Xi'an University of Science and Technology

Abstract. Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC_FCS30-2015 achieved an overall accuracy of 82.5 % and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 % and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 % and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI_LC, MCD12Q1, FROM_GLC, and GlobeLand30) indicated that GLC_FCS30-2015 provides more spatial details than CCI_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM_GLC-2015 and GlobeLand30-2010. They also showed that GLC_FCS30-2015 achieved the best overall accuracy of 82.5 % against FROM_GLC-2015 of 59.1 % and GlobeLand30-2010 of 75.9 %. Therefore, it is concluded that the GLC_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).

BACKGROUND: High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, particularly the identification of long non-coding RNAs (lncRNAs) from de novo sequencing data. This requires tools that are not restricted by prior gene annotations, genomic sequences and high-quality sequencing. RESULTS: We present an alignment-free tool called PLEK (predictor of long non-coding RNAs and messenger RNAs based on an improved k-mer scheme), which uses a computational pipeline based on an improved k-mer scheme and a support vector machine (SVM) algorithm to distinguish lncRNAs from messenger RNAs (mRNAs), in the absence of genomic sequences or annotations. The performance of PLEK was evaluated on well-annotated mRNA and lncRNA transcripts. 10-fold cross-validation tests on human RefSeq mRNAs and GENCODE lncRNAs indicated that our tool could achieve accuracy of up to 95.6%. We demonstrated the utility of PLEK on transcripts from other vertebrates using the model built from human datasets. PLEK attained >90% accuracy on most of these datasets. PLEK also performed well using a simulated dataset and two real de novo assembled transcriptome datasets (sequenced by PacBio and 454 platforms) with relatively high indel sequencing errors. In addition, PLEK is approximately eightfold faster than a newly developed alignment-free tool, named Coding-Non-Coding Index (CNCI), and 244 times faster than the most popular alignment-based tool, Coding Potential Calculator (CPC), in a single-threading running manner. CONCLUSIONS: PLEK is an efficient alignment-free computational tool to distinguish lncRNAs from mRNAs in RNA-seq transcriptomes of species lacking reference genomes. PLEK is especially suitable for PacBio or 454 sequencing data and large-scale transcriptome data. Its open-source software can be freely downloaded from https://sourceforge.net/projects/plek/files/.

This paper develops a novel integral sliding mode controller (ISMC) for a general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured velocities, unknown disturbances, and uncertain hydrodynamics of the robot have been successfully solved in the control design. An adaptive MIMO-ESO is designed not only to estimate the unmeasurable linear and angular velocities, but also to estimate the unknown external disturbances. An ISMC is then designed using Lyapunov synthesis, and an adaptive gain update algorithm is introduced to estimate the upper bound of the uncertainties. Rigorous theoretical analysis is performed to show that the proposed control method is able to achieve asymptotical tracking performance for the underwater robot. Experimental studies are also carried out to validate the effectiveness of the proposed control, and to show that the proposed approach performs better than a conventional potential difference (PD) control approach.

Piezoelectric nanogenerators with large output, high sensitivity, and good flexibility have attracted extensive interest in wearable electronics and personal healthcare. In this paper, the authors propose a high‐performance flexible piezoelectric nanogenerator based on piezoelectrically enhanced nanocomposite micropillar array of polyvinylidene fluoride‐trifluoroethylene (P(VDF‐TrFE))/barium titanate (BaTiO 3 ) for energy harvesting and highly sensitive self‐powered sensing. By a reliable and scalable nanoimprinting process, the piezoelectrically enhanced vertically aligned P(VDF‐TrFE)/BaTiO 3 nanocomposite micropillar arrays are fabricated. The piezoelectric device exhibits enhanced voltage of 13.2 V and a current density of 0.33 µA cm −2 , which an enhancement by a factor of 7.3 relatives to the pristine P(VDF‐TrFE) bulk film. The mechanisms of high performance are mainly attributed to the enhanced piezoelectricity of the P(VDF‐TrFE)/BaTiO 3 nanocomposite materials and the improved mechanical flexibility of the micropillar array. Under mechanical impact, stable electricity is stably generated from the nanogenerator and used to drive various electronic devices to work continuously, implying its significance in the field of consumer electronic devices. Furthermore, it can be applied as self‐powered flexible sensor work in a noncontact mode for detecting air pressure and wearable sensors for detecting some human vital signs including different modes of breath and heartbeat pulse, which shows its potential applications in flexible electronics and medical sciences.

Lightweight magnesium alloys are attractive as structural materials for improving energy efficiency in applications such as weight reduction of transportation vehicles. One major obstacle for widespread applications is the limited ductility of magnesium, which has been attributed to [Formula: see text] dislocations failing to accommodate plastic strain. We demonstrate, using in situ transmission electron microscope mechanical testing, that [Formula: see text] dislocations of various characters can accommodate considerable plasticity through gliding on pyramidal planes. We found that submicrometer-size magnesium samples exhibit high plasticity that is far greater than for their bulk counterparts. Small crystal size usually brings high stress, which in turn activates more [Formula: see text] dislocations in magnesium to accommodate plasticity, leading to both high strength and good plasticity.

