Mapping quantitative trait loci for flag leave senescence as a yield determinant in winter wheat under optimal and drought stressed environments

The timing of flag leaf senescence (FLS) is an important determinant of yield under stress and optimal environments. A doubled haploid population derived from crossing the photo period-sensitive variety Beaver,with the photo period-insensitive variety Soissons, varied significantly for this trait, measured as the percent green flag leaf area remaining at 14 days and 35 days after anthesis. This trait also showed a significantly positive correlation with yield under variable environmental regimes. QTL analysis based on a genetic map derived from 48 doubled haploid lines using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers, revealed the genetic control of this trait. The coincidence of QTL for senescence on chromosomes 2B and 2D under drought-stressed and optimal environments, respectively, indicate a complex genetic mechanism of this trait involving the re-mobilisation of resources from the source to the sink during senescence.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

Hälsofrämjande arbete på SSAB i Borlänge : Hälsofrämjande processer i arbetslivet och ledarskapets betydelse för medarbetarens hälsa

Problem: Arbetsmiljön är viktig för människors välbefinnande. Hälsofrämjande faktorer antas inte bara styrka individens fysiska och psykiska hälsa, utan även företagets konkurrenskraft och lönsamhet. I uppsatsen undersöker vi hur företaget SSAB i Borlänge arbetar med hälsa genom att tillämpa ett hälsofrämjande perspektiv och utgå från teorin om Känslan av sammanhang (KASAM). Ledarskapsvärderingar har betydelse för medarbetares hälsa (Hanson, 2004) och vi undersöker vilken uppfattning ett antal chefer har om sina roller i det hälsofrämjande arbetet och deras syn på medarbetarundersökningen HälsoSAM som företagshälsovården på SSAB bedriver. Arbetsbelastningen på medarbetare och chefer ökar till följd av sparkrav, samtidigt som resurserna minskar (Gatu, 2003). Följden blir en större risk för ohälsa och ett sätt att minska sjukfrånvaron är att genomföra hälsofrämjande insatser (Prevent, 2001). Syfte: Syftet med studien är att utifrån ett hälsofrämjande perspektiv förklara vad chefer har för möjligheter att skapa förutsättningar för att främja medarbetarnas hälsa på SSAB i Borlänge. Metod: Den metod som ligger till grund för uppsatsen baseras på ett kvalitativt angreppssätt där semistrukturerade intervjuer samt litteraturstudier genomförts för att samla information. Analys: Ledarens beteende påverkar medarbetarna. Hög arbetsbelastning på SSABs chefer leder till lägre närvaro bland medarbetarna, vilket i sin tur ger sämre förutsättningar för att främja hälsan. HälsoSAM kartlägger medarbetarnas hälsoläge, både välbefinnande och arbetskapacitet. Resultaten är vägledande i det hälsofrämjande arbetet men överbelastning, tidsbrist och kunskapsbrist hos cheferna bidrar till att uppföljningen inte blir systematisk. För ett väl fungerande hälsoarbete krävs systematik. Slutsats: Hälsofrämjande processer i arbetslivet skapas genom balans mellan krav och resurser. På SSAB i Borlänge hindras hälsoarbetet av tidsbrist och kunskapsbrist hos cheferna. Kostnadsbesparingar påverkar hälsoarbetet negativt och systematiken i uppföljningsarbetet blir lidande. Ansvaret för hälsoarbetet läggs på företagshälsovården, men utan stöd från chefer blir inte hälsoarbetet en naturlig del i verksamheten. Det är viktigt att i rådande situation se vad främjande av hälsa kan ge tillbaka till företaget i både ekonomiska och kvalitativa termer. Insikt om detta ökar chefernas incitament att prioritera hälsofrämjande processer och organisera arbetet så att människor har förutsättningar att hantera, kontrollera och klara av sina uppgifter.

Engaging Faculty in Online Education: Rightsizing Incentives and Optimizing Support

: Colleges and universities of all types are pursuing increasingly ambitious goals for online education for a range of reasons—enhancing learning, increasing access, growing enrollment, managing costs. However, concerns about workload, support resources, autonomy, and course quality leave many faculty skeptical of online instruction, and most institutions expanding online offerings are struggling to get sufficient numbers of faculty both willing and prepared to teach online. This study presents best practices in managing the strategic and operational challenges associated with increasing the number of fully online and hybrid courses

The workload and plasma ion concentration in a training match session of high-goal (elite) polo ponies

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Creatine does not promote hypertrophy in skeletal muscle in supplemented compared with nonsupplemented rats subjected to a similar workload

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)