The “second generation” of glyphosate-tolerant soybean (GT2 soybean) was developed through a different technique of insertion of the glyphosate-insensitive EPSPs gene, in comparison with “first generation” of glyphosate-tolerant soybean. However, there is not enough information available about glyphosate selectivity in GT2 soybean and the effects on the quality of seeds produced. The aim of this study was to evaluate tolerance to glyphosate and seed quality of soybean cultivar NS 6700 IPRO (GT2) with cp4-EPSPs and cry1Ac genes, after application at post-emergence (V4). The experiment was conducted in a randomized block design with four replicates and seven treatments, or rates of glyphosate (0; 720; 1,440; 2,160; 2,880; 3,600; 4,320 g of acid equivalent − a.e. · ha−1). Assessments were performed for crop injury, SPAD index and variables related to agronomic performance and seed quality. A complementary trial with the same cultivar and treatments in a greenhouse was conducted in a completely randomized design with four replications. Data analysis indicated no significant effect of glyphosate on V4 on agronomic performance and physiological quality of seeds, for two growing seasons. The soybean cultivar NS 6700 IPRO (GT2), with cp4-EPSPs and cry1Ac genes, was tolerant to glyphosate up to the maximum rate applied (4,320 g a.e. · ha−1) at post-emergence (V4). The quality of soybean seeds was not affected by glyphosate up to the maximum rate applied (4,320 g a.e. · ha−1) at post-emergence (V4).
When mineral processing separation results, for either constant or varying quality of the feed, can be approximated on the so-called Fuerstenau upgrading plots with the same one-fitting parameter a, then this parameter can be used as a selectivity indicator. If the equation has a form [...], where [...] stands for recovery of non-useful component in tailing while\ksi is the recovery of useful component in the concentrate, then at the same selectivity of upgrading (constant a) the increase of the useful component in the feed \alfa results in an increased amount of this component in both concentrate \beta and tailing [...] while the ratio of [...] to \beta is linearly dependent on \alfa when \epsilon is constant. Thus, at a constant selectivity a and constant \epsilon an increase in \alfa leads to a greater increase of the considered component in the tailing \theta than in the concentrate \beta.
The problem of query selectivity estimation for database queries is critical for efficientquery execution by database management systems. A query execution method strongly depends on earlyestimated size of a query result. This estimation determines a data access method used later during thequery execution. The selectivity parameter is a fraction of table rows that satisfy a single-table querycondition. For a selection condition of a range query where an attribute has a continuous domain, theselectivity is equivalent to a definite integral form probability density function (PDF) of attribute valuesdistribution. For a compound selection condition based on many attributes we need a multidimensionalspace-efficient non-parametric estimator of multivariate PDF of attribute values distribution. A knownapproach based on Discrete Cosine Transform (DCT) spectrum as an representation of multidimensionalPDF is considered. The energy compaction property of DCT lets omit a region of spectrum coefficientswith small absolute values without significant losing an accuracy of selectivity estimation. An area ofrelevant spectrum coefficients is called a sampling zone. Results of experiments from previous worksshows that applying the reciprocal shape of the sampling zone gives the least selectivity estimation errorsubject to a predetermined size of the zone. The main result of this work is a theoretical confirmation of onlyexperimental results from previous works. The paper presents the proof of the theorem that the reciprocalshape of the sampling zone is asymptotically error-optimal. The proof is based on calculus of variationsand the isoperimetric problem.
Semiconductive - resistive sensors of toxic and explosive gases were fabricated from nanograins of SnO2 using thick-.lm technology. Sensitivity, selectivityand stabilityof sensors working in di.erent temperature depend on the way the tin dioxide and additives were prepared. A construction also plays an important role. The paper presents an attitude towards the evaluation of transport of electrical charges in semiconductive grain layer of SnO2, when dangerous gases appear in the surrounding atmosphere.
