China University of Mining and Technology

Triboelectrification is a well-known phenomenon that commonly occurs in nature and in our lives at any time and any place. Although each and every material exhibits triboelectrification, its quantification has not been standardized. A triboelectric series has been qualitatively ranked with regards to triboelectric polarization. Here, we introduce a universal standard method to quantify the triboelectric series for a wide range of polymers, establishing quantitative triboelectrification as a fundamental materials property. By measuring the tested materials with a liquid metal in an environment under well-defined conditions, the proposed method standardizes the experimental set up for uniformly quantifying the surface triboelectrification of general materials. The normalized triboelectric charge density is derived to reveal the intrinsic character of polymers for gaining or losing electrons. This quantitative triboelectric series may serve as a textbook standard for implementing the application of triboelectrification for energy harvesting and self-powered sensing.

In this paper, we construct a generalized Darboux transformation for the nonlinear Schrödinger equation. The associated N-fold Darboux transformation is given in terms of both a summation formula and determinants. As applications, we obtain compact representations for the Nth-order rogue wave solutions of the focusing nonlinear Schrödinger equation and Hirota equation. In particular, the dynamics of the general third-order rogue wave is discussed and shown to exhibit interesting structures.

Abstract A long debate on the charge identity and the associated mechanisms occurring in contact‐electrification (CE) (or triboelectrification) has persisted for many decades, while a conclusive model has not yet been reached for explaining this phenomenon known for more than 2600 years! Here, a new method is reported to quantitatively investigate real‐time charge transfer in CE via triboelectric nanogenerator as a function of temperature, which reveals that electron transfer is the dominant process for CE between two inorganic solids. A study on the surface charge density evolution with time at various high temperatures is consistent with the electron thermionic emission theory for triboelectric pairs composed of Ti–SiO 2 and Ti–Al 2 O 3 . Moreover, it is found that a potential barrier exists at the surface that prevents the charges generated by CE from flowing back to the solid where they are escaping from the surface after the contacting. This pinpoints the main reason why the charges generated in CE are readily retained by the material as electrostatic charges for hours at room temperature. Furthermore, an electron‐cloud–potential‐well model is proposed based on the electron‐emission‐dominatedcharge‐transfer mechanism, which can be generally applied to explain all types of CE in conventional materials.

Abstract The intercalation of potassium ions into graphite is demonstrated to be feasible, while the electrochemical performance of potassium‐ion batteries (KIBs) remains unsatisfying. More effort is needed to improve the specific capacity while maintaining a superior rate capability. As an attempt, nitrogen/oxygen dual‐doped hierarchical porous hard carbon (NOHPHC) is introduced as the anode in KIBs by carbonizing and acidizing the NH 2 ‐MIL‐101(Al) precursor. Specifically, the NOHPHC electrode delivers high reversible capacities of 365 and 118 mA h g −1 at 25 and 3000 mA g −1 , respectively. The capacity retention reaches 69.5% at 1050 mA g −1 for 1100 cycles. The reasons for the enhanced electrochemical performance, such as the high capacity, good cycling stability, and superior rate capability, are analyzed qualitatively and quantitatively. Quantitative analysis reveals that mixed mechanisms, including capacitance and diffusion, account for the K‐ion storage, in which the capacitance plays a more important role. Specifically, the enhanced interlayer spacing (0.39 nm) enables the intercalation of large K ions, while the high specific surface area of ≈1030 m 2 g −1 and the dual‐heteroatom doping (N and O) are conducive to the reversible adsorption of K ions.

Wearable electronics fabricated on lightweight and flexible substrate are believed to have great potential for portable devices, but their applications are limited by the life span of their batteries. We propose a hybridized self-charging power textile system with the aim of simultaneously collecting outdoor sunshine and random body motion energies and then storing them in an energy storage unit. Both of the harvested energies can be easily converted into electricity by using fiber-shaped dye-sensitized solar cells (for solar energy) and fiber-shaped triboelectric nanogenerators (for random body motion energy) and then further stored as chemical energy in fiber-shaped supercapacitors. Because of the all-fiber-shaped structure of the entire system, our proposed hybridized self-charging textile system can be easily woven into electronic textiles to fabricate smart clothes to sustainably operate mobile or wearable electronics.

