Yield losses in grain sorghum due to rust infection

Although rust (caused by Puccinia purpurea) is a common disease in Australian grain sorghum crops, particularly late in the growing season (April onwards), its potential to reduce yield has not been quantified. Field trials were conducted in Queensland between 2003 and 2005 to evaluate the effect of sorghum rust on grain yield of two susceptible sorghum hybrids (Tx610 and Pride). Rust was managed from 28-35 days after sowing until physiological maturity by applying oxycarboxin (1 kg active ingredient/100 L of water/ha) every 10 days. When data were combined for the hybrids, yield losses ranged from 13.1% in 2005 to 3.2% in 2003 but differences in yield the between sprayed and unsprayed treatments were statistically significant (P a parts per thousand currency signaEuro parts per thousand 0.05) only in 2005. Final area under the disease progress curve (AUDPC) values reflected the yield losses in each year. The higher yield loss in 2005 can be attributed primarily to the early development of the rust epidemic and the higher inoculum levels in spreader plots at the time of planting of the trials.

Bentonite can decrease ammonia volatilisation losses from poultry litter: laboratory studies

Ammonia volatilisation from manure materials within poultry sheds can adversely affect production, and also represents a loss of fertiliser value from the spent litter. This study sought to compare the ability of alum and bentonite to decrease volatilisation losses of ammonia from spent poultry litter. An in-vessel volatilisation trial with air flushing, ammonia collection, and ammonia analysis was conducted over 64 days to evaluate the mitigation potential of these two materials. Water-saturated spent litter was incubated at 25°C in untreated condition (control) or with three treatments: an industry-accepted rate of alum [4% Al2(SO4)3·18H2O by dry mass of litter dry mass; ALUM], air-dry bentonite (127% by dry mass; BENT), or water-saturated bentonite (once again at 127% by dry mass; SATBENT). A high proportion of the nitrogen contained in the untreated spent litter was volatilised (62%). Bentonite additions were superior to alum additions at retaining spent litter ammonia (nitrogen losses: 15%, SATBENT; 34%, BENT; 54%, ALUM). Where production considerations favour comparable high rates of bentonite addition (e.g. where the litter is to be re-formulated as a fertiliser), this clay has potential to decrease ammonia volatilisation either in-shed or in spent litter stockpiles or formulated products, without the associated detrimental effect of alum on phosphorus availability.

Energy losses at pipe trifurcations

In the case of pipe trifurcation, previous observations report negative energy losses in the centre branch. This causes an anomaly, because there should not be any negative energy loss due to conservation of energy principle. Earlier investigators have suggested that this may be due to the non-inclusion of kinetic energy coefficient (a) in the computations of energy losses without any experimental evidence. In the present work, through experimentally determined velocity profiles, energy loss coefficients have been evaluated. It has been found that with the inclusion of a in the computations of energy loss, there is no negative energy loss in the centre branch.

Clays Can Decrease Gaseous Nutrient Losses from Soil-Applied Livestock Manures

Clays could underpin a viable agricultural greenhouse gas (GHG) abatement technology given their affinity for nitrogen and carbon compounds. We provide the first investigation into the efficacy of clays to decrease agricultural nitrogen GHG emissions (i.e., N2O and NH3). Via laboratory experiments using an automated closed-vessel analysis system, we tested the capacity of two clays (vermiculite and bentonite) to decrease N2O and NH3 emissions and organic carbon losses from livestock manures (beef, pig, poultry, and egg layer) incorporated into an agricultural soil. Clay addition levels varied, with a maximum of 1:1 to manure (dry weight). Cumulative gas emissions were modeled using the biological logistic function, with 15 of 16 treatments successfully fitted (P < 0.05) by this model. When assessing all of the manures together, NH3 emissions were lower (×2) at the highest clay addition level compared with no clay addition, but this difference was not significant (P = 0.17). Nitrous oxide emissions were significantly lower (×3; P < 0.05) at the highest clay addition level compared with no clay addition. When assessing manures individually, we observed generally decreasing trends in NH3 and N2O emissions with increasing clay addition, albeit with widely varying statistical significance between manure types. Most of the treatments also showed strong evidence of increased C retention with increasing clay additions, with up to 10 times more carbon retained in treatments containing clay compared with treatments containing no clay. This preliminary assessment of the efficacy of clays to mitigate agricultural GHG emissions indicates strong promise.

