Energy spectra of two interacting fermions with spin-orbit coupling in a harmonic trap

We explore the two-body spectra of spin-$1/2$ fermions in isotropic harmonic traps with external spin-orbit potentials and short range two-body interactions. Using a truncated basis of total angular momentum eigenstates, non-perturbative results are presented for experimentally realistic forms of the spin-orbit coupling: a pure Rashba coupling, Rashba and Dresselhaus couplings in equal parts, and a Weyl-type coupling. The technique is easily adapted to bosonic systems and other forms of spin-orbit coupling.Comment: 12 pages, 9 figure

MAPPING SOIL ORGANIC CARBON DYNAMICS OVER THE LAST DECADES IN MEDITERRANEAN AGRO-ECOSYSTEMS WITH LEGACY DATA

Summary Soil organic carbon (SOC) represents the biggest carbon pool of the biosphere, bigger than the living plant pool. In agriculture, SOC is of pivotal importance for sustainable soil management and is a main soil fertility indicator. As soils are responsible for food production and the provision of various ecosystem services, there is a sturdy interest in understanding how land use and management affect natural plant and crop growth, and ecosystem resilience and functioning. These processes require time and soil sustainability is to be evaluated in a long-term economic perspective by policy makers with the aim of maintaining adequate, and likely improved, conditions of the soil and the whole farm for the future. Thus, long-term actions for crop sustainability could also admit little short-time yield reduction if yield potential, stability and environmental health are maintained at the long-time. Food production and ecosystem services provision depend on the maintenance, or increase, of SOC in agricultural soil, since SOC act as a short-term nutrient reservoir, increase water holding capacity and soil infiltration rate, reduce soil compaction, and favour soil resilience against pollutants. These effects should be taken into account at both a narrow and broad geographical breadth. When aiming to manage SOC at broad geographical extent, a detailed knowledge of SOC distribution and likely change in time is required. However, such a knowledge relies on correct sampling method and modelling procedures that in turn depend on the environmental variability of the area under study. Mediterranean areas are frequently variable as an harbour, the area has been subjected to a high share of soil and above-ground biodiversity and experienced long cultivation history and intensification since the last century, which increased their fragility. In this environment, the acquisition of reliable information on SOC can require a highly dense sampling, which can also negatively affect some relict environment. In addition, sampling can imply a high cost for field work and laboratory analyses. The aim of my Ph.D. work was thus to investigate the main factors related to SOC spatial distribution in agricultural land under various pedoclimatic conditions in semiarid Mediterranean areas, using a legacy soil database (1968-2008) of SOC and soil bulk density. The dissertation is structured in six chapters: the first one is a general introduction where the rationale of the dissertation is explained, and the research questions are stated. The second chapter is a novel approach to systematically collecting literature from international peer-review issues, namely systematic map. The third one is an analysis of the legacy soil database, which intends to make the database ready to be used for the SOC assessment and for the digital soil mapping. The fourth chapter touches an issue dealing with SOC stock mapping with the boosted regression tree and a set of covariates to produce local SOC benchmarks to be compared with European and Global SOC maps. The fifth chapter fits in the same modelling frame and it is addressed at the SOC dynamics using the most widespread legacy sampling campaign. A high number of available spatial data were collected and computed and used to calibrate the SOC models. At this stage, due to the ungridded structure of the data, a machine learning based model has been used (Boosted Regression Trees). The last chapter is a comparison of models (geostatistical, machine learning and linear), and shows useful information about the way that the error is reported by each algorithm. Soil maps are not just produced for the sake of creating attractive geographical visualizations: they have a very precise task to fulfil, i.e. provide accurate and reliable information on soil properties that decision makers can use to plan interventions of any kind. The use of the Regression Kriging and Boosted Regression Trees models, which resulted in the best prediction performance in terms of R2 and RMSE, highlighted the SOC dependence on environmental factors, and the prediction of the agricultural land covers. All land cover groups were studied in the preliminary stage of this study (chapter 2), while only the cropland identified with the legacy data was the candidate for the development of the final models which lead to the detection of a positive SOC trend. The last chapter aimed at the comparison between geostatistical, machine learning and linear models to predict SOC in agricultural lands, and an improvement in local uncertainty estimation. The outstanding result was that SOC at the monitoring sites were accurately simulated, being in full agreement with observed data. Once more, actual data will be available and the model will be calibrated and validated, a model of SOC potential sequestration regional scale can be produced. The results of this dissertation has led to a clear and shared vision in the community regarding the selection of the estimation methods for SOC prediction needs to be based on careful considerations. It is good practice to test algorithms already used in literature for similar purposes, but it may be counterproductive to only look at an algorithm because it is new and never used before in a particular field. This sometimes happens in science where methods are selected only because fashionable and not based on real and tested experiments. In the dissertation the origin of the data was sometimes know and sometimes it has been data driven based. In particular, sampling design was based on geostatistics only in the 2008 campaign and it may well be that looking at very advanced methods like deep-learning could be interesting, but still less accurate than the geostatistical kriging based algorithms, which can also provide robust and well tested uncertainty estimations. In summary, even though we have now access to advanced algorithms it does not mean that we need to use them blindly without fully considering what we are trying to achieve with our working hypothesis and research question