The end-Permian mass extinction was the most severe biodiversity crisis in Earth history. To better constrain the timing, and ultimately the causes of this event, we collected a suite of geochronologic, isotopic, and biostratigraphic data on several well-preserved sedimentary sections in South China. High-precision U-Pb dating reveals that the extinction peak occurred just before 252.28 ± 0.08 million years ago, after a decline of 2 per mil (‰) in δ(13)C over 90,000 years, and coincided with a δ(13)C excursion of -5‰ that is estimated to have lasted ≤20,000 years. The extinction interval was less than 200,000 years and synchronous in marine and terrestrial realms; associated charcoal-rich and soot-bearing layers indicate widespread wildfires on land. A massive release of thermogenic carbon dioxide and/or methane may have caused the catastrophic extinction.

Graphene oxide, an intermediate during graphene synthesis by a modified Hummers's method, exhibits higher capacitance, up to 189 F g−1, than graphene due to an additional pseudo-capacitance effect of attached oxygen-containing functional groups on its basal planes. Taking its higher capacitance, lower cost and shorter processing time into consideration, graphene oxide may be a better choice than graphene as an electrode material for supercapacitors.

Shape-memory polymer light-emitting diodes (PLEDs) using a new silver nanowire/polymer electrode are reported. The electrode can be stretched by up to 16% with only a small increase in sheet resistance. Large deformation shape change and recovery of the PLEDs to various bistable curvatures result in minimal loss of electroluminescence performance.

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance.

Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.

Carbon dots (CDs) have tremendous potential applications in bioimaging, biomedicine, and optoelectronics. By far, it is still difficult to produce photoluminescence (PL) tunable CDs with high quantum yield (QY) across the entire visible spectrum and narrow the emission peak widths of CDs close to those of typical quantum dots. In this work, a series of CDs with tunable emission from 443 to 745 nm, quantum yield within 13-54%, and narrowed full width at half maximum (FWHM) from 108 to 55 nm, are obtained by only adjusting the reaction solvents in a one-pot solvothermal route. The distinct optical features of these CDs are based on their differences in the particle size, and the content of graphitic nitrogen and oxygen-containing functional groups, which can be modulated by controlling the dehydration and carbonization processes during solvothermal reactions. Blue, green, yellow, red, and even pure white light emitting films (Commission Internationale de L'Eclairage (CIE)= 0.33, 0.33, QY = 39%) are prepared by dispersing one or three kinds of CDs into polyvinyl alcohol with appropriate ratios. The near-infrared emissive CDs are excellent fluorescent probes for both in vitro and in vivo bioimaging because of their high QY in water, long-term stability, and low cytotoxicity.

In order to realize dynamic and seamless modeling of the global multi-scale terrain efficiently, a corresponding real-time, dynamic, and seamless modeling method is presented based on the spherical DQG (Degenerate Quadtree Grid) in this paper. By using the thoughts of the view-dependent and the LOD (Level of Details), the new method not only eliminates the grids cracks between the same level or the different levels completely, but avoids the phenomena of T connection and steep as well. In the end, the experiment is done by using c# programing language and DirectX graphics interface. The results show that this method realizes dynamic seamless expression of the global LOD terrain data, and the increased triangles in this method is only about 1/20 of that the vertical skirt method. The requirement of the real-time visualization of the global multi-scale terrain is satisfied completely.