Denitrification losses from an intensively managed sub-tropical pasture: Impact of soil moisture on the partitioning of N2 and N2O emissions

Intensively managed pastures in subtropical Australia under dairy production are nitrogen (N) loaded agro-ecosystems, with an increased pool of N available for denitrification. The magnitude of denitrification losses and N2:N2O partitioning in these agro-ecosystems is largely unknown, representing a major uncertainty when estimating total N loss and replacement. This study investigated the influence of different soil moisture contents on N2 and N2O emissions from a subtropical dairy pasture in Queensland, Australia. Intact soil cores were incubated over 15 days at 80% and 100% water-filled pore space (WFPS), after the application of 15N labelled nitrate, equivalent to 50 kg N ha−1. This setup enabled the direct quantification of N2 and N2O emissions following fertilisation using the 15N gas flux method. The main product of denitrification in both treatments was N2. N2 emissions exceeded N2O emissions by a factor of 8 ± 1 at 80% WFPS and a factor of 17 ± 2 at 100% WFPS. The total amount of N-N2 lost over the incubation period was 21.27 kg ± 2.10 N2-N ha−1 at 80% WFPS and 25.26 kg ± 2.79 kg ha−1 at 100% WFPS respectively. N2 emissions remained high at 100% WFPS, while related N2O emissions decreased. At 80% WFPS, N2 emissions increased constantly over time while N2O fluxes declined. Consequently, N2/(N2 + N2O) product ratios increased over the incubation period in both treatments. N2/(N2 + N2O) product ratios responded significantly to soil moisture, confirming WFPS as a key driver of denitrification. The substantial amount of fertiliser lost as N2 reveals the agronomic significance of denitrification as a major pathway of N loss for sub-tropical pastures at high WFPS and may explain the low fertiliser N use efficiency observed for these agro-ecosystems.

Fraudsters change tactics as a crackdown cuts some losses due to online scams

The amount of financial loss from online fraud suffered by people in Western Australia has almost halved, dropping from A$16.8 million in 2014 to A$9.8 million for 2015, according to a statement this January from the state’s Attorney General and Minister for Commerce, Michael Mischin. In addition, the minister noted that losses from relationship and dating fraud have fallen by 55%, to A$4.9 million lost last year. These are both impressive claims, and at face value, there is truth to the statistics. Both assertions are based on data received by WA’s Scamnet, which is the public interface between consumer protection and citizens. While it is good to see a reduction in the number of losses overall, particularly to relationship and dating fraud, it is highly unlikely that the statistics tell the full story.

Surface resistance and residual losses of Ag?doped YBa2Cu3O7?? thin films on sapphire

High?quality Ag?doped YBa2Cu3O7?? thin films have been grown by laser ablation on R?plane ?1102? sapphire without any buffer layer. Thin films have been found to be highly c?axis oriented with Tc=90 K, transition width ?T?1 K, and transport Jc=1.2×106 A?cm?2 at 77 K in self?field conditions. The microwave surface resistance of these films measured on patterned microstrip resonators has been found to be 530 ?? at 10 GHz at 77 K which is the lowest reported on unbuffered sapphire. Improved in?plane epitaxy and reduced reaction rate between the substrate and the film caused due to Ag in the film are believed to be responsible for this greatly improved microwave surface resistance. © 1995 American Institute of Physics.

Structured Learning for Non-Smooth Ranking losses

Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP(mean average precision). We propose new, almost-lineartime algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain)in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization.The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.

Evaluation of mechanical losses in a linear motor pressure wave generator

A moving magnet linear motor compressor or pressure wave generator (PWG) of 2 cc swept volume with dual opposed piston configuration has been developed to operate miniature pulse tube coolers. Prelimnary experiments yielded only a no-load cold end temperature of 180 K. Auxiliary tests and the interpretation of detailed modeling of a PWG suggest that much of the PV power has been lost in the form of blow-by at piston seals due to large and non-optimum clearance seal gap between piston and cylinder. The results of experimental parameters simulated using Sage provide the optimum seal gap value for maximizing the delivered PV power.

Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

Assessment of post-harvest losses in Nigeria fishery: the Kainji Lake model

The paper provides a description of a methodology used for quantitative assessment of post harvest losses in the Kainji Lake Fishery (Nigeria). The sample population was made up of 314 fisherfolk, 115 processors, 125 fish buyers and 111 fish sellers. For the determination of handling losses, 24,839 fishes weighing 2,389.31 kg belonging to 43 species were examined of which 10% by number and 9% by weight deteriorated at checking and 4% by number and 3% by weight at landing. Processing losses recorded 22% by number and 16% by weight deteriorated prior to and during smoking with the traditional 'Banda' kiln. During marketing, 16% of fish sold had deteriorated and 6% by weight of fish bought also deteriorated, mainly due to insect infestation during storage. Based on the 1995 yield estimate for Kainji Lake fishery, approximately 1000 tons of fish estimated at 80 million Naira were lost during handling alone. This figure would be much higher if the level of losses during processing and marketing are included. This assessment technique is recommended for use in obtaining quantifiable data on post harvest losses from other water bodies in Nigeria