Synthesis Strategies about 2D Materials

In recent years, more and more attentions have been paid to two-dimensional (2D) materials due to the excellent electrical, optical, thermal and mechanical properties. To characterize the layer-dependent changes in properties and to provide pathways for their integration into a multitude of applications, it is essential to explore the reliable syntheses of single- and few-layer 2D materials. Therefore, many strategies, such as micromechanical exfoliation, ultrasonic exfoliation, hydrothermal method, topochemical transformation, chemical vapor deposition method and so on, have been developed to synthesize high-quality and ultrathin nanosheets showing their own merits and demerits in preparing 2D nanomaterials. Herein, an overview of the recent progress in the synthetic techniques is presented for 2D materials, in which we would introduce their experimental scheme, advantages and disadvantages, and applications of these synthetic strategies. Eventually, the potential trends and future directions for synthesizing technology for 2D materials are proposed

The geography of city liveliness and consumption: evidence from location-based big data

Understanding the complexity in the connection between city liveliness and spatial configurationsfor consumptive amenities has been an important but understudied research field in fast urbanising countries like China. This paper presents the first step towards filling this gap though location-based big data perspectives. City liveliness is measured by aggregated spacetime human activity intensities using mobile phone positioning data.Consumptive amenities are identified by point-of-interest data from Chinese Yelp website (dian ping). The results provide the insights into the geographic contextual uncertainties of consumptive amenities in shaping the rise and fall in the vibrancy of city liveliness

MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are limited. Due to the high cost of acquiring pixel-level segmentation annotations on the large-scale pretraining dataset, pretraining with unannotated images is highly desirable. In this work, we propose a novel self-supervised learning strategy named Volume Fusion (VF) for pretraining 3D segmentation models. It fuses several random patches from a foreground sub-volume to a background sub-volume based on a predefined set of discrete fusion coefficients, and forces the model to predict the fusion coefficient of each voxel, which is formulated as a self-supervised segmentation task without manual annotations. Additionally, we propose a novel network architecture based on parallel convolution and transformer blocks that is suitable to be transferred to different downstream segmentation tasks with various scales of organs and lesions. The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models. The code and pretrained model are available at https://github.com/openmedlab/MIS-FM.Comment: 13 pages, 8 figure

Suspension of a Single Sphere in a Stirred Tank with Transitional Flow

ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support from Scientific Research and Technology Development Projects of China National Petroleum Corporation (No.2020B-2512).Peer reviewe

How Can Recommender Systems Benefit from Large Language Models: A Survey

Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the "HOW" question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi

Integrating remote sensing and geospatial big data for urban land use mapping: a review

Remote Sensing (RS) has been used in urban mapping for a long time; however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data (GBD) are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects (i.e., urban land cover) to socioeconomic aspects (i.e., urban land use). Integrating RS and GBD could be an effective way to combine physical and socioeconomic aspects with great potential for high-quality urban land use classification. In this study, we reviewed the existing literature and focused on the state-of-the-art and perspective of the urban land use categorization by integrating RS and GBD. Specifically, the commonly used RS features (e.g., spectral, textural, temporal, and spatial features) and GBD features (e.g., spatial, temporal, semantic, and sequence features) were identified and analyzed in urban land use classification. The integration strategies for RS and GBD features were categorized into feature-level integration (FI) and decision-level integration (DI). To be more specific, the FI method integrates the RS and GBD features and classifies urban land use types using the integrated feature sets; the DI method processes RS and GBD independently and then merges the classification results based on decision rules. We also discussed other critical issues, including analysis unit setting, parcel segmentation, parcel labeling of land use types, and data integration. Our findings provide a retrospect of different features from RS and GBD, strategies of RS and GBD integration, and their pros and cons, which could help to define the framework for future urban land use mapping and better support urban planning, urban environment assessment, urban disaster monitoring and urban traffic analysis

Case report of a Li-Fraumeni syndrome-like phenotype with a de novo mutation in <i>CHEK2</i>

BACKGROUND: Cases of multiple tumors are rarely reported in China. In our study, a 57-year-old female patient had concurrent squamous cell carcinoma, mucoepidermoid carcinoma, brain cancer, bone cancer, and thyroid cancer, which has rarely been reported to date. METHODS: To determine the relationship among these multiple cancers, available DNA samples from the thyroid, lung, and skin tumors and from normal thyroid tissue were sequenced using whole exome sequencing. RESULTS: The notable discrepancies of somatic mutations among the 3 tumor tissues indicated that they arose independently, rather than metastasizing from 1 tumor. A novel deleterious germline mutation (chr22:29091846, G->A, p.H371Y) was identified in CHEK2, a Li–Fraumeni syndrome causal gene. Examining the status of this novel mutation in the patient's healthy siblings revealed its de novo origin. CONCLUSION: Our study reports the first case of Li–Fraumeni syndrome-like in Chinese patients and demonstrates the important contribution of de novo mutations in this type of rare disease

Photoemission Evidence of a Novel Charge Order in Kagome Metal FeGe

A charge order has been discovered to emerge deep into the antiferromagnetic phase of the kagome metal FeGe. To study its origin, the evolution of the low-lying electronic structure across the charge order phase transition is investigated with angle-resolved photoemission spectroscopy. We do not find signatures of nesting between Fermi surface sections or van-Hove singularities in zero-frequency joint density of states, and there are no obvious energy gaps at the Fermi level, which exclude the nesting mechanism for the charge order formation in FeGe. However, two obvious changes in the band structure have been detected, i.e., one electron-like band around the K point and another one around the A point move upward in energy position when the charge order forms. These features can be well reproduced by our density-functional theory calculations, where the charge order is primarily driven by magnetic energy saving via large dimerizations of a quarter of Ge1-sites (in the kagome plane) along the c-axis. Our results provide strong support for this novel charge order formation mechanism in FeGe, in contrast to the conventional nesting mechanism.Comment: 6 pages, 4 figure