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Number of results: 5
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Abstract

Nine phyto-ashes from the biomass combustion of birch (Betula), oak (Quercus), red oak (Quercus rubra), horbeam (Carpinus), pine (Pinus sylvestris), poplar (Populus), maple (Acer), oilseed rape straw (Brassica napus) and wheat straw (Triticum aestivum) were blended with a biogas digestate at 1:1 mass ratio to give nine organic-mineral soil improvers. The concept of the research was to outline an eco-friendly and low cost soil improver for remediating degraded lands. These (i.e. phyto-ashes, improvers and the biogas digestate) were applied (0, 5, 10, 20, 40 t·ha-1) to a soil metallurgically contaminated with Cu, Zn, Pb and Cd. Of several tested parameters, pH changes revealed that organic-mineral soil improvers may efficiently replace (linear R2>0.90****, P<0.001) phyto-ashes in soil remedial goals. Buffering properties expressed by the cation exchange capacity (CEC) improved progressively: 29, 52, 71, 100% (phyto-ash treatments) and: 18, 37, 44, 73% (improvers treatments) for the rates 5, 10, 20, 40 t·ha-1, respectively as referred to the control CEC. The Dynamic Remediation Efficiency (DRE) indices for Cu, Zn, Pb, Cd revealed metal-specific geochemical reactions initiated by phyto-ashes, improvers and biogas digestate. The rates 5.0–10.0 t·ha-1 for phyto-ashes and about 20 t·ha-1 for improvers [1:1, i.e. Phyto-ash:Biogas digestate] are recommended. For biogas digestate, the rates 10–20 t·ha-1 were found more efficient.
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Abstract

Scientists around the world agree that nowadays, science is facing severe challenges like poor peer-review system, replicability crisis, or locked science behind paywalls. The National Science Center addresses at least some of them by introducing procedures that promote integrity, ethics, social responsibility, transparency, and openness in science.
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Abstract

A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
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Abstract

There is a general agreement that remembering depends not only on the memory processes as such but rather that encoding, storage and retrieval are under the constant influence of the overarching, metacognitive processes. Moreover, many interventions designed to improve memory refer in fact to metacognition. Most attempts to integrate the very different theoretical and experimental approaches in this domain focus on encoding, whereas there is relatively little integration of approaches that focus on retrieval. Therefore, we reviewed the studies that used new ideas to improve memory retrieval due to a “metacognitive intervention”. We concluded that whereas single experimental manipulations were not likely to increase metacognitive ability, more extensive interventions were. We proposed possible theoretical perspectives, namely the Source Monitoring Framework, as a means to integrate the two, so far separate, ways of thinking about the role of metacognition in retrieval: the model of strategic regulation of memory, and the research on appraisals in autobiographical memory. We identified venues for future research which could address, among other issues, integration of these perspectives.
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Abstract

Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.
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