Beijing Information Science & Technology University

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

Abstract Zinc‐ion batteries are under current research focus because of their uniqueness in low cost and high safety. However, it is still desirable to improve the rate performance by improving the Zn 2+ (de)intercalation kinetics and long‐cycle stability by eliminating the dendrite formation problem. Herein, the first paradigm of a high‐rate and ultrastable flexible quasi‐solid‐state zinc‐ion battery is constructed from a novel 2D ultrathin layered zinc orthovanadate array cathode, a Zn array anode supported by a conductive porous graphene foam, and a gel electrolyte. The nanoarray structure for both electrodes assures the high rate capability and alleviates the dendrite growth. The flexible Zn‐ion battery has a depth of discharge of ≈100% for the cathode and 66% for the anode, and delivers an impressive high‐rate of 50 C (discharge in 60 s), long‐term durability of 2000 cycles at 20 C, and unprecedented energy density ≈115 Wh kg −1 , together with a peak power density ≈5.1 kW kg −1 (calculation includes masses of cathode, anode, and current collectors). First principles calculations and quantitative kinetics analysis show that the high‐rate and stable properties are correlated with the 2D fast ion‐migration pathways and the introduced intercalation pseudocapacitance.

Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

In recent years, the concept of the Metaverse has attracted considerable attention. This article provides a comprehensive overview of the Metaverse. First, the development status of the Metaverse is presented. We summarize the policies of various countries, companies, and organizations relevant to the Metaverse, as well as statistics on the number of Metaverse-related publications. Characteristics of the Metaverse are identified: 1) multitechnology convergence; 2) sociality; and 3) hyper-spatio-temporality. For the multitechnology convergence of the Metaverse, we divide the technological framework of the Metaverse into five dimensions. For the sociality of the Metaverse, we focus on the Metaverse as a virtual social world. Regarding the characteristic of hyper-spatio-temporality, we introduce the Metaverse as an open, immersive, and interactive 3-D virtual world which can break through the constraints of time and space in the real world. The challenges of the Metaverse are also discussed.

With the development of flexible electronic devices and large-scale energy storage technologies, functional polymer-matrix nanocomposites with high permittivity (high-k) are attracting more attention due to their ease of processing, flexibility, and low cost. The percolation effect is often used to explain the high-k characteristic of polymer composites when the conducting functional fillers are dispersed into polymers, which gives the polymer composite excellent flexibility due to the very low loading of fillers. Carbon nanotubes (CNTs) and graphene nanosheets (GNs), as one-dimensional (1D) and two-dimensional (2D) carbon nanomaterials respectively, have great potential for realizing flexible high-k dielectric nanocomposites. They are becoming more attractive for many fields, owing to their unique and excellent advantages. The progress in dielectric fields by using 1D/2D carbon nanomaterials as functional fillers in polymer composites is introduced, and the methods and mechanisms for improving dielectric properties, breakdown strength and energy storage density of their dielectric nanocomposites are examined. Achieving a uniform dispersion state of carbon nanomaterials and preventing the development of conductive networks in their polymer composites are the two main issues that still need to be solved in dielectric fields for power energy storage. Recent findings, current problems, and future perspectives are summarized.

Despite a plethora of applications ranging from quantum memories to high-resolution lithography, the current technologies to generate vector vortex beams (VVBs) suffer from less efficient energy use, poor resolution, low damage threshold, and bulky size, preventing further practical applications. We propose and experimentally demonstrate an approach to generate VVBs with a single metasurface by locally tailoring phase and transverse polarization distribution. This method features the spin-orbit coupling and the superposition of the converted part with an additional phase pickup and the residual part without a phase change. By maintaining the equal components for the converted part and the residual part, the cylindrically polarized vortex beams carrying orbital angular momentum are experimentally demonstrated based on a single metasurface at subwavelength scale. The proposed approach provides unprecedented freedom in engineering the properties of optical waves with high-efficiency light utilization and a minimal footprint.

