The effect of simulated flooding post-anthesis on pre-harvest sprouting and subsequent seed longevity in contrasting rice cultivars

Unpredictable flooding is a major constraint to rice production. It can occur at any growth stage. The effect of simulated flooding post-anthesis on yield and subsequent seed quality of pot-grown rice (Oryza sativa L.) plants was investigated in glasshouses and controlled-environment growth cabinets. Submergence post-anthesis (9-40 DAA) for 3 or 5 days reduced seed weight of japonica rice cv. Gleva, with considerable pre-harvest sprouting (up to 53%). The latter was greater the later in seed development and maturation that flooding occurred. Sprouted seed had poor ability to survive desiccation or germinate normally upon rehydration, whereas the effects of flooding on the subsequent air-dry seed storage longevity (p50) of the non-sprouted seed fraction was negligible. The indica rice cvs IR64 and IR64Sub1 (introgression of submergence tolerance gene Submergence1A-1) were both far more tolerant to flooding post-anthesis than cv. Gleva: four days’ submergence of these two near-isogenic cultivars at 10-40 DAA resulted less than 1% sprouted seeds. The presence of the Sub1A-1 allele in cv. IR64Sub1 was verified by gel electrophoresis and DNA sequencing. It had no harmful effect on loss in seed viability during storage compared with IR64 in both control and flooded environments. Moreover, the germinability and changes in dormancy during seed development and maturation were very similar to IR64. The efficiency of using chemical spray to increase seed dormancy was investigated in the pre-harvest sprouting susceptible rice cv. Gleva. Foliar application of molybdenum at 100 mg L-1 reduced sprouted seeds by 15-21% following 4 days’ submergence at 20-30 DAA. Analyses confirmed that the treatment did result in molybdenum uptake by the plants, and also tended to increase seed abscisic acid concentration. The latter was reduced by submergence and declined exponentially during grain ripening. The selection of submergence-tolerant varieties was more successful than application of molybdenum in reducing pre-harvest sprouting.

Zero attracting recursive least squares algorithms

The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Sparse density estimation on multinomial manifold combining local component analysis

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes. Speci�cally, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of people's dedication. This new problem is named the Multiple Team Formation Problem (MTFP). Second, an optimization model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimization model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most effi�cient in almost all cases. Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research.