plants and by 51% and by 15% in NH plants. Conversely, low and moderate water stress did not substantially modify the patterns of dry matter and nitrogen deposition in grain. Although remobilization of dry matter and N was less affected by water stress than accumulation, it was not able to counterbalance the reduction of assimilation and consequently it was not able to stabilize grain yield under drought

Stigma and discrimination (Sad) at the time of the sars-cov-2 pandemic

Infectious disease control is a crucial public health issue. Although it is important to urgently perform public health measures in order to reduce the risk of spread, it could end up stigmatizing entire groups of people rather than offering control measures based on sound scientific principles. This “us” versus “them” dynamic is common in stigmatization, in general, and indicates a way in which disease stigma can be viewed as a proxy for other types of fears, especially xenophobia and general fear of outsiders. The pandemic risk associated with SARS-CoV-2 infection led us to consider, among other related issues, how stigma and discrimination remain serious barriers to care for people suspected of being infected, even more if they are assisting professions, such as health workers, employed in emergency response. The purpose of this review is to evaluate and promote the importance of psychological aspects of the stigma and social discrimination (SAD) in pandemic realities and, more specifically, nowadays, in the context of SARS-CoV-2/COVID-19. Just as it happened with HIV, HCV, tuberculosis, and Zika, stigma and discrimination undermine the social fabric compromising the ethics and principles of civilization to which each individual in entitled. Recognizing disease stigma history can give us insight into how, exactly, stigmatizing attitudes are formed, and how they are disbanded. Instead of simply blaming the ignorance of people espousing stigmatizing attitudes about certain diseases, we should try to understand precisely how these attitudes are formed so that we can intervene in their dissemination. We should also look at history to see what sorts of interventions against stigma may have worked in the past. Ongoing research into stigma should evaluate what has worked in the past, as above-mentioned, providing us with some clues as to what might work in the current pandemic emergency, to reduce devastating discrimination that keeps people from getting the care they need. We propose a systematic and historical review, in order to create a scientific and solid base for the following SAD analysis. The aim is to propose a coping strategy to face stigma and discrimination (SAD) related to SARS-CoV-2/COVID-19 pandemic outbreak, borrowing coping strategy tools and solutions from other common contagious diseases. Furthermore, our study observes how knowledge, education level, and socioeconomic status (SES) can influence perception of SARS-CoV-2/ COVID-19 risk in a digital world, based on previous research, best practices, and evidence-based research