Dielectric materials with high electric energy densities and low dielectric losses are of critical importance in a number of applications in modern electronic and electrical power systems. An organic–inorganic 0–3 nanocomposite, in which nanoparticles (0‐dimensional) are embedded in a 3‐dimensionally connected polymer matrix, has the potential to combine the high breakdown strength and low dielectric loss of the polymer with the high dielectric constant of the ceramic fillers, representing a promising approach to realize high energy densities. However, one significant drawback of the composites explored up to now is that the increased dielectric constant of the composites is at the expense of the breakdown strength, limiting the energy density and dielectric reliability. In this study, by expanding the traditional 0–3 nanocomposite approach to a multilayered structure which combines the complementary properties of the constituent layers, one can realize both greater dielectric displacement and a higher breakdown field than that of the polymer matrix. In a typical 3‐layer structure, for example, a central nanocomposite layer of higher breakdown strength is introduced to substantially improve the overall breakdown strength of the multilayer‐structured composite film, and the outer composite layers filled with large amount of high dielectric constant nanofillers can then be polarized up to higher electric fields, hence enhancing the electric displacement. As a result, the topological‐structure modulated nanocomposites, with an optimally tailored nanomorphology and composite structure, yield a discharged energy density of 10 J/cm 3 with a dielectric breakdown strength of 450 kV mm –1 , much higher than those reported from all earlier studies of nanocomposites.

Abstract Non-fullerene acceptors (NFAs) based on non-fused conjugated structures have more potential to realize low-cost organic photovoltaic (OPV) cells. However, their power conversion efficiencies (PCEs) are much lower than those of the fused-ring NFAs. Herein, a new bithiophene-based non-fused core (TT-P i ) featuring good planarity as well as large steric hindrance was designed, based on which a completely non-fused NFA, A4T-16, was developed. The single-crystal result of A4T-16 reveals that a three-dimensional interpenetrating network can be formed due to the compact π–π stacking between the adjacent end-capping groups. A high PCE of 15.2% is achieved based on PBDB-TF:A4T-16, which is the highest value for the cells based on the non-fused NFAs. Notably, the device retains ~84% of its initial PCE after 1300 h under the simulated AM 1.5 G illumination (100 mW cm −2 ). Overall, this work provides insight into molecule design of the non-fused NFAs from the aspect of molecular geometry control.

As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it also supports artificial intelligence evolving from a centralized manner to a distributed one. In this paper, we provide a comprehensive survey on the distributed artificial intelligence (DAI) empowered by end-edge-cloud computing (EECC), where the heterogeneous capabilities of on-device computing, edge computing, and cloud computing are orchestrated to satisfy the diverse requirements raised by resource-intensive and distributed AI computation. Particularly, we first introduce several mainstream computing paradigms and the benefits of the EECC paradigm in supporting distributed AI, as well as the fundamental technologies for distributed AI. We then derive a holistic taxonomy for the state-of-the-art optimization technologies that are empowered by EECC to boost distributed training and inference, respectively. After that, we point out security and privacy threats in DAI-EECC architecture and review the benefits and shortcomings of each enabling defense technology in accordance with the threats. Finally, we present some promising applications enabled by DAI-EECC and highlight several research challenges and open issues toward immersive performance acquisition.

