Plant analysis as a tool to determine crop nitrogen status

An effective plant nutrient management strategy optimises nitrogen (N) use efficiency for minimised environmental impact, while ensuring an optimum N status of the crop for good product quality and maximum growth. Soil or plant analysis can be used to evaluate the strategy; however the use of plant analysis for this purpose has been limited. One reason is lack of reliable reference values for the critical concentration needed for optimal growth. This study builds on theories that relate ontogenetic changes in the critical N concentration to changes in the relation between mass and surface area of the entire plant and of individual leaves. Through the establishment of critical N concentrations on the basis of these theories, some of the drawbacks hitherto experienced with plant analysis, such as difficulties in defining growth stage or plant part to sample, can be avoided. The aim of this thesis was to establish critical N concentrations for white cabbage (Brassica oleracea L. var. capitata L. f. alba D.C.) on the basis of these theories. Multi-N-rate and multi-harvest experiments were conducted in the field and in a climate chamber. The results showed that the critical N concentration declined at the same rate (-0.33) as the plant's leaf area ratio (leaf area divided by plant mass), which is in agreement with the 2/3-Power rule or "skin-core" hypothesis. The critical N concentration (% of DM) on a whole plant basis was estimated to 4.5 (W1.5 t ha-1), where W is weight per unit area of plant dry matter exclusive of roots. Moreover, it was concluded that the unshaded horizontally orientated leaves of cabbage can be used for leaf area based plant analysis of individual leaves. The critical N concentration of these leaves expressed on an area basis was found to be 3.7 g N m-2, while that for the whole plant N on a leaf area basis was 4.7 g N m-2. The ratio of these two critical concentrations, 0.8, was similar to the leaf N ratio (leaf N/whole plant N) of young plants before self shading occurs

Artificial neural network based model for evaluating performance of immobilized cell biofilter

[Abstract] Artificial neural networks (ANNs) are powerful data driven modelling tools which has the potential to approximate and interpret complex input/output relationships based on the given sets of data matrix. In this paper, a predictive computerised approach has been proposed to predict the performance of an immobilized cell biofilter treating NH3 vapours in terms of its removal efficiency (RE) and elimination capacity (EC). The input parameters to the ANN model were inlet concentration, loading rate, flow rate and pressure drop, while the output parameters were RE and EC respectively. The data set was divided into two parts, training matrix consisting of 51 data points, while the test matrix had 16 data points representing each parameter considered in this study. Earlier, experiments from continuous operation in the biofilter showed removal efficiencies from 60 to 100% at inlet loading rates varying between 0.5 to 5.5 g NH3/m3.h. The internal network parameters of the ANN model during simulation was selected using the 2k factorial design and the best network topology for the model was thus estimated. The predictions were evaluated based on their determination coefficient values (R2). The results showed that a multilayer network (4-4-2) with a back propagation algorithm was able to predict biofilter performance effectively with R2 values of 0.9825 and 0.9982. The proposed ANN model for biofilter operation could be used as a potential alternative for knowledge based models through proper training and testing of the state variables

Differential Performance Debugging with Discriminant Regression Trees

Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences in asymptotic performance among various input classes in terms of program internals. We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs. We propose a new algorithm for DRT learning that first clusters the data into functional clusters, capturing different asymptotic performance classes, and then invokes off-the-shelf decision tree learning algorithms to explain these clusters. We focus on linear functional clusters and adapt classical clustering algorithms (K-means and spectral) to produce them. For the K-means algorithm, we generalize the notion of the cluster centroid from a point to a linear function. We adapt spectral clustering by defining a novel kernel function to capture the notion of linear similarity between two data points. We evaluate our approach on benchmarks consisting of Java programs where we are interested in debugging performance. We show that our algorithm significantly outperforms other well-known regression tree learning algorithms in terms of running time and accuracy of classification.Comment: To Appear in AAAI 201

Analysis of indentation size effect in copper and its alloys

For describing the indentation size effect (ISE), numerous models, which relate the load or hardness to the indent dimensions, have been proposed. Unfortunately, it is still difficult to associate the different parameters involved in such relationships with physical or mechanical properties of the material. This is an unsolved problem since the ISE can be associated with various causes such as workhardening, roughness, piling-up, sinking-in, indenter tip geometry, surface energy, varying composition and crystal anisotropy. For interpreting the change in hardness with indent size, an original approach is proposed on the basis of composite hardness modelling together with the use of a simple model, which allows the determination of the hardness–depth profile. Applied to copper and copper alloys, it is shown that it is possible to determine the maximum hardness value reached at the outer surface of the material and the distance over which both the ISE and the workhardening take place

