Assessing soil key fertility attributes using a portable X-ray fluorescence : a simple method to overcome matrix effect

The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R-2= 0.87), ex-K (R-2 >= 0.94), and ex-Ca (R-2 >= 0.96), whereas clay predictions were comparable with and without CN (0.89 <= R-2 <= 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA)

Forecast in the pharmaceutical area – Statistic models vs deep learning

The main goal of this work was to evaluate the application of statistical and connectionist models for the problem of pharmacy sales forecasting. Since R is one of the most used software environment for statistical computation, we used the functions presented in its forecast package. These functions allowed for the construction of models that were then compared with the models developed using Deep Learning algorithms. The Deep Learning architecture was constructed using Long Short-Term Memory layers. It is very common to use statistical models in time series forecasting, namely the ARIMA model, however, with the arising of Deep Learning models our challenge was to compare the performance of these two approaches applied to pharmacy sales. The experiments studied, showed that for the used dataset, even a quickly developed LSTM model, outperformed the long used R forecasting package ARIMA model. This model will allow the optimization of stock levels, consequently the reduction of stock costs, possibly increase the sales and the optimization of human resources in a pharmacy.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

Timescales of carbon turnover in soils with mixed crystalline mineralogies

Organic matter–mineral associations stabilize much of the carbon (C) stored globally in soils. Metastable short-range-order (SRO) minerals such as allophane and ferrihydrite provide one mechanism for long-term stabilization of organic matter in young soil. However, in soils with few SRO minerals and a predominance of crystalline aluminosilicate or Fe (and Al) oxyhydroxide, C turnover should be governed by chemisorption with those minerals. Here, we correlate mineral composition from soils containing small amounts of SRO minerals with mean turnover time (TT) of C estimated from radiocarbon (¹⁴C) in bulk soil, free light fraction and mineral-associated organic matter. We varied the mineral amount and composition by sampling ancient soils formed on different lithologies in arid to subhumid climates in Kruger National Park (KNP), South Africa. Mineral contents in bulk soils were assessed using chemical extractions to quantify Fe oxyhydroxides and SRO minerals. Because of our interest in the role of silicate clay mineralogy, particularly smectite (2 : 1) and kaolinite (1 : 1), we separately quantified the mineralogy of the clay-sized fraction using X-ray diffraction (XRD) and measured ¹⁴C on the same fraction. <br><br> Density separation demonstrated that mineral associated C accounted for 40–70 % of bulk soil organic C in A and B1 horizons for granite, nephelinite and arid-zone gabbro soils, and &gt; 80 % in other soils. Organic matter strongly associated with the isolated clay-sized fraction represented only 9–47 % of the bulk soil C. The mean TT of C strongly associated with the clay-sized fraction increased with the amount of smectite (2 : 1 clays); in samples with &gt; 40 % smectite it averaged 1020 ± 460 years. The C not strongly associated with clay-sized minerals, including a combination of low-density C, the C associated with minerals of sizes between 2 µm and 2 cm (including Fe oxyhydroxides as coatings), and C removed from clay-sized material by 2 % hydrogen peroxide had TTs averaging 190 ± 190 years in surface horizons. Summed over the bulk soil profile, we found that smectite content correlated with the mean TT of bulk soil C across varied lithologies. The SRO mineral content in KNP soils was generally very low, except for the soils developed on gabbros under more humid climate that also had very high Fe and C contents with a surprisingly short, mean C TTs. In younger landscapes, SRO minerals are metastable and sequester C for long timescales. We hypothesize that in the KNP, SRO minerals represent a transient stage of mineral evolution and therefore lock up C for a shorter time. <br><br> Overall, we found crystalline Fe-oxyhydroxides (determined as the difference between Fe in dithionate citrate and oxalate extractions) to be the strongest predictor for soil C content, while the mean TT of soil C was best predicted from the amount of smectite, which was also related to more easily measured bulk properties such as cation exchange capacity or pH. Combined with previous research on C turnover times in 2 : 1 vs. 1 : 1 clays, our results hold promise for predicting C inventory and persistence based on intrinsic timescales of specific carbon–mineral interactions

Semi-supervised cross-entropy clustering with information bottleneck constraint

In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting goals: the accuracy with which the data set is modeled, the simplicity of the model, and the consistency of the clustering with side information. Experiments demonstrate that CEC-IB has a performance comparable to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but is faster, more robust to noisy labels, automatically determines the optimal number of clusters, and performs well when not all classes are present in the side information. Moreover, in contrast to other semi-supervised models, it can be successfully applied in discovering natural subgroups if the partition-level side information is derived from the top levels of a hierarchical clustering

The Irish Rural Environmental Protection Scheme and lack of Strategic Environmental Assessment

working paperProposals specifically aimed at delivering environmental benefits are often exempt from assessment, despite evidence that they can be poorly thought-through and sometimes counterproductive. This is doubly true of agri-environmental schemes where local farm-scale actions are expected to generate large-area cumulative effects on soil and water quality, biodiversity or landscape. There is evidence that the benefits of such schemes have often been assumed rather than planned for, thus necessitating ex-post assessment to justify their continuance

Changing medical student attitudes to patient safety: A multicentre study

Background: Although patient safety is becoming widely taught in medical schools, its effect has been less rigorously evaluated. We describe a multicentre study to evaluate student changes in patient safety attitudes using a standardized instrument, the Attitudes to Patient Safety Questionnaire3 (APSQ3). Methods: A patient safety training package designed for medical students was delivered in the first year and second year in four Australian medical schools. It comprises eight face-to-face modules, each of two hours. Seminars start with an interactive introduction using questions, video and role play, followed by small group break-outs to discuss a relevant case study. Groups are led by medical school tutors with no prior training in patient safety. Students and tutors then reassemble to give feedback and reinforce key concepts. Knowledge and attitudes to patient safety were measured using the APSQ3, delivered prior to safety teaching, at the end of the first and second years and 12 months after teaching ceased. Results: A significant improvement in attitude over time was demonstrated for four of nine key items measured by the APSQ3: value of patient safety teaching; danger of long working hours, value of team work and the contribution patients can make in reducing error. Informal feedback from students was very positive. Conclusion: We showed persistent, positive learning from a patient safety education intervention 12 months after teaching finished. Building on the introduction of patient safety teaching into medical schools, pathways for motivated students such as appropriate electives, option terms and team-based research projects would be of value

Organic Trends 8

Newsletter on Organic agriculture and food development in China

TEM, XRD, and Thermal Stability of Adsorbed Paranitrophenol on DDOAB Organoclay

Water purification is of extreme importance to modern society. Organoclays through adsorption of recalcitrant organics provides one mechanism for the removal of these molecules. The organoclay was synthesised through ion exchange with dimethyldioctadecylammonium bromide labeled as DDOAB of formula (CH3(CH2)17)2NBr(CH3)2. Paranitrophenol was adsorbed on the organoclay at a range of concentrations according to the cation exchange capacity (CEC) of the host montmorillonite. The paranitrophenol in solution was analysed by a UV-260 spectrophotometer at 317nm, with detection limits being 0.05mg/L. The expansion of the montmorillonite was studied by a combination of X-ray diffraction and transmission electron microscopy. Upon adsorption of the paranitrophenol the basal spacing decreased. The thermal stability of the organoclay was determined by a combination of thermogravimetry and infrared emission spectroscopy. The surfactant molecule DDOAB combusts at 166, 244 and 304 degrees Celsius and upon intercalation into Na-montmorillonite is retained up to 389 degrees Celsius thus showing the organoclay is stable to significantly high temperatures well above the combustion/decomposition temperature of the organoclay