N-[(5-Chloro-3-methyl-1-phenyl-1H-pyrazol-4-yl)carbon­yl]-N′-(4-hydroxy­phen­yl)thio­urea

In the title compound, C18H15ClN4O2S, the pyrazole ring makes dihedral angles of 67.4 (1) and 12.5 (1)° with the phenyl and 4-hydroxy­phenyl groups, respectively; the two benzene rings are twisted by 60.1 (1)° with respect to each other. The thio­urea NH groups are involved in N—H⋯O and N—H⋯Cl intra­molecular hydrogen bonds. A hydrogen bond between the phenolic OH group and the pyrazole N atom connects mol­ecules into a one-dimensional polymeric structure

Electronic structure and Magnetism in BaMn2_2As2_2 and BaMn2_2Sb2_2

We study the properties of ThCr$_2$Si$_2$ structure BaMn$_2$As$_2$ and BaMn$_2$Sb$_2$ using density functional calculations of the electronic and magnetic as well experimental measurements on single crystal samples of BaMn$_2$As$_2$. These materials are local moment magnets with moderate band gap antiferromagnetic semiconducting ground states. The electronic structures show substantial Mn - pnictogen hybridization, which stabilizes an intermediate spin configuration for the nominally $d^5$ Mn. The results are discussed in the context of possible thermoelectric applications and the relationship with the corresponding iron / cobalt / nickel compounds Ba(Fe,Co,Ni)$_2$As$_2$

Phytoextraction of phosphorus for ecological restoration: application of soil additives

The European Habitats Directive urges the European member states to take measures for maintaining and restoring natural habitats. In Flanders (Belgium) and the Netherlands, the surface area of nature reserves is intended to be enlarged with 38 000 ha and 150 000 ha, respectively, what is mainly to be realised on former agricultural land. In order to restore species rich nature habitats on former agricultural land, it is crucial to decrease the availability of nutrients and a limitation for plant growth by at least one nutrient should be ensured. The former fertilization of P in the agricultural context results in an immense P pool fixated to the soil and this is one of the main problems hindering the ecological restoration. We focus on an alternative restoration method, the phytoextraction of P, also P-mining. This is the deprivation of soil P with a crop with high P-use efficiency and non-P fertilization. This method allows the gradual transition from agricultural land use towards nature management. Up until now there have only been estimations of the P-mining duration time from the initial phase of the mining-process. In order to estimate the P-extraction over time the experiments take place on a soil-P-chronosequence. A controlled pot experiment was set up with soil from three former agricultural sites with different soil-P-levels, Lolium perenne was sown and chemical and biological compounds were added to enhance the bioavailability of P for plant-uptake. The additives used were two concentrations of humic acids, phosphorus solubilising bacteria and arbuscular mycorrhizal fungi. Largest effects of the soil additions on the biomass production were measured in the lowest soil-P-level. Limitation by P in the Mid and Low P soils was very pronounced. The phytoextraction of P will slow down with soil P level decreasing in time. The effect of the soil additions is discussed

A Convolutional Neural Network Model based on Multiscale Structural Similarity for the Prediction of Flow Fields

We have seen the emerging applications of deep neural networks for flow field predictions in the past few years. Most of the efforts rely on the increased complexity of the model itself or take advantage of novel network architectures, such as convolutional neural networks (CNN). However, reaching low prediction error cannot guarantee the quality of the predicted flow fields in terms of the perceived visual quality. This work introduces the multi-scale structural similarity (MS-SSIM) index method for flow field prediction. First, we train CNN models using the commonly used root mean squared error (RMSE) loss function as the reference. Then we introduce the SSIM loss function to capture the high-level features. Furthermore, we investigate the effects of the MS-SSIM weights on the predictive performance. Our results show that while the pixel-wise prediction error of RMSE-based models is as low as 1.3141 x 10−2, the perceived visual quality of the predicted flow fields, such as contour-line smoothness, is poorly represented. In contrast, the MS-SSIM models significantly improve the perceived visual quality with an SSIM loss value as low as 7.370 x 10−3, although having a slightly higher prediction error of 1.3912x10−2 . These values are 41.7% lower in the SSIM loss and 5.9% higher in the RMSE than the best RMSE model. In particular, we report that a weight combination of 0.3 and 0.7 for the MS-SSIM loss function provides the best predictive performance in our case. Our study has pointed out a possible future endeavor to invent a quality metric based on structural similarity, which should excel in flow-field-related approximations