Abstract. Land-cover change has been identified as an important cause or driving force of global climate change and is a significant research topic. Over the past few decades, global land-cover mapping has progressed; however, long-time-series global land-cover-change monitoring data are still sparse, especially those at 30 m resolution. In this study, we describe GLC_FCS30D, a novel global 30 m land-cover dynamics monitoring dataset containing 35 land-cover subcategories and covering the period 1985–2022 in 26 time steps (maps were updated every 5 years before 2000 and annually after 2000). GLC_FCS30D has been developed using continuous change detection and all available Landsat imagery based on the Google Earth Engine platform. Specifically, we first take advantage of the continuous change-detection model and the full time series of Landsat observations to capture the time points of changed pixels and identify the temporally stable areas. Then, we apply a spatiotemporal refinement method to derive the globally distributed and high-confidence training samples from these temporally stable areas. Next, local adaptive classification models are used to update the land-cover information for the changed pixels, and a temporal-consistency optimization algorithm is adopted to improve their temporal stability and suppress some false changes. Further, the GLC_FCS30D product is validated using 84 526 globally distributed validation samples from 2020. It achieves an overall accuracy of 80.88 % (±0.27 %) for the basic classification system (10 major land-cover types) and 73.04 % (±0.30 %) for the LCCS (Land Cover Classification System) level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series datasets used for validation from the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that GLC_FCS30D offers significant stability in terms of variation across the accuracy time series and achieves mean accuracies of 79.50 % (±0.50 %) and 81.91 % (±0.09 %) over the two regions. Lastly, we draw conclusions about the global land-cover-change information from the GLC_FCS30D dataset; namely, that forest and cropland variations have dominated global land-cover change over past 37 years, the net loss of forests reached about 2.5 million km2, and the net gain in cropland area is approximately 1.3 million km2. Therefore, the novel dataset GLC_FCS30D is an accurate land-cover-dynamics time-series monitoring product that benefits from its diverse classification system, high spatial resolution, and long time span (1985–2022); thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D dataset is available via https://doi.org/10.5281/zenodo.8239305 (Liu et al., 2023).

Abstract This study reports the synthesis of ultrafine NiFe‐layered double hydroxide (NiFe‐LDH) nanosheets, possessing a size range between 1.5 and 3.0 nm with a thickness of 0.6 nm. Abundant metal and oxygen vacancies impart the ultrafine nanosheets with semi‐metallic character, and thus superior charge transfer properties and electrochemical water oxidation performance with overpotentials (η) of 254 mV relative to monolayer LDH nanosheets (η of 280 mV) or bulk LDH materials (η of 320 mV) at 10 mA cm −2 . These results are highly encouraging for the future application of ultrafine monolayer LDH nanosheets in electronics, solar cells, and catalysis.

The “environmental pollution–economic development” circle is a problem in the process of national sustainable development. As a complex concept of environmental protection and technology innovation, green technology innovation is the key to cracking this strange circle. This paper divides green technology innovation into green product innovation and green process innovation and measures green technology innovation based on the perspective of energy saving and emission reduction. Furthermore, we examine the effects of environmental regulation and government R&D funding on green technology innovation. The empirical findings are as follows: (1) from the dynamic point of view, we test whether there is a significant ”U-shaped” relationship between environmental regulation and green technological innovation, and we find there exists an “inflection point” in the role of environmental regulation in green technology innovation, and China is at the stage of inhibition before the “inflection point”; (2) direct government funding and tax incentives can promote green technology innovation, but the promotion of government tax incentives to green technology innovation is not significant; (3) the interaction between environmental regulation and government R&D will promote green product innovation and inhibit green process innovation, which is closely related to the imbalance of environmental regulation intensity in energy saving and emission reduction. In addition, this paper also gives out three kinds of control variables (the level of regional development, the proportion of the regional manufacturing industry, and the development level of regional export-oriented economy) and presents their effects on green technology innovation.

Abstract By integrating sensing, memory and processing functionalities, biological nervous systems are energy and area efficient. Emulating such capabilities in artificial systems is, however, challenging and is limited by the device heterogeneity of sensing and processing cores. Here we report an organic electrochemical transistor capable of sensing, memory and processing. The device has a vertical traverse architecture and a crystalline–amorphous channel that can be selectively doped by ions to enable two reconfigurable modes: a volatile receptor and a non-volatile synapse. As a volatile receptor, the device is capable of multi-modal sensing and is responsive to stimuli such as ions and light. As a non-volatile synapse, it is capable of 10-bit analogue states, low switching stochasticity and good state retention. We also show that the homogeneous integration of the devices could provide functions such as conditioned reflexes and could be used for real-time cardiac disease diagnoses via reservoir computing.

