Measurement and modelling of solubility for calcium sulfate dihydrate and calcium hydroxide in NaOH/KOH solutions

The solubility of calcium sulfate dihydrate (CaSO(4)center dot 2H(2)O) and calcium hydroxide (Ca(OH)(2)) in alkali solutions is essential to understand their desilication behavior from Bayer liquor. In this work, solubilities of calcium sulfate dihydrate and calcium hydroxide for the ternary systems of CaSO(4)center dot 2H(2)O-NaOH-H(2)O, CaSO(4)center dot 2H(2)O-KOH-H(2)O, and Ca(OH)(2)-NaOH-H(2)O were measured by using the classic isothermal dissolution method over the temperature range of 25-75 degrees C. The Pitzer model embedded in Aspen Plus platform was used to model the experimental solubility data for these systems. The experimental solubility data was employed to obtain the new binary interaction parameters for Ca(OH)(+)-OH(-), Ca(OH)(+)-Ca(2+) and Ca(OH)(+)-K(+), suggesting that the species Ca(OH)(+) is a dominant species in simulated solubility for alkali systems. Validation of the parameters was performed by predicting the solubility for the ternary systems of Ca(OH)(2)-NaOH-H(2)O, CaSO(4)center dot 2H(2)O-NaOH-H(2)O and CaSO(4)center dot 2H(2)O-KOH-H(2)O with the overall average relatively deviation (ARD) of 2.12%, 0.75% and 1.63%, respectively. (C) 2010 Elsevier B.V. All rights reserved

InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting

Long-term time series forecasting (LTSF) provides substantial benefits for numerous real-world applications, whereas places essential demands on the model capacity to capture long-range dependencies. Recent Transformer-based models have significantly improved LTSF performance. It is worth noting that Transformer with the self-attention mechanism was originally proposed to model language sequences whose tokens (i.e., words) are discrete and highly semantic. However, unlike language sequences, most time series are sequential and continuous numeric points. Time steps with temporal redundancy are weakly semantic, and only leveraging time-domain tokens is hard to depict the overall properties of time series (e.g., the overall trend and periodic variations). To address these problems, we propose a novel Transformer-based forecasting model named InParformer with an Interactive Parallel Attention (InPar Attention) mechanism. The InPar Attention is proposed to learn long-range dependencies comprehensively in both frequency and time domains. To improve its learning capacity and efficiency, we further design several mechanisms, including query selection, key-value pair compression, and recombination. Moreover, InParformer is constructed with evolutionary seasonal-trend decomposition modules to enhance intricate temporal pattern extraction. Extensive experiments on six real-world benchmarks show that InParformer outperforms the state-of-the-art forecasting Transformers

Microstructure and Mechanical Properties of a Combination Interface between Direct Energy Deposition and Selective Laser Melted Al-Mg-Sc-Zr Alloy

Selective laser melting (SLM) and direct energy deposition (DED) are two widely used technologies in additive manufacturing (AM). However, there are few studies on the combination of the two technologies, which can synthetically combine the advantages of the two technologies for more flexible material design. This paper systematically studies the Al-Mg-Sc-Zr alloy by combination of SLM and DED with emphasis on its bonding properties, microstructure, and metallurgical defects. It is found that the aluminum alloy prepared by the two methods achieves a good metallurgical combination. The microstructure of aluminum alloy prepared by DED is composed of equiaxed crystals, and there are a large number of Al3(Sc, Zr) precipitated phase particles rich in Sc and Zr. The microstructure of SLM aluminum alloy is composed of equiaxed crystals and columnar crystals, and there is a fine-grained area at the boundary of the molten pool. With the decrease of laser volumetric energy density (VED), the width and depth of the molten pool at the interface junction gradually decrease. The porosity gradually increases with the decrease of VED, and the microhardness shows a downward trend. Tensile strength and elongation at fracture of the SLM printed sample at 133.3 J/mm3 are about 400 MPa and 9.4%, while the direct energy depositioned sample are about 280 MPa and 5.9%. Due to the excellent bonding performance, this research has certain guiding significance for SLM–DED composite aluminum alloy

Design crystallographic ordering in NbTa0.5TiAlx refractory high entropy alloys with strength-plasticity synergy

To make up for the poor strength of high plasticity NbTa0·5Ti refractory medium entropy alloys (MEAs), light metal Al was introduced as alloying element. In this work, the NbTa0·5Ti-Alx (x = 0, 0.2, 0.4, 0.6, 0.8, 1.0) series non-isoatomic refractory high entropy alloys (RHEAs) were prepared by arc melting, and phase equilibrium was predicted by CALPHAD. The effects of Al content and annealing temperature on microstructure and phase evolution, and its mechanical properties were studied. The NbTa0·5Ti-Alx alloys changed from single phase BCC to two-phase A2+B2 crystal structure after adding Al. The hardness and strength of the as-cast alloys are increased by the solution strengthening and precipitation strengthening effect, but the brittleness is increased. The precipitation of Laves of plate-like NbAlTi2 and finer-scale A15 of (needle, particle)-like AlTi3 precipitates at and near grain boundaries after annealing. Higher annealing temperature is beneficial to eliminate dendrites formed by element segregation in the arc melting cooling process and promote grain growth (up to ∼200 μm). This work designed a new alloys with excellent compression plasticity and enriched the field of composition design and aging treatment of the Al-containing second generation RHEAs， so that their microstructure can be better controlled to achieve a balance of strength and plasticity