The efficient, stable and reliable operation of the blast furnace secures the proper quality of coke, which is one of the basic components of the blast furnace charge. In modern blast-furnace technology, when using substitute fuels, i.e. coal dust, the role of coke is extremely important. For this reason, the demands placed on its quality increase. Domestic coking plants have a limited base of Polish high quality coking coals at their disposal, therefore the full use of their coking properties is extremely important. The grain composition of the coal blend is one of the basic factors affecting the quality of the produced coke. This influence depends on the quantity and quality of coal components that make up the blend. In the conducted research, 21 coking coals, differing significantly in the degree of rank and origin (Polish and overseas coals), it was shown that the separated grain classes differ in properties, both coking properties and the degree of devolatalization during heating. In analyzing the obtained results, it was observed that the grain volume growth occurs essentially in the temperature range between the beginning and the maximum of fluidity. It has been shown that there is a linear correlation between the temperature corresponding to maximum fluidity and the temperature at which the maximum rate of evolution of volatiles enters. The presented phenomena accompany the emergence of coal expansion pressure during the coking process and they are its primary causes. The presented results can be an important guide for preparing the milling of coal for the coking process.
Similarly to many towns in Galicia, Rzeszów has gained street planting at the turn of the 19th and 20th centuries. In the period after World War II, little attention was paid to them. It is only from the beginning of the first decade of the 21st century, that we have seen a clear breakthrough. “Modernized” forms of planting are returning to historical places, and new communication arteries are planted with numerous specimens of tree, perennial and seasonal plants, well selected in terms of habitat requirements.
The paper presents the experience of using the ŁPrP, ŁPKO, ŁPSp, ŁPSpA i ŁPSp3R types of flattened supports for longwall entries in the conditions of the JSW S.A. Knurów-Szczygłowice coal mine. The article concentrates on the support solutions applied in the conditions of the mine and the results in terms of stability and usefulness of the structures of the supports. An analysis of the load bearing capacity and technological conditions has been conducted for various flattened supports solutions, with special consideration given to the ŁPSp and ŁPrPJ support sets. Comparing these two, the ŁPSp exhibits a load bearing capacity that is 21% higher while using 31% less steel mass. The experiment results allowed to determine that the ŁPSpA and ŁPSp3R support types are an advantageous solutions in case of longwall set-up rooms.
Machine learning (ML) methods facilitate automated data mining. The authors compare the effectiveness of selected ML methods (RBF networks, Kohonen networks, and random forest) as modelling tools supporting the selection of materials in ecodesign. Applied in the design process, ML methods help benefit from the knowledge, experience and creativity of designers stored in historical data in databases. Implemented into a decision support system, the knowledge can be utilized – in the case under analysis – in the process of design of environmentally friendly products. The study was initiated with an analysis of input data for the selection of materials. The input data, specified in cooperation with designers, include both technological and environmental parameters which guarantee the desired compatibility of materials. Next, models were developed using selected ML methods. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models supporting the selection of materials, connections and disassembly methods help boost the recycling properties of designed products.
The binary classifiers are appropriate for classification problems with two class labels. For multi-class problems, decomposition techniques, like one-vs-one strategy, are used because they allow the use of binary classifiers. The ensemble selection, on the other hand, is one of the most studied topics in multiple classifier systems because a selected subset of base classifiers may perform better than the whole set of base classifiers. Thus, we propose a novel concept of the dynamic ensemble selection based on values of the score function used in the one-vs-one decomposition scheme. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The proposed approach is compared with the static ensemble selection method based on the integration of base classifiers in geometric space, which also uses the one-vs-one decomposition scheme. In addition, other base classification algorithms are used to compare results in the conducted experiments. The obtained results demonstrate the effectiveness of our approach.
This article proposes a model describing the nature of associative processes as diagnostic cues for formulating attitudes and judgments. The assumption of the model is that attitudes, judgments and behaviours are based on how people selectively activate, interpret and integrate previously associated signals (selectively limiting the excess of information from both the senses and from our immediate environment). The model specifies which factors hinder or facilitate the formulation of associations between diagnostic signals and how it translates into attitudes, judgments and behaviours. To test the predictions derived from this model, we first showed that linguistic cues of diminutives can indicate physical properties – they were associated with the belief that the described objects were smaller but also worse or less valuable. The second line of research dealt with embodied moral judgments – we demonstrated that the usage of a hand over heart gesture led to more honest behaviour, an increase in judgments of honesty but also reduced tendency to lie for one's own profit. Our findings also suggest that using “standing at attention” body manipulation increased participants' submissiveness to the experimenter and their obedience to norms. This pattern of results suggests that the described model integrates perspectives of embodied cognition and social cognition, documenting the cognitive mechanism needed to formulate and adjust attitudes and judgments.