Interval Methods in Knowledge Representation

Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.

Effect of Foliar Spraying of Organic and Inorganic Selenium Fertilizers On Selenium Accumulation and Speciation During Different Stages of Rice Growth

Aims Most crops are supplemented with selenium (Se) through the exogenous addition of inorganic selenium fertilizer. There is a lack of in-depth research on organic selenium fertilizers. Meanwhile, the dosage range between human selenium deficiency and selenium toxicity is narrow, so the selenium content of agricultural products needs to be controlled within a reasonable interval. Methods W e analyzed and compared the Se accumulation and speciation in rice during three different growth stages (late tillering stage, initiate heading stage, and full heading stage) using three selenium fertilizers, selenite, fermented Se, and potassium Selenocyanoacetate (Se-AAF) via the foliar application. Results The selenium content in rice sprayed with organic selenium fertilizer was controlled in the relatively safe range and met the human selenium supplement requirement compared to the sprayed sodium selenite, which was too high of a dose. The percentage of organic Se and protein Se in brown rice was found to be similar in all three Se fertilizers. The highest organic selenium content of 91.57% was found in the grain of rice at the full heading stage by spraying Se-AAF. The main Se species in the grain was selenomethionine (SeMet), which reached 80% of the total selenium. Se-methyl selenocysteine(SeMeCys) was found only in Se-AAF. The grain quality showed that all three Se fertilizers increased the consistency of gelatinization. Conclusions Appropriately delaying the spraying time and selecting organic Se fertilizer as the Se source can help to produce green and safe selenium-rich rice.

Interval Methods in Knowledge Representation

Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA…

Improved interval methods for solving circle packing problems in the unit square

In this work computer-assisted optimality proofs are given for the problems of finding the densest packings of 31, 32, and 33 non-overlapping equal circles in a square. In a study of 2005, a fully interval arithmetic based global optimization method was introduced for the problem class, solving the cases 28, 29, 30. Until now, these were the largest problem instances solved on a computer. Using the techniques of that paper, the estimated solution time for the next three cases would have been 3–6 CPU months. In the present paper this former method is improved in both its local and global search phases. We discuss a new interval-based polygon representation of the core local method for eliminating suboptimal regions, which has a simpler implementation, easier proof of correctness, and faster behaviour than the former one. Furthermore, a modified strategy is presented for the global phase of the search, including improved symmetry filtering and tile pattern matching. With the new method the cases $$n=31,32,33$$ n = 31 , 32 , 33 have been solved in 26, 61, and 13 CPU hours, giving high precision enclosures for all global optimizers and the optimum value. After eliminating the hardware and compiler improvements since the former study, the new proof technique became roughly about 40–100 times faster than the previous one. In addition, the new implementation is suitable for solving the next few circle packing instances with similar computational effort.

Exclusion regions for parameter-dependent systems of equations

This paper presents a new algorithm based on interval methods for rigorously constructing inner estimates of feasible parameter regions together with enclosures of the solution set for parameter-dependent systems of nonlinear equations in low (parameter) dimensions. The proposed method allows to explicitly construct feasible parameter sets around a regular parameter value, and to rigorously enclose a particular solution curve (resp. manifold) by a union of inclusion regions, simultaneously. The method is based on the calculation of inclusion and exclusion regions for zeros of square nonlinear systems of equations. Starting from an approximate solution at a fixed set p of parameters, the new method provides an algorithmic concept on how to construct a box $${\mathbf {s}}$$ s around p such that for each element $$s\in {\mathbf {s}}$$ s ∈ s in the box the existence of a solution can be proved within certain error bounds.

A Monte Carlo Study of Confidence Interval Methods for Generalizability Coefficient

Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the coefficients. As generalizability theory can be framed to a linear mixed-effect model (LMM), bootstrap and simulation techniques from LMM paradigm can be used to construct the confidence intervals. The purpose of this research is to examine four different LMM-based methods for computing the confidence intervals that have been proposed and to determine their accuracy under six simulated conditions based on the type of test scores (normal, dichotomous, and polytomous data) and data measurement design ( p× i× r and p× [ i:r]). A bootstrap technique called “parametric methods with spherical random effects” consistently produced more accurate confidence intervals than the three other LMM-based methods. Furthermore, the selected technique was compared with model-based approach to investigate the performance at the levels of variance components via the second simulation study, where the numbers of examines, raters, and items were varied. We conclude with the recommendation generalizability coefficients, the confidence interval should accompany the point estimate.

Interval Methods in Knowledge Representation

Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA…

Confidence interval methods for antimicrobial resistance surveillance data

Background Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents. Methods We used AMR data from up to 381 laboratories in the United States from The Surveillance Network to evaluate methods for estimating uncertainty of AMR prevalence estimates. We constructed confidence intervals for the proportion of resistant isolates using (1) methods that account for the clustered structure of the data, and (2) standard methods that assume data independence. Using samples of the full dataset with increasing facility coverage levels, we examined how likely the estimated confidence intervals were to include the population mean. Results Methods constructing 95% confidence intervals while accounting for possible within-cluster correlations (Survey and standard methods adjusted to employ cluster-robust errors), were more likely to include the sample mean than standard methods (Logit, Wilson score and Jeffreys interval) operating under the assumption of independence. While increased geographical coverage improved the probability of encompassing the mean for all methods, large samples still did not compensate for the bias introduced from the violation of the data independence assumption. Conclusion General methods for estimating the confidence intervals of AMR rates that assume data are independent, are likely to produce biased results. When feasible, the clustered structure of the data and any possible intra-cluster variation should be accounted for when calculating confidence intervals around AMR estimates, in order to better capture the uncertainty of prevalence estimates.

Preface

The Summer Workshop on Interval Methods (SWIM) is an annual meeting initiated in 2008 by the French MEA working group on Set Computation and Interval Techniques of the French research group on Automatic Control. A special focus of the MEA group is on promoting interval analysis techniques and applications to a broader community of researchers, facilitated by such multidisciplinary workshops. Since 2008, SWIM has become a keystone event for researchers dealing with various aspects of interval and set-based methods. In 2019, the 12th edition in this workshop series was held at ENSTA Paris, France, with a total of 25 talks. Traditionally, workshops in the series of SWIM provide a platform for both theoretical and applied researchers who work on the development, implementation, and application of interval methods, verified numerics, and other related (set-membership) techniques.For this edition, given talks were in the fields of the verified solution of initial value problems for ordinary differential equations, differential-algebraic system models, and partial differential equations, scientific computing with guaranteed error bounds, the design of robust and fault-tolerant control systems, the implementation of corresponding software libraries, and the usage of the mentioned approaches for a large variety of system models in areas such as control engineering, data analysis, signal and image processing. Seven papers were selected for submission to this Acta Cybernetica special issue. After a two turn peer-review process, six high-quality articles were selected for publication in this special issue. Three papers propose a contribution regarding differential equations, two papers focus on robust control, and one paper considers fault detection.