Simulations and performance of the QUBIC optical beam combiner

QUBIC, the Q & U Bolometric Interferometer for Cosmology, is a novel ground-based instrument that aims to measure the extremely faint B-mode polarisation anisotropy of the cosmic microwave background at intermediate angular scales (multipoles of l = 30 – 200). Primordial B-modes are a key prediction of Inflation as they can only be produced by gravitational waves in the very early universe. To achieve this goal, QUBIC will use bolometric interferometry, a technique that combines the sensitivity of an imager with the immunity to systematic effects of an interferometer. It will directly observe the sky through an array of back-to-back entry horns whose beams will be superimposed using a cooled quasioptical beam combiner. Images of the resulting interference fringes will be formed on two focal planes, each tiled with transition-edge sensors, cooled down to 320 mK. A dichroic filter placed between the optical combiner and the focal planes will select two frequency bands (centred at 150 GHz and 220 GHz), one frequency per focal plane. Polarization modulation will be achieved using a cold stepped half-wave plate (HWP) and polariser in front of the sky-facing horns. The full QUBIC instrument is described elsewhere; in this paper we will concentrate in particular on simulations of the optical combiner (an off-axis Gregorian imager) and the feedhorn array. We model the optical performance of both the QUBIC full module and a scaled-down technological demonstrator which will be used to validate the full instrument design. Optical modelling is carried out using full vector physical optics with a combination of commercial and in-house software. In the high-frequency channel we must be careful to consider the higher-order modes that can be transmitted by the horn array. The instrument window function is used as a measure of performance and we investigate the effect of, for example, alignment and manufacturing tolerances, truncation by optical components and off-axis aberrations. We also report on laboratory tests carried on the QUBIC technological demonstrator in advance of deployment to the observing site in Argentina

Convolutional Neural Networks for Water segmentation using Sentinel-2 Red, Green, Blue (RGB) composites and derived Spectral Indices

Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achieved using spectral indices. Spectral water segmentation is limited by environmental factors and requires human expertise to be applied effectively. In recent years, the use of convolutional neural networks (CNN’s) for water segmentation has been successful when used on high-resolution satellite imagery, but to a lesser extent for medium resolution imagery. Existing studies have been limited to geographically localized datasets and reported metrics have been benchmarked against a limited range of spectral indices. This study seeks to determine if a single CNN based on Red, Green, Blue (RGB) image classification can effectively segment water on a global scale and outperform traditional spectral methods. Additionally, this study evaluates the extent to which smaller datasets (of very complex pattern, e.g harbour megacities) can be used to improve globally applicable CNNs within a specific region. Multispectral imagery from the European Space Agency, Sentinel-2 satellite (10 m spatial resolution) was sourced. Test sites were selected in Florida, New York, and Shanghai to represent a globally diverse range of waterbody typologies. Region-specific spectral water segmentation algorithms were developed on each test site, to represent benchmarks of spectral index performance. DeepLabV3-ResNet101 was trained on 33,311 semantically labelled true-colour samples. The resulting model was retrained on three smaller subsets of the data, specific to New York, Shanghai and Florida. CNN predictions reached a maximum mean intersection over union result of 0.986 and F1-Score of 0.983. At the Shanghai test site, the CNN’s predictions outperformed the spectral benchmark, primarily due to the CNN’s ability to process contextual features at multiple scales. In all test cases, retraining the networks to localized subsets of the dataset improved the localized region’s segmentation predictions. The CNN’s presented are suitable for cloud-based deployment and could contribute to the wider use of satellite imagery for water management

The Fairness of Fair Trade: An Analysis of the Economics of Fair Trade

In 2015, a study done by Cone Communications found that millennials are universally more engaged in corporate social responsibility. In fact, 87% of millennials are willing to purchase a product with social or environmental benefits. Enter, the fair trade label. The fair trade label, which is attached to products which meet the previously mentioned consumer demands, has emerged over the last three decades. Products like organic produce, textiles, and natural commodities have entered into global retailers and supermarkets through these non-traditional distribution channels, supported by increased consumption as well as changing consumer preferences. In order to uncover the underlying economic and social benefits and potential disadvantages, an analysis of the fair trade model has been conducted. Additionally, this paper will examine the future outlook of fair trade labels and how companies are creating niche business strategies within the model to develop sustained competitive advantages

Analysis of the influence of tectonics on the evolution of valley networks based on Srtm Dem, Jemma River basin, Ethiopia

The Ethiopian Highlands are a good example of a high plateau landscape formed by a combination of tectonic uplift and episodic volcanism. Deeply incised gorges indicate active fluvial erosion, which leads to instabilities of over-steepened slopes. In this study we focus on the Jemma River basin, which is a left bank tributary of the Abay - Blue Nile in order to assess the influence of neotectonics on the evolution of its river and valley network. Tectonic lineaments, shape of valley networks, direction of river courses and intensity of fluvial erosion were compared in six subregions, which were delineate beforehand by means of morphometric analysis. The influence of tectonics on the valley network is low in the older deep and wide canyons and on the high plateau covered with Tertiary lava flows, whilst in the younger upper part of the canyons it is high. Furthermore, the coincidence of the valley network with the tectonic lineaments differs in the subregions. The direction of the fluvial erosion along the main tectonic zones (NE-SW) made it possible for backward erosion to reach far distant areas in the east. This tectonic zone also separates older areas in the west from the youngest landscape evolution subregions in the east, next to the Rift Valley

Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands

BACKGROUND: Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0-30 cm soil depth and tested. RESULTS: The measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05. A Wilcoxon test applied to the variation of SOC from 1994 to 2017 suggested that there was not a statistical difference in SOC concentration after 23 years (Z = - 0.556; 2-tailed asymptotic significance = 0.578). In particular, only 40% of resampled sites showed a higher SOC concentration than in 2017. CONCLUSIONS: This finding contrasts with a previous SOC concentration increase that was found in 2008 (75.8% increase when estimated as differences of 2 models built with non-paired data), when compared to 1994 observed data (Z = - 9.119; 2-tailed asymptotic significance < 0.001). This suggests that the use of legacy data to estimate SOC concentration dynamics requires soil resampling in the same locations to overcome the stochastic model errors. Further experiment is needed to identify the percentage of the sites to resample in order to align two legacy datasets in the same area

Synthesis and Photophysical Properties of 9,10-Disubstituted Anthracenes

We report the synthesis and photophysical characterization of four 9,10-disubstituted diphenylanthracenes with specific modifications of the model backbone which involve both the 9,10 para substituents at the phenyl rings and the substitution with carbon-carbon triple bonds. The effects of such modifications on the photoluminescence and electroluminescence properties have been investigated on the basis of the diphenylanthracene molecular characteristics and in view of application to light-emitting devices. We have found that the substitution with the carbon-carbon triple bonds at the two 9,10-phenyls noticeably alters the electronic states of the reference molecule, also introducing a certain degree of sensitivity to the phenyl substituents, which improves the tunability of the optical emission. Differently, the 9,10 para substituents produce minor changes in the single-molecule properties, due to the lack of electronic conjugation across the 9,10-phenyls. However, even a single nitro substituent in the phenyl para position produces the formation of excimers, which appreciably reduces the optical quantum efficiency. These properties are maintained in solid-state blends and simple spin-coated bilayer electroluminescent devices have been fabricated