Physiological electric potential is well-known for its indispensable role in maintaining bone volume and quality. Although implanted biomaterials simulating structural, morphological, mechanical, and chemical properties of natural tissue or organ has been introduced in the field of bone regeneration, the concept of restoring physiological electric microenvironment remains ignored in biomaterials design. In this work, a flexible nanocomposite membrane mimicking the endogenous electric potential is fabricated to explore its bone defect repair efficiency. BaTiO3 nanoparticles (BTO NPs) were first coated with polydopamine. Then the composite membranes are fabricated with homogeneous distribution of Dopa@BTO NPs in poly(vinylidene fluoridetrifluoroethylene) (P(VDF-TrFE)) matrix. The surface potential of the nanocomposite membranes could be tuned up to -76.8 mV by optimizing the composition ratio and corona poling treatment, which conform to the level of endogenous biopotential. Remarkably, the surface potential of polarized nanocomposite membranes exhibited a dramatic stability with more than half of original surface potential remained up to 12 weeks in the condition of bone defect. In vitro, the membranes encouraged bone marrow mesenchymal stem cells (BM-MSCs) activity and osteogenic differentiation. In vivo, the membranes sustainably maintained the electric microenvironment giving rise to rapid bone regeneration and complete mature bone-structure formation. Our findings evidence that physiological electric potential repair should be paid sufficient attention in biomaterials design, and this concept might provide an innovative and well-suited strategy for bone regenerative therapies.

In this paper, we propose a dynamic audit service for verifying the integrity of an untrusted and outsourced storage. Our audit service is constructed based on the techniques, fragment structure, random sampling, and index-hash table, supporting provable updates to outsourced data and timely anomaly detection. In addition, we propose a method based on probabilistic query and periodic verification for improving the performance of audit services. Our experimental results not only validate the effectiveness of our approaches, but also show our audit system verifies the integrity with lower computation overhead and requiring less extra storage for audit metadata.

Compared with radar and visible light imaging, infrared imaging has its own unique advantages, and in recent years, it has become a topic of intense research interest. Robust small-target detection is one of the key techniques in infrared search and tracking (IRST) applications, and there is no doubt that it has become an investigatory hot spot. In real applications, targets and backgrounds usually change quickly with very high velocities. In addition, a rapidly moving sensor platform typically makes the motion traces of the targets inconsistent. These factors reduce the detection performance of spatiotemporal-based methods, and thus single-frame infrared small-target detection is even more essential. In this survey, existing single-frame infrared small-target detection methods are comprehensively reviewed.

Free vibration and static bending of functionally graded (FG) graphene nanoplatelet (GPL) reinforced composite doubly-curved shallow shells with three distinguished distributions are analyzed. Material properties with gradient variation in the thickness aspect are evaluated by the modified Halpin-Tsai model. Mathematical model of the simply supported doubly-curved shallow shells rests upon Hamilton Principle and a higher order shear deformation theory (HSDT). The free vibration frequencies and bending deflections are gained by taking into account Navier technique. The agreement between the obtained results and ANSYS as well as the prior results in the open literature verifies the accuracy of the theory in this article. Further, parametric studies are accomplished to highlight the significant influence of GPL distribution patterns and weight fraction, stratification number, dimensions of GPLs and shells on the mechanical behavior of the system.

With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.

Purpose This paper aims to investigate what motivates consumers to adopt one of the emerging mobile applications of the sharing economy, ridesharing application. Using social cognitive theory as the theoretical framework, this study develops a value adoption model to illustrate important factors that influence adoption of ridesharing applications. Design/methodology/approach Based on prior literature, a quantitative methodology was adopted using a survey questionnaire that allows for the measurement of the nine constructs contained in the hypothesized theoretical model. Data collected from a sample of 314 respondents in Beijing, China provided the foundation for the examination of the proposed relationships in the model. Findings First, the results indicate that self-efficacy is a fundamental factor that has a direct effect on consumers’ perceptions of value and an indirect effect on behavioral intentions. Second, the study demonstrates that functional value, emotional value and social value are critical antecedents of overall perceived value of ridesharing applications. On the other hand, learning effort and risk perception are not significant perceived costs for consumers in adopting ridesharing applications. Research limitations/implications Although typical adopters of internet applications constitute a significant portion of younger consumers, the use of a college student sample in this study may affect the generalizability of the results. Practical implications The findings provide critical insight into consumer motivations behind adoption of ridesharing applications specifically, and for sharing economy platforms in general. Originality/value This study provides important theoretical implications for innovation adoption research through an empirical examination of the relationship between personal, environmental and behavioral factors in a framework of social cognitive theory.