Primary Investigation of Phenotypic Plasticity in <i>Fritillaria cirrhosa</i> Based on Metabolome and Transcriptome Analyses

Phenotypic plasticity refers to the adaptability of an organism to a heterogeneous environment. In this study, the differential gene expression and compositional changes in Fritillaria cirrhosa during phenotypic plasticity were evaluated using transcriptomic and metabolomic analyses. The annotation profiles of 1696 differentially expressed genes from the transcriptome between abnormal and normal phenotypes revealed that the main annotation pathways were related to the biosynthesis of amino acids, ABC transporters, and plant–pathogen interactions. According to the metabolome, the abnormal phenotype had 36 upregulated amino acids, including tryptophan, proline, and valine, which had a 3.77-fold higher relative content than the normal phenotype. However, saccharides and vitamins were found to be deficient in the abnormal phenotypes. The combination profiles demonstrated that phenotypic plasticity may be an effective strategy for overcoming potential stress via the accumulation of amino acids and regulation of the corresponding genes and transcription factors. In conclusion, a pathogen attack on F. cirrhosa may promote the synthesis of numerous amino acids and transport them into the bulbs through ABC transporters, which may further result in phenotypic variation. Our results provide new insights into the potential mechanism of phenotypic changes

Dual-Encoder Transformer for Short-Term Photovoltaic Power Prediction Using Satellite Remote-Sensing Data

The penetration of photovoltaic (PV) energy has gained a significant increase in recent years because of its sustainable and clean characteristics. However, the uncertainty of PV power affected by variable weather poses challenges to an accurate short-term prediction, which is crucial for reliable power system operation. Existing methods focus on coupling satellite images with ground measurements to extract features using deep neural networks. However, a flexible predictive framework capable of handling these two data structures is still not well developed. The spatial and temporal features are merely concatenated and passed to the following layer of a neural network, which is incapable of utilizing the correlation between them. Therefore, we propose a novel dual-encoder transformer (DualET) for short-term PV power prediction. The dual encoders contain wavelet transform and series decomposition blocks to extract informative features from image and sequence data, respectively. Moreover, we propose a cross-domain attention module to learn the correlation between the temporal features and cloud information and modify the attention modules with the spare form and Fourier transform to improve their performance. The experiments on real-world datasets, including PV station data and satellite images, show that our model achieves better results than other models for short-term PV power prediction

Unconventional precipitation and martensitic transformation behaviour of Ni-rich NiTi alloy fabricated via laser-directed energy deposition

In this study, we report an unconventional precipitation and martensitic transformation behaviour of directly aged Ni-rich NiTi alloys fabricated via laser-directed energy deposition (LDED). Ni4Ti3 particles precipitate uniformly under all ageing conditions and no traditional multiple-step martensitic transformations are observed. We conclude this unique behaviour to the intrinsic characteristics of the LDED technique, which are metastable microstructures and high residual stresses. On the one hand, these features make grain boundaries no longer a fevered location for precipitation and, on the other hand, significantly suppress the martensitic transformation when ageing at low temperatures (300°C/400°C). As the aging temperature increase (500°C), residual stresses release significantly, accompanied by the growth of Ni4Ti3 precipitates from several nanometres to 452 ± 181 nm with increased interparticle spacing. At the same time, reverse martensitic transformations change from two-step (B19′ → R → B2) to single-step (B19′ → B2)

Protecting Mobile Livelihoods: Actors’ Responses to the Emerging Health Challenges in Beijing and Tianjin

Drawing on extensive fieldwork in Beijing and Tianjin, and applying a livelihood framework combined with a well-being perspective, this article examines an important aspect of rural–urban migrants’ social protection in China, namely migrants’ health, in particular work safety and occupational health. It argues that migrant workers’ social rights to health and livelihoods are a fiercely contested domain of citizenship entailing aspects of exclusion, inclusion, and control and allocation of economic, social, and political resources. The article shows that in spite of the accelerated pace of legislation and consolidated efforts to reconstruct the welfare system in China in recent years, the new social security schemes have thus far, by and large, failed to protect migrant workers in a systematic manner. The issues raised in the article therefore call for greater academic attention and more effective public policy responses

Oxygen Vacancy-Reinforced Water-Assisted Proton Hopping for Enhanced Catalytic Hydrogenation

Water-assisted proton hopping (WAPH) has been intensively investigated for promoting the performance of metal oxide-supported catalysts for hydrogenation. However, the effects of the structure of the metal oxide support on WAPH have received little attention. Herein, we construct oxygen vacancy-bearing, MoO3–x-supported Pd nanoparticle catalysts (Pd/MoO3–x-R), where the oxygen vacancies can promote WAPH, thereby facilitating catalytic hydrogenation. The experimental results and theoretical calculations show that the oxygen vacancies favor the adsorption of water, which assists the proton hopping across the surface of the metal oxide, enhancing the catalytic hydrogenation. Our finding will provide a potential approach to the design of metal oxide-supported catalysts for hydrogenation