Automatic path planning problem is essential for efficient mission execution by unmanned aerial vehicles (UAVs), which needs to access the optimal path rapidly in the complicated field. To address this problem, a novel hybrid particle swarm optimization (PSO) algorithm, namely, SDPSO, is proposed in this article. The proposed algorithm improves the update strategy of the global optimal solution in the PSO algorithm by merging the simulated annealing algorithm, which enhances the optimization ability and avoids falling into local convergence; each particle integrates the beneficial information of the optimal solution according to the dimensional learning strategy, which reduces the phenomenon of particles oscillation during the evolution process and increases the convergence speed of the SDPSO algorithm. The simulation results show that compared with PSO, dynamic-group-based cooperative optimization (DGBCO), gray wolf optimizer (GWO), RPSO, and two-swarm learning PSO (TSLPSO), the SDPSO algorithm can quickly plan higher quality paths for UAVs and has better robustness in complex 3-D environments.

Abstract Perovskite nanocrystals are attracting great interest due to their excellent photonic properties. Here, through a supramolecular self‐assembly approach, the perovskite nanocrystals (NCs) with a novel circularly polarized luminescence (CPL) are successfully endowed. It is found that the achiral perovskite NCs can coassemble with chiral gelator in nonpolar solvents, in which the gelator molecules modify the surface of the perovskite NCs. Through such cogelation, the molecular chirality can transfer to the NCs resulting in CPL signals with a dissymmetric factor ( g lum ) up to 10 −3 . Furthermore, depending on the molecular chirality of the gelator, the CPL sense can be selected and the mirror‐imaged CPL is obtained. Such gels can be further embedded into the polymer film to facilitate flexible CPL devices. It is envisaged that this approach will afford a new insight into the designing of the functional chiroptical materials.

Despite the significant progresses made in all-polymer solar cells (all-PSCs) recently, the relatively low short-circuit current density ( J sc ) and large energy loss are still quite difficult to overcome for further development. To address these challenges, we developed a new class of narrow-bandgap polymer acceptors incorporating a benzotriazole (BTz)-core fused-ring segment, named the PZT series. Compared to the commonly used benzothiadiazole (BT)-containing polymer PYT, the less electron-deficient BTz renders PZT derivatives with significantly red-shifted optical absorption and up-shifted energy levels, leading to simultaneously improved J sc and open-circuit voltage in the resultant all-PSCs. More importantly, a regioregular PZT (PZT-γ) has been developed to achieve higher regiospecificity for avoiding the formation of isomers during polymerization. Benefiting from the more extended absorption, better backbone ordering, and more optimal blend morphology with donor component, PZT-γ-based all-PSCs exhibit a record-high power conversion efficiency of 15.8% with a greatly enhanced J sc of 24.7 mA/cm 2 and a low energy loss of 0.51 eV.

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).

The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification algorithms, convolution neural network (CNN) plays an important role. However, the classical CNN has the problem of consuming too much computing resources. To solve this problem, first, this paper proposed a dilated CNN model which is built through replacing the convolution kernels of traditional CNN by the dilated convolution kernels, and then, the dilated CNN model is tested on the Mnist handwritten digital recognition data set. Second, to solve the detail loss problem in the dilated CNN model, the hybrid dilated CNN (HDC) is built by stacking dilated convolution kernels with different dilation rates, and then the HDC model is tested on the wide-band remote sensing image data set of earth's terrain. The results show that under the same environment, compared with the traditional CNN model, the dilated CNN model reduces the training time by 12.99% and improves the training accuracy by 2.86% averagely, compared with the dilated CNN model, the HDC model reduces the training time by 2.02% and improves the training and testing accuracy by 14.15% and 15.35% averagely. Therefore, the dilated CNN and HDC model proposed in this paper can significantly improve the image classification performance.

As a powerful and promising statistical signal modeling technique, sparse representation has been widely used in various image processing and analysis fields. For hyperspectral image classification, previous studies have shown the effectiveness of the sparsity-based classification methods. In this paper, we propose a nonlocal weighted joint sparse representation classification (NLW-JSRC) method to improve the hyperspectral image classification result. In the joint sparsity model (JSM), different weights are utilized for different neighboring pixels around the central test pixel. The weight of one specific neighboring pixel is determined by the structural similarity between the neighboring pixel and the central test pixel, which is referred to as a nonlocal weighting scheme. In this paper, the simultaneous orthogonal matching pursuit technique is used to solve the nonlocal weighted joint sparsity model (NLW-JSM). The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparsity-based algorithms and the classical support vector machine hyperspectral classifier.

Advances in nanotechnology and nanomaterials have attracted considerable interest and play key roles in scientific innovations in diverse fields. In particular, increased attention has been focused on carbon-based nanomaterials exