Influences of Ultrafine Ti(C, N) on the Sintering Process and Mechanical Properties of Micron Ti(C, N)-Based Cermets

For investigating the influence mechanism underlying ultrafine Ti(C, N) within micron Ti(C, N)-based cermets, three cermets including diverse ultrafine Ti(C, N) contents were employed. In addition, for the prepared cermets, their sintering process, microstructure, and mechanical properties were systematically studied. According to our findings, adding ultrafine Ti(C, N) primarily affects the densification and shrinkage behavior in the solid-state sintering stage. Additionally, material-phase and microstructure evolution were investigated under the solid-state stage from 800 to 1300 °C. Adding ultrafine Ti(C, N) enhanced the diffusion and dissolution behavior of the secondary carbide (Mo2C, WC, and (Ta, Nb)C) under a lower sintering temperature of 1200 °C. Further, as sintering temperature increased, adding ultrafine Ti(C, N) enhanced heavy element transformation behaviors in the binder phase and accelerated solid-solution (Ti, Me) (C, N) phase formation. When the addition of ultrafine Ti(C, N) reached 40 wt%, the binder phase had increased its liquefying speed. Moreover, the cermet containing 40 wt% ultrafine Ti(C, N) displayed superb mechanical performances

Integrated analysis of C3AR1 and CD163 associated with immune infiltration in intracranial aneurysms pathogenesis

Background: To identify potential immune-related biomarkers, molecular mechanism, and therapeutic agents of intracranial aneurysms (IAs). Methods: We identified the differentially expressed genes (DEGs) between IAs and control samples from GSE75436, GSE26969, GSE6551, and GSE13353 datasets. We used weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) analysis to identify immune-related hub genes. We evaluated the expression of hub genes by using qRT-PCR analysis. Using miRNet, NetworkAnalyst, and DGIdb databases, we analyzed the regulatory networks and potential therapeutic agents targeting hub genes. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to identify optimal biomarkers among hub genes. The diagnostic value was validated by external GSE15629 dataset. Results: We identified 227 DEGs and 22 differentially infiltrating immune cells between IAs and control samples from GSE75436, GSE26969, GSE6551, and GSE13353 datasets. We further identified 41 differentially expressed immune-related genes (DEIRGs), which were primarily enriched in the chemokine-mediated signaling pathway, myeloid leukocyte migration, endocytic vesicle membrane, chemokine receptor binding, chemokine activity, and viral protein interactions with cytokines and their receptors. Among 41 DEIRGs, 10 hub genes including C3AR1, CD163, CCL4, CXCL8, CCL3, TLR2, TYROBP, C1QB, FCGR3A, and FCGR1A were identified with good diagnostic values (AUC >0.7). Hsa-mir-27a-3p and transcription factors, including YY1 and GATA2, were identified the primary regulators of hub genes. 92 potential therapeutic agents targeting hub genes were predicted. C3AR1 and CD163 were finally identified as the best diagnostic biomarkers using LASSO logistic regression (AUC = 0.994). The diagnostic value of C3AR1 and CD163 was validated by the external GSE15629 dataset (AUC = 0.914). Conclusions: This study revealed the importance of C3AR1 and CD163 in immune infiltration in IAs pathogenesis. Our finding provided a valuable reference for subsequent research on the potential targets for molecular mechanisms and intervention of IAs

A topological framework for real-time 3D weather radar data processing

Real-time 3D weather radar data processing makes it possible to efficiently simulate meteorological processes in digital Earth and support the assessment of meteorological disasters. The current real-time meteorological operation system can only deal with radar data within 2D space as a flat map and lacks supporting 3D characteristics. Thus, valuable 3D information imbedded in radar data cannot be completely presented to meteorological experts. Due to the large amount of data and high complexity of radar data 3D operation, regular methods are not competent for supporting real-time 3D radar data processing and representation. This study aims to perform radar data 3D operations with high efficiency and instant speed to provide real-time 3D support for the meteorological field. In this paper, a topological framework composed of basic inner topological objects is proposed along with the quadtree structure and LOD architecture, based on which 3D operations on radar data can be conducted in a split second and 3D information can be presented in real time. As the applications of the proposed topological framework, two widely used 3D algorithms in the meteorological field are also implemented in this paper. Finally, a case study verifies the applicability and validity of the proposed topological framework

