The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase the accuracy of rating prediction by using additional information. However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction. In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items. The proposed algorithm is easy to implement. The main advantage of the algorithm is that the running time is polynomial, namely it is θ(n2), for sparse matrices. Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms.
In this study, we presented the most commonly employed net photosynthetic light-response curves (PN/I curves) fitted by the Solver function of Microsoft Excel. Excel is attractive not only due to its wide availability as a part of the Microsoft Office suite but also due to the increased level of familiarity of undergraduate students with this tool as opposed to other statistical packages. In this study, we explored the use of Excel as a didactic tool which was built upon a previously published paper presenting an Excel Solver tool for calculation of a net photosynthetic/chloroplastic CO2-response curve. Using the Excel spreadsheets accompanying this paper, researchers and students can quickly and easily choose the best fitted PN/I curve, selecting it by the minimal value of the sum of the squares of the errors. We also criticized the misuse of the asymptotic estimate of the maximum gross photosynthetic rate, the light saturation point estimated at a specific percentile of maximum net photosynthetic rate, and the quantum yield at zero photosynthetic photon flux density and we proposed the replacement of these variables by others more directly linked to plant ecophysiology. and F. de A. Lobo ... [et al.].
Environmental factors that influence stomatal conductance (gs) interact through a complex network of signal transduction and have therefore highly interdependent effect.
In the present study we examined how plant water status affects stomatal sensitivity to the change of CO2 concentration ([CO2]). We investigated the short-term dynamic of stomatal response to a sudden [CO2] increase (from 400 to 700 µmol(CO2) mol-1) in maize supplied with different amounts of water (resulting ψw = -0.35, -0.52 and -0.75 MPa). Gas exchange measurements were performed in short logging intervals and the response was monitored under two different levels of water vapour pressure deficit (VPD) of 1 and 2 kPa in order to observe the impact of air humidity. Generalized logistic curves were fitted to standardized stomatal response data, which enabled us to objectively estimate the level (relative decrease of g s) and the dynamics of the response.
Soil water stress and high VPD significantly decreased relative stomatal closure in response to [CO2] rise, but simultaneously accelerated stomatal response to [CO2], as revealed by shorter half life (t1/2). VPD significantly affected the response of well-watered plants. In contrast, a fast stomatal reaction of water-deprived plants was predetermined by a low xylem water potential (ψw) of the leaf and the influence of air humidity was minor. and J. Hladnik ... [et al.].