Anatomy, drying behaviour and mechanical properties of lesser used wood species from Mozambique

The study provides an overview of Mozambique timber sector, descriptive and comparative wood anatomy, drying experiments and interrelationships between mechanics, density and anatomical features of lesser used species from Mozambique. Exhaustive literature review described the timber sector as dominated by few hardwood species selectively harvested from a forest containing 118 potential wood species. The survey discussed the need to revert the current situation by proposing studies on lesser used timbers to enlarge the resource base and ensure sustainable logging practices. Afterwards, based on growing stock reported in the forest inventory, three lesser used timbers namely ntholo (Pseudolachnostylis maprounaefolia Pax), metil (Sterculia appendiculata K. Schum) and muanga (Pericopsis angolensis Meeuwen) were selected and subjected to descriptive and comparative wood anatomical studies aiming to understand their structure with regard to prospective end uses. Classical methods of wood sectioning and both light microscopy (LM) and scanning electron microscopy (SEM) were used. The results showed that ntholo and muanga are characterized by diffuse porosity, indistinct growth rings, 18 vessel/mm2, extractives in heartwood vessels, ray width (1-3 cells), % fibre proportion (ntholo 57%; muanga 58%). This set of anatomical features typifies dense timbers with recognized strength and good natural durability. Metil has shown wood structure characterized by very wide vessels without extractives recorded in low density (< 5 vessels/mm2), ground tissues dominated by thin-walled axial parenchyma (50-61%) and fibre proportion (17-27%). Metil wood anatomy is typical for light timbers with poor natural durability. Nevertheless, metil timber seems easy to impregnate with wood preservatives as demonstrated by the high uptake of 463 kg/m3. In addition, the study carried out drying experiments on ntholo boards aimed to assign adequate drying schedule. The experiment was conducted in two stages, i.e. non-symmetrical drying tests (NSD) and laboratory batch kiln drying (LBK). NSD was intended to select provisional schedule and LBK to test the performance of the selected schedule based on European standards. The results from NSD assigned provisional schedule (T6-D2) corresponding to other tropical timbers with similar drying behaviour. The LBK lasted 266 h and ensured standard quality drying described by 8.9% final moisture content, a gradient of 1.2% and 1.2 mm of casehardening. Minor deformations were recorded and twist was the largest with an average of 3.4 mm. However, in general, the drying experiment can be regarded as successful since it provided background for industrial drying. Finally, the study determined ntholo timber mechanics and thereafter examined interrelationships with density and anatomical features of ntholo through correlation and regression analysis. The results show that ntholo is a very dense timber with high mechanical strength in comparison to well known timbers. Correlation analysis revealed fibre length as the only anatomical feature significantly correlated to density and all tested mechanical properties of ntholo. The number of vessels/mm2 and %vessels were not significantly correlated to any of the measured properties but appeared to be key anatomical features for predictions under regression analysis. All tested properties of ntholo sapwood seemed to be influenced mainly by ground tissue proportions, while heartwood properties were described by more leveled anatomical predictors. The regression analyses show that both ntholo sapwood and heartwood densities are poor predictors for the tested mechanical properties, although may provide rough indication of tested properties given the observed correlations. The integrated analysis of results from the timber sector review, wood anatomy, drying behaviour and mechanical properties are expected to form a reliable background for a successful utilization of the relatively lesser explored timbers from Mozambique

The Complementarity of Eastern and Western Hemisphere Long-Baseline Neutrino Oscillation Experiments

We present a general formalism for extracting information on the fundamental parameters associated with neutrino masses and mixings from two or more long baseline neutrino oscillation experiments. This formalism is then applied to the current most likely experiments using neutrino beams from the Japan Hadron Facility (JHF) and Fermilab's NuMI beamline. Different combinations of muon neutrino or muon anti-neutrino running are considered. To extract the type of neutrino mass hierarchy we make use of the matter effect. Contrary to naive expectation, we find that both beams using neutrinos is more suitable for determining the hierarchy provided that the neutrino energy divided by baseline ($E/L$) for NuMI is smaller than or equal to that of JHF. Whereas to determine the small mixing angle, $\theta_{13}$, and the CP or T violating phase $\delta$, one neutrino and the other anti-neutrino is most suitable. We make extensive use of bi-probability diagrams for both understanding and extracting the physics involved in such comparisons.Comment: 21 pages, Latex, 3 postscript figure