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BACKGROUND Monazite is a common rare earth mineral and an important ore mineral in many rare earth deposits, while sulfur-containing monazite is relatively rare and is only found in a few carbonate rocks, kimberlites, and metamorphic rocks. OBJECTIVES To accurately analyze the chemical composition of S-enriched monazite, and to infer its genesis. METHODS The main chemical components of S-enriched monazite from the REE ores in the Baiyanggou area of Puchun County, Dabie orogenic belt were determined by polarized light microscopy and electron probe microanalysis. RESULTS The sulfur-enriched monazite in the rare-earth mineralized amphibole schist in the study area was mostly colloidal and agglomerate, some were plate-like and columnar euhedral crystals, which were granular aggregates. There was mutual interaction, inclusion, and a semi-inclusion spatial relationship between sulfur-enriched monazite and apatite. Crosscut of apatite by network veins was also present. Petrography showed that there was a metasomatism similar to hydrothermal alteration between sulfur-enriched monazite and apatite. The SO3 content of S-enriched monazite in the rare earth-enriched mineral samples was as high as 14.57%, with an average content of 10.54%, which was the monazite with the highest S content. The S-enriched monazite has a higher CaO content and a low NdO content, which was quite different from the composition of the granite genesis monazite and hydrothermal metasomatic sulfur-deficient monazite. According to the negative correlation between S6+ and P5+, as well as the negative correlation between (Sr, Ca)2+, S6+ and REE3+, P5+, the S-containing monazatite can be explained by the "anhydrite coupling" displacement reaction, that is, (Sr, Ca)2++S6+↔REE3++P5+. This indicated that hydrothermal activity related to REE mineralization in the Baiyanggou area was present. CONCLUSIONS Combined with the geological background of the Baiyanggou area, it is inferred that the source of the hydrothermal fluid may be related to the intense lithospheric extensional movement and the late magmatic activity in the study area since the Cretaceous period. This provides new clues for research on the origin of rare earth polymetallic mineralization in this area.

In order to achieve low-carbon optimization in the intelligent mix ratio design of concrete materials, this work first constructs a concrete mix ratio database and performs a statistical characteristics analysis. Secondly, it employs a standard back propagation (BP) and a genetic algorithm-improved BP (GA-BP) to predict the concrete mix ratio. The NSGA-II algorithm is then used to optimize the mix ratio. Finally, the method's accuracy is validated through experiments. The study's results indicate that the statistical characteristics of the concrete mix ratio data show a wide distribution range and good representativeness. Compared to the standard BP, the fitting accuracies of each GA-BP set are improved by 4.9%, 0.3%, 16.7%, and 4.6%, respectively. According to the Fast Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization for meeting C50 concrete strength requirements, the optimal concrete mix ratio is as follows: cement 331.3 kg/m3, sand 639.4 kg/m3, stone 1039 kg/m3, fly ash 56 kg/m3, water 153 kg/m3, and water-reducing agent 0.632 kg/m3. The 28-day compressive strength, material cost, and carbon emissions show relative errors of 2.1%, 0.6%, and 2.9%, respectively. Compared with commercial concrete of the same strength grade, costs and carbon emissions are reduced by 7.2% and 15.9%, respectively. The methodology used in this study not only significantly improves the accuracy of concrete design but also considers the carbon emissions involved in the concrete preparation process, reflecting the strength, economic, and environmental impacts of material design. Practitioners are encouraged to explore integrated low-carbon research that spans from material selection to structural optimization. [ABSTRACT FROM AUTHOR]

This paper presented the development process of a novel multi-layer self-centering damper utilizing a NiTi shape memory alloy (SMA) with remarkable superelastic properties. The construction and operating principles of the novel damping device were introduced. A model for calculating the restoring force–displacement hysteretic curve of the novel damper was established, and based on this theoretical model, a parameter analysis of the damper's hysteresis performance was conducted. The effect of SMA pre-strain, SMA diameter, number of layers in the damper, and number of SMA wires per layer on the damper's stiffness, the unit cycle energy dissipation, and the equivalent viscous damping ratio were investigated, respectively. The results showed that the restoring force–displacement hysteretic curve of the novel SMA damper exhibits a full spindle shape, demonstrating the damper's excellent energy dissipation capacity, self-centering capability, significant stroke, and unique variable stiffness characteristics (i.e., appropriate initial stiffness, minimal isolation stiffness, and significant limit stiffness). The results also indicated that the SMA pre-strain has a minor impact on the damper's stiffness but a significant influence on unit cyclic energy dissipation and equivalent damping ratio. As the SMA pre-strain increased from 0.03 to 0.04, 0.05, and 0.06, the maximum stroke of the damper continuously decreases, while the unit cyclic energy dissipation initially increases and then decreases, with the optimal energy dissipation achieved at a pre-strain of 0.04. Increasing the SMA diameter results in a higher damper stiffness and energy dissipation capacity, with no significant change in maximum stroke and equivalent damping ratio. Increasing the number of damper layers leads to an increase in maximum stroke and unit-cycle energy dissipation, accompanied by a decrease in stiffness and almost constant equivalent damping ratio. As the number of SMA wires per layer increased from 8 to 16 and 32, the maximum stroke and equivalent damping ratio presented little variation, but the damper's stiffness and unit cyclic energy dissipation continuously increased. [ABSTRACT FROM AUTHOR]

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA–protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA–protein complexes. Despite technological advancements, accurately identifying DNA–protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA–protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA–protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA–protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA–protein complexes. [ABSTRACT FROM AUTHOR]

Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility. Thus, the current approach usually needs to screen thousands of candidate inhibitors for binding. Here, we propose a dynamic geometry design approach to enrich the hits with only a tiny pool of designed geometrically compatible scaffold candidates. First, our method uses graph-based tree decomposition to explore the complementarity rigid binding cyclic peptide and design the amino acid side chain length and charge to fit the RNA pocket. Then, we perform an energy-based dynamical network algorithm to optimize the inhibitor-RNA hydrogen bonds. Dynamic geometry-guided design yields successful inhibitors with low micromolar binding affinity scaffolds and experimentally competes with the natural RNA chaperone. The results indicate that the dynamic geometry method yields higher efficiency and accuracy than traditional methods. The strategy could be further optimized to design the length and chirality by adopting nonstandard amino acids and facilitating RNA engineering for biological or medical applications. [ABSTRACT FROM AUTHOR]

The freeze-thaw damage mechanism of concrete was summarized, and the influence and modification mechanisms of mineral admixtures, chemical admixtures, aggregate types and fibers on the freezeresistance performances of self-compacting concrete (SCC) were reviewed. Mineral admixtures and chemical admixtures reduce the porosity and microporous spacing of hardened SCC by changing the workability and air content of its mixture. The porosity, mechanical properties, deformation properties and particle composition of coarse and fine aggregates affect the fluidity of mixes and the state of water residence in the system, changing the pore structure and pore connectivity of hardened SCC. The fibers retard the generation and expansion of cracks caused by capillary pressure during freezing and thawing through micro-air-entraining and bridging and crack-blocking effects. The problems and development prospects in the research and application of SCC were further elaborated, and the ways to improve the frost resistance of self-compacting concrete were proposed. [ABSTRACT FROM AUTHOR]