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Zeolite morphology is crucial in determining their catalytic activity, selectivity and stability, but quantitative descriptors of such a morphology effect are challenging to define. Here we introduce a descriptor that accounts for the morphology effect in the catalytic performances of H-ZSM-5 zeolite for C 4 olefin catalytic cracking. A series of H-ZSM-5 zeolites with similar sheet-like morphology but different c -axis lengths were synthesized. We found that the catalytic activity and stability is improved in samples with longer c- axis. Combining time-resolved in-situ FT-IR spectroscopy with molecular dynamics simulations, we show that the difference in catalytic performance can be attributed to the anisotropy of the intracrystalline diffusive propensity of the olefins in different channels. Our descriptor offers mechanistic insight for the design of highly effective zeolite catalysts for olefin cracking. The morphology of zeolites influences their catalytic activity, but defining descriptors to link morphology and activity is challenging. Here, time-resolved in situ FT-IR spectroscopy and MD simulations reveal that the difference in the catalytic performance of a series of H-ZSM-5 zeolites with similar sheet-like morphology can be attributed to intracrystalline diffusive propensities in different channels.

It is important to achieve the 3D reconstruction of UAV remote sensing images in deep learning-based multi-view stereo (MVS) vision. The lack of obvious texture features and detailed edges in UAV remote sensing images leads to inaccurate feature point matching or depth estimation. To address this problem, this study improves the TransMVSNet algorithm in the field of 3D reconstruction by optimizing its feature extraction network and costumed body depth prediction network. The improvement is mainly achieved by extracting features with the Asymptotic Pyramidal Network (AFPN) and assigning weights to different levels of features through the ASFF module to increase the importance of key levels and also using the UNet structured network combined with an attention mechanism to predict the depth information, which also extracts the key area information. It aims to improve the performance and accuracy of the TransMVSNet algorithm’s 3D reconstruction of UAV remote sensing images. In this work, we have performed comparative experiments and quantitative evaluation with other algorithms on the DTU dataset as well as on a large UAV remote sensing image dataset. After a large number of experimental studies, it is shown that our improved TransMVSNet algorithm has better performance and robustness, providing a valuable reference for research and application in the field of 3D reconstruction of UAV remote sensing images.

Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we want to obtain novel images of satellites photographed in space by another satellite, we must face problems like inaccurate camera focal lengths and poor image texture. There are also some small structures on satellites that NeRF-like algorithms cannot render well. In these cases, the NeRF’s performance cannot sufficiently meet the project’s needs. In fact, the images rendered by the NeRF will have many incomplete structures, while the MipNeRF will blur the edges of the structures on the satellite and obtain unrealistic colors. In response to these problems, we proposed STs-NeRF, which improves the quality of the new perspective images through an encoding module and a new network structure. We found a method for calculating poses that are suitable for our dataset and that enhance the network’s input learning effect by recoding the sampling points and viewing directions through a dynamic encoding (DE) module. Then, we input them into our layer-by-layer normalized multi-layer perceptron (LLNMLP). By simultaneously inputting points and directions into the network, we avoid the mutual influence between light rays, and through layer-by-layer normalization, we ease the model’s overfitting from a training perspective. Since real images should not be made public, we created a synthetic dataset and conducted a series of experiments. The experiments showed that our method achieves the best results in reconstructing captured satellite images, compared with the NeRF, the MipNeRF, the NeuS and the NeRF2Mesh, and improves the Peak Signal-to-Noise Ratio (PSNR) by 19%. We have also tested on public datasets, and our NeRF can still render acceptable images on datasets with better textures.