Abstract The ever‐increasing demand for large‐scale energy storage systems requires novel battery technologies with low‐cost and sustainable properties. Due to earth‐abundance and cost effectiveness, the development of rechargeable potassium ion batteries (PIBs) has recently attracted much attention. Since carbon‐based materials are abundant, inexpensive, nontoxic, and safe, extensive feasibility investigations have suggested that they can become promising anode materials for PIBs. This review not only attempts to provide better understanding of the potassium storage mechanism, but also summarizes the availability of new carbon‐based materials and their electrochemical performance covering graphite, graphene, and hard carbon materials plus carbon‐based composites. Finally, the critical issues, challenges, and perspectives are discussed to demonstrate the developmental direction of PIBs.

The studies of topological phases of matter have been developed from condensed matter physics to photonic systems, resulting in fascinating designs of robust photonic devices. Recently, higher-order topological insulators have been investigated as a novel topological phase of matter beyond the conventional bulk-boundary correspondence. Previous studies of higher-order topological insulators have been mainly focused on the topological multipole systems with negative coupling between lattice sites. Here we experimentally demonstrate that second-order topological insulating phases without negative coupling can be realized in two-dimensional dielectric photonic crystals. We visualize both one-dimensional topological edge states and zero-dimensional topological corner states by using the near-field scanning technique. Our findings open new research frontiers for photonic topological phases and provide a new mechanism for light manipulating in a hierarchical way.

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA-disease associations and predicting potential human lncRNA-disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

This study aims to establish a stacked climate record of the Quaternary period from the Chinese loess sequence and to address the forcing mechanisms for the regional climate history of the Loess Plateau by correlating the stacked record with a composite δ 18 O record in deep‐sea sediments. A total of 18,352 samples were obtained from five loess sections, located at Baoji, Lingtai, Jingchuan, Puxian, and Pingliang in the southern and middle Loess Plateau. These yielded high‐resolution grain size records. Between‐section correlation of these shows that although small depositional hiatuses are present in places within a single section, most parts of the sections display near‐continuous dust deposition throughout the Quaternary. The grain size records were tuned simultaneously to the theoretical variations in obliquity and precession of the Earth’s orbit. The grain size records plotted on their orbital timescales were then averaged to form a stacked loess grain size time series, termed the “Chiloparts” record. This resolves most of the orbital timescale paleoclimate events buried in the loess‐soil sequences of the southern and middle Loess Plateau and can be used as a regional archive of the Pleistocene climate history in the Loess Plateau. Comparison of the “Chiloparts” record with a composite marine δ 18 O record shows that for the past 1.8 Ma, the loess‐paleosol record can be correlated almost cycle by cycle with the marine record. Several discrepancies in the climatic events between the two records have also been identified, implying that regional forcing mechanisms may have played a part in the climatic evolution of the Chinese Loess Plateau.

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

Purpose – The aim of this paper is to investigate the impact of knowledge sharing (KS) on firm performance and the mediating role of intellectual capital (IC). Design/methodology/approach – A research model was developed based on prior KS and IC studies. A survey was administered to a sample of high technology firms in China and 228 usable responses were collected. Structural equation modeling (SEM) was employed to test the research model. Findings – Tacit KS significantly was found to contribute to all three components of IC, namely human, structural and relational capital, while explicit KS only has a significant influence on human and structural capital. Human, structural and relational capital, enhance both operational and financial performance of firms. The effect of KS on firm performance is mediated by IC. Explicit KS has a greater effect on financial performance than operational performance, whereas tacit KS has a greater impact on operational performance than financial performance. Research limitations/implications – The sample of high technology firms in China might limit the generalization of the findings. Nonetheless, this study takes its lead from and extends prior research, thus providing a deepened understanding of the role of KS in organizational settings. Practical implications – The paper suggests that managers can enhance firm performance by enhancing their KS and IC. Managers can develop corresponding strategies based on the findings to achieve their specific performance goals. Originality/value – This is one of the first papers to examine how KS contributes to firm performance through the mediation of IC. It will add significant value for organizations trying to enhance their performance though KS practices.

Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This article presents an enhanced federated learning technique by proposing an asynchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks (DNNs) are categorized into shallow and deep layers, and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two data sets with different DNNs. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.