Activity-based process construction for participatory geo-analysis

Due to its advantages in participation and collaboration, participatory geo-analysis has been used for solving different types of geographical issues. Participatory geo-analysis is usually a complicated process consisting of various tasks that may involve different multidisciplinary participants. Previous studies have focused primarily on how to improve participation in specific individual tasks, especially idea discussion and decision-making, but they have ignored collaboration throughout the entire process. During a complete participatory geo-analysis effort, the various participants should concentrate on their familiar work and fully exploit their talents to perform work collaboratively. Therefore, we propose an activity-based process construction method to assist different participants in understanding the geo-analysis process and in concentrating on their familiar work. Eight core activities are established for the geo-analysis process: (1) context definition and resource collection, (2) data processing, (3) data analysis, (4) data visualization, (5) geo-analysis model construction, (6) model effectiveness evaluation, (7) geographical simulation, and (8) decision making. By using a visualization-based method, different activities can be linked together to represent the entire analytical process. Moreover, each activity is designed via a specialized web-based workspace in which online tools and resources are accessed to assist the participants with their geo-analysis practices. A prototype system was developed based on the proposed method, and a case study on a participatory risk assessment of coronavirus disease 2019 (COVID-19) was demonstrated using this system. The result suggests that the proposed method can promote collaboration among participants with different backgrounds, and verifies its feasibility and suitability

Customizable process design for collaborative geographic analysis

Collaborative geographic analysis can lead to better outcomes but requires complicated interactions among participants, support resources and analytic tools. A process expression with explicit structure and content can help coordinate and guide these interactions. For different geographic problems, the structure and content of collaborative geographic analysis are generally distinct. Since the process structure embodies the pathway of problem-solving and the process content contains the information flow and internal interactions, both the structure and the content of the process expression must be clarified during process customization. However, relevant studies concerning the collaborative geographic analysis process mainly focus on the process structure, which remains a “black box” in terms of the process content, especially the internal interactions. Therefore, this article designs a customizable process expression model that takes both process structure and content into account and proposes a corresponding process customization method for collaborative geographic analysis. Additionally, a support method for geographic analysis process implementation is also provided. To verify the feasibility and capability, these methods were implemented in a prototype system, and a case study on traffic noise assessment was conducted. The results suggest that the proposed strategy can effectively improve geographic analysis by customizing processes, guiding participants, performing interactions, and recording operations throughout the process

Sustained and Targeted Delivery of Self-Assembled Doxorubicin Nonapeptides Using pH-Responsive Hydrogels for Osteosarcoma Chemotherapy

While chemotherapeutic agents have particularly potent effects in many types of cancer, their clinical applications are still far from satisfactory due to off-target drug exposure, chemotherapy resistance, and adverse effects, especially in osteosarcoma. Therefore, it is clinically promising to construct a novel tumor-targeted drug delivery system to control drug release and alleviate side effects. In this study, a pH-responsive nonapeptide hydrogel was designed and fabricated for the tumor-targeted drug delivery of doxorubicin (DOX). Using a solid-phase synthesis method, a nonapeptide named P1 peptide that is structurally akin to surfactant-like peptides (SLPs) due to its hydrophobic tail and hydrophilic head was synthesized. The physicochemical properties of the P1 hydrogel were characterized via encapsulation capacity, transmission electron microscopy (TEM), circular dichroism (CD), zeta potential, rheological analysis, and drug release studies. We also used in vitro and in vivo experiments to investigate the cytocompatibility and tumor inhibitory efficacy of the drug-loaded peptide hydrogel. The P1 peptide could self-assemble into biodegradable hydrogels under neutral conditions, and the prepared drug-loaded hydrogels exhibited good injectability and biocompatibility. The in vitro drug release studies showed that DOX-P1 hydrogels had high sensitivity to acidic conditions (pH 5.8 versus 7.4, up to 3.6-fold). Furthermore, the in vivo experiments demonstrated that the DOX-P1 hydrogel could not only amplify the therapeutic effect but also increase DOX accumulation at the tumor site. Our study proposes a promising approach to designing a pH-responsive hydrogel with controlled doxorubicin-release action based on self-assembled nonapeptides for targeted chemotherapy

Structural Studies of Fluoroborate Laser Glasses by Solid State NMR and EPR Spectroscopies

The structure of glasses in the systems (100 – <i>x</i>)­B₂O₃–<i>x</i>PbF₂ (<i>x</i> = 30, 40, and 50) and 50B₂O₃–(50 – <i>x</i>)­PbO–<i>x</i>PbF₂ (<i>x</i> = 5, 10, 15, 20, 25, 30, 35, 40, and 45) has been studied by solid state NMR and EPR spectroscopies. On the basis of ¹¹B and ¹⁹F high resolution solid state NMR as well as on ¹¹B/¹⁹F double resonance results, we develop a quantitative structural description on the atomic scale. ¹⁹F NMR results indicate a systematic dependence of the fluoride speciation on PbF₂ content: At low <i>x</i>-values, F^– ions are predominantly found on BO_3/2F^– units, whereas, at higher <i>x</i>-values, fluoride tends to be sequestrated into amorphous domains rich in PbF₂. In addition, both pulsed EPR studies of Yb³⁺ doped glasses and photophysical studies of Eu³⁺ doped samples indicate a mixed fluoride/borate coordination of the rare-earth ions and the absence of nanophase segregation effects

Iterative integration of deep learning in hybrid Earth surface system modelling

Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations