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In the agroecosystem, surface crop residues are widely recognized as affecting many processes such as soil water dynamics, crop growth, nitrogen and carbon cycling. For this reason, developing models that simulate the effect of surface residues and their decomposition is crucial, especially while modeling conservation agriculture. To date, even though many cropping systems and C-oriented models differently simulate the evolution of surface residue biomass, a comprehensive approach is still missing. In this study, we developed a new simulation module that explicitly simulates the decomposition of surface residues, by including all the variables and processes that are relevant for agroecosystem's simulation. This module has been later integrated into the ARMOSA cropping system model. To quantify the contribution of each parameter to the simulated outputs (i.e., decomposed biomass), a sensitivity analysis (SA) was conducted, comparing the result with the APSIM model used as a benchmark. The SA was conducted on four different crop residues (maize, rye, soybean and wheat) over three different years. In addition, for each crop residue, we verified whether parameters changed their relevance depending on the considered time period. The most critical parameters of the new module reflected the importance of air temperature, soil water content and residue biomass in the decomposition process. The po-tential decomposition rate had minor importance, highlighting that, when setting crop-specific values, other environment-related parameters are more relevant for the actual decomposition rate. In the case of APSIM model, the potential decomposition rate and the optimum temperature for this process resulted in the first two ranks. Finally, concordance coefficients were used to compare SA outputs: compared to APSIM, the new model showed higher concordance passing from one crop residue to another, even when comparing the different simulation periods within the same crop. In summary, t

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as, a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as, called sunny-as2, was then submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work we present the technical advancements of sunny-as2, by detailing through several empirical evaluations and by providing new insights. Its current version, built on the top of the preliminary version submitted to OASC, is able to outperform sunny-as and other state-of-the-art AS methods, including those who did not attend the challenge.

Recent experiments demonstrated the generation of entanglement by quasiadiabatically driving through quantum phase transitions of a ferromagnetic spin-1 Bose-Einstein condensate in the presence of a tunable quadratic Zeeman shift. We analyze, in terms of the Fisher information, the interferometric value of the entanglement accessible by this approach. In addition to the Twin-Fock phase studied experimentally, we unveil a second regime, in the broken axisymmetry phase, which provides Heisenberg scaling of the quantum Fisher information and can be reached on shorter time scales. We identify optimal unitary transformations and an experimentally feasible optimal measurement prescription that maximize the interferometric sensitivity. We further ascertain that the Fisher information is robust with respect to nonadiabaticity and measurement noise. Finally, we show that the quasiadiabatic entanglement preparation schemes admit higher sensitivities than dynamical methods based on fast quenches.

BACKGROUND: Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients. Thus, alternative methods should be proposed. METHODS: In this work we evaluate different approaches for predicting whether a patient will survive or not according to simple and easily measurable observations. We conducted a rigorous, comparative study based on the most important prediction techniques using real clinical data of the US National Trauma Data Bank. RESULTS: Empirical results show that well-known Machine Learning classifiers can outperform the TRISS methodology. Based on our findings, we can say that the best approach we evaluated is Random Forest: it has the best accuracy, the best area under the curve, and k-statistic, as well as the second-best sensitivity and specificity. It has also a good calibration curve. Furthermore, its performance monotonically increases as the dataset size grows, meaning that it can be very effective to exploit incoming knowledge. Considering the whole dataset, it is always better than TRISS. Finally, we implemented a new tool to compute the survival of victims. This will help medical practitioners to obtain a better accuracy than the TRISS tools. CONCLUSION: Random Forests may be a good candidate solution for improving the predictions on survival upon the standard TRISS methodology.