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

OBJECTIVE: To identify factors driving the rapid increase in caesarean section in China between 1988 and 2008. METHODS: Data from four national cross-sectional surveys (1993, 1998, 2003 and 2008) and modified Poisson regression were used to determine whether changes in household income, access to health insurance or women's education accounted for the rise in caesarean sections in urban and rural areas. FINDINGS: In 2008, 64.1% of urban women and 11.3% of women in the poorest rural region reported giving birth by caesarean section. A fast rise was occurring in all socioeconomic groups. Between 1993 and 2008, the risk of caesarean section had increased more than threefold in urban areas (relative risk, RR: 3.63; 95% confidence interval, CI: 2.61-5.04) and more than 15-fold in rural areas (RR: 15.46; 95% CI: 10.46-22.86). After adjustment for improvements in income, education and access to health insurance over the study period, the RR dropped minimally in urban areas (RR: 3.07; 95% CI: 2.32-4.07), which suggests that these factors do not explain the rise; in rural areas, the adjusted RR dropped to 7.18 (95% CI: 4.82-10.69), which shows that socioeconomic change is only partly responsible for the rise. Socioeconomic region of residence was a more important driver of the caesarean section rate than individual socioeconomic status. CONCLUSION: The large variation in caesarean section rate by socioeconomic region--independent of individual income, health insurance or education--suggests that structural factors related to service supply have influenced the increasing rate more than a woman's ability to pay.

Tip-enhanced Raman spectroscopy (TERS), a combination of Raman spectroscopy and apertureless near-field scanning optical microscopy using a metallic tip which resonates with the local mode of the surface plasmon, can provide a high-sensitive and high-spatial-resolution optical analytical approach. The basic principle of TERS, common experimental setups, various SPM technologies, and excitation/collection configurations are introduced as well as recent research progress with respect to TERS.

The trends in miniaturization of electronic devices give rise to the attention of energy harvesting technologies that gathers tiny wattages of power. Here this study demonstrates an ultrathin flexible single electrode triboelectric nanogenerator (S‐TENG) which not only could harvest mechanical energy from human movements and ambient sources, but also could sense instantaneous force without extra energy. The S‐TENG, which features an extremely simple structure, has an average output current of 78 μA, lightening up at least 70 LEDs (light‐emitting diode). Even tapped by bare finger, it exhibits an output current of 1 μA. The detection sensitivity for instantaneous force sensing is about 0.947 μA MPa −1 . Performances of the device are also systematically investigated under various motion types, press force, and triboelectric materials. The S‐TENG has great application prospects in sustainable wearable devices, sustainable medical devices, and smart wireless sensor networks owning to its thinness, light weight, energy harvesting, and sensing capacities.

Abstract The interaction of exciton‐plasmon coupling and the conversion of exciton‐plasmon‐photon have been widely investigated experimentally and theoretically. In this review, we introduce the exciton‐plasmon interaction from basic principle to applications. There are two kinds of exciton‐plasmon coupling, which demonstrate different optical properties. The strong exciton‐plasmon coupling results in two new mixed states of light and matter separated energetically by a Rabi splitting that exhibits a characteristic anticrossing behavior of the exciton‐LSP energy tuning. Compared to strong coupling, such as surface‐enhanced Raman scattering, surface plasmon (SP)‐enhanced absorption, enhanced fluorescence, or fluorescence quenching, there is no perturbation between wave functions; the interaction here is called the weak coupling. SP resonance (SPR) arises from the collective oscillation induced by the electromagnetic field of light and can be used for investigating the interaction between light and matter beyond the diffraction limit. The study on the interaction between SPR and exaction has drawn wide attention since its discovery not only due to its contribution in deepening and broadening the understanding of SPR but also its contribution to its application in light‐emitting diodes, solar cells, low threshold laser, biomedical detection, quantum information processing, and so on.