Zeolites, as crystalline materials with regular pore channels, are widely utilized in energy, environmental, and advanced manufacturing sectors. Characterizing zeolites is crucial for understanding their structure and properties, which are essential for various applications. Synchrotron Radiation‐based X‐ray Diffraction (SR‐XRD) has become an advanced tool in zeolite research, providing higher resolution, faster scans, and more precise structural information than laboratory X‐ray diffraction methods. This technique allows for detailed studies, from atomic structures to dynamic behaviors, particularly in understanding structural evolution during synthesis and monitoring changes in the framework during reactions. Moreover, SR‐XRD has made significant contributions to catalytic research by revealing structural alterations during catalytic processes and identifying active sites. However, SR‐XRD still faces challenges in data interpretation and other technological limitations. To overcome these, integrating SR‐XRD with other techniques and using AI‐assisted analysis are expected to further advance zeolite characterization and catalytic research. Synchrotron Radiation‐based X‐ray Diffraction (SR‐XRD) offers high‐resolution insights and real‐time monitoring of zeolite synthesis, structural transformations, and catalytic reactions, advancing zeolite‐based catalyst development. However, challenges such as data interpretation and technological limitations remain. Combining SR‐XRD with other techniques and AI‐assisted analysis can overcome these issues and accelerate research in zeolite and catalytic studies.

Control of the structure and function of three-dimensional multicellular tissues depends critically on the spatial and temporal coordination of cellular physical properties, yet the organizational principles that govern these events and their disruption in disease remain poorly understood. Using a multicellular mammary cancer organoid model, we map here the spatial and temporal evolution of positions, motions and physical characteristics of individual cells in three dimensions. Compared with cells in the organoid core, cells at the organoid periphery and the invasive front are found to be systematically softer, larger and more dynamic. These mechanical changes are shown to arise from supracellular fluid flow through gap junctions, the suppression of which delays the transition to an invasive phenotype. These findings highlight the role of spatiotemporal coordination of cellular physical properties in tissue organization and disease progression. A platform for probing the mechanics and migratory dynamics of a growing model breast cancer reveals that cells at the invasive edge are faster, softer and larger than those in the core. Eliminating the softer cells delays the transition to invasion.

With the assistance of emerging techniques, such as cloud computing, fog computing and Internet of Things (IoT), smart city is developing rapidly into a novel and well-accepted service pattern these days. The trend also facilitates numerous relevant applications, e.g., smart health care, smart office, smart campus, etc., and drives the urgent demand for data sharing. However, this brings many concerns on data security as there is more private and sensitive information contained in the data of smart city applications. It may incur disastrous consequences if the shared data are illegally accessed, which necessitates an efficient data access control scheme for data sharing in smart city applications with resource-poor user terminals. To this end, we proposes an efficient traceable and revocable time-based CP-ABE (TR-TABE) scheme which can achieve time-based and fine-grained data access control over large attribute universe for data sharing in large-scale smart city applications. To trace and punish the malicious users that intentionally leak their keys to pursue illicit profits, we design an efficient user tracing and revocation mechanism with forward and backward security. For efficiency improvement, we integrate outsourced decryption and verify the correctness of its result. The proposed scheme is proved secure with formal security proof and is demonstrated to be practical for data sharing in smart city applications with extensive performance evaluation.

Topological design of π electrons in zigzag-edged graphene nanoribbons (ZGNRs) leads to a wealth of magnetic quantum phenomena and exotic quantum phases 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 – 10 . Symmetric ZGNRs typically show antiferromagnetically coupled spin-ordered edge states 1 , 2 . Eliminating cross-edge magnetic coupling in ZGNRs not only enables the realization of a class of ferromagnetic quantum spin chains 11 , enabling the exploration of quantum spin physics and entanglement of multiple qubits in the one-dimensional limit 3 , 12 , but also establishes a long-sought-after carbon-based ferromagnetic transport channel, pivotal for ultimate scaling of GNR-based quantum electronics 1 , 2 – 3 , 9 , 13 . Here we report a general approach for designing and fabricating such ferromagnetic GNRs in the form of Janus GNRs (JGNRs) with two distinct edge configurations. Guided by Lieb’s theorem and topological classification theory 14 , 15 – 16 , we devised two JGNRs by asymmetrically introducing a topological defect array of benzene motifs to one zigzag edge, while keeping the opposing zigzag edge unchanged. This breaks the structural symmetry and creates a sublattice imbalance within each unit cell, initiating a spin-symmetry breaking. Three Z-shaped precursors are designed to fabricate one parent ZGNR and two JGNRs with an optimal lattice spacing of the defect array for a complete quench of the magnetic edge states at the ‘defective’ edge. Characterization by scanning probe microscopy and spectroscopy and first-principles density functional theory confirms the successful fabrication of JGNRs with a ferromagnetic ground-state localized along the pristine zigzag edge. Janus graphene nanoribbons with localized states on a single zigzag edge are fabricated by introducing a topological defect array of benzene motifs on the opposite zigzag edge, to break the structural symmetry.

Background Surgical treatment alone is not effective in addressing delayed fracture healing (DFH). This study nvestigates the molecular mechanism underlying fracture healing to identify improved therapeutic strategies. Methods Serum samples were collected from 76 normal fracture healing (NFH) and 70 DFH patients. RT-qPCR was performed to determine LINC00662, miR-330-3p, and PTEN mRNA expression in serum and Human Bone marrow mesenchymal stem cells (HBMSCs). The diagnostic potential of LINC00662 and miR-330-3p for DFH patients was evaluated via ROC analysis. Binding sites were predicted using bioinformatic databases and validated by dual-luciferase reporter assays. HBMSCs proliferation, apoptosis and osteogenic differentiation were analyzed using CCK-8, flow cytometry, andosteogenesis-related gene expression assays. Results LINC00662 was upregulated and miR-330-3p downregulated in DFH, and the two combined could efficiently diagnose DFH. LINC00662 knockdown promoted runt-related transcription factor 2 (RUNX2), osteocalcin (OCN), osteopontin (OPN), and alkaline phosphatase (ALP) mRNA expression, enhanced HBMSCs proliferation, and suppressed apoptosis. miR-330-3p inhibition reversed these effect. As a target of miR-330-3p, PTEN downregulation ameliorated the negative effects of miR-330-3p downregulation on HBMSCs. Conclusion LINC00662 and miR-330-3p synergistically could improve the diagnostic efficiency of DFH. LINC00662 impedes fracture healing by suppressing osteogenic differentiation and proliferation of HBMSCs while promoting apoptosis in HBMSCs via the miR-330-3p/PTEN axis.

Evaluating and understanding the effect of manufacturing processes on the creep performance in superalloys poses a significant challenge due to the intricate composition involved. This study presents a machine‐learning strategy capable of evaluating the effect of the heat treatment process on the creep performance of superalloys and predicting creep rupture life with high accuracy. This approach integrates classification and regression models with domain‐specific knowledge. The physical constraints lead to significantly enhanced prediction accuracy of the classification and regression models. Moreover, the heat treatment process is evaluated as the most important descriptor by integrating machine learning with superalloy creep theory. The heat treatment design of Waspaloy alloy is used as the experimental validation. The improved heat treatment leads to a significant enhancement in creep performance (5.5 times higher than the previous study). The research provides novel insights for enhancing the precision of predicting creep rupture life in superalloys, with the potential to broaden its applicability to the study of the effects of heat treatment processes on other properties. Furthermore, it offers auxiliary support for the utilization of machine learning in the design of heat treatment processes of superalloys. The workflow in the above figure is used to show the framework and how to use it to achieve HT optimization with enhanced creep rupture life. Figure (a) presents the model process, i.e., label HT, evaluate HT, and creep rupture prediction. Figure (b) shows the experimental validation for HT optimization based on the three processes.

In this work, 1,3-diketone synthesized via the Claisen condensation method and nano-copper particles modified by the Brust–Schiffrin method were added into a commercial marine medium-speed diesel engine cylinder piston oil to evaluate their effects on boundary lubrication performance. Friction and wear tests conducted on CKS-coated piston ring and cast-iron cylinder liner samples demonstrated significant reductions in both friction and wear with the addition of 1,3-diketone and nano-copper particles. Compared to the original oil without additives, the friction force was reduced by up to 16.7%, while the wear of the piston ring and cylinder liner was decreased by up to 21.6% and 15.1% at 150 °C, respectively. A worn surface analysis indicated that the addition of 1,3-diketone and functionalized nano-copper particles influenced the depolymerization and tribo-chemical reactions of the anti-wear additive ZDDP (zinc dialkyldithiophosphate) in the original engine oil. This modification enhanced the oil’s anti-friction and anti-wear properties, offering valuable insights into the development of eco-friendly lubricants for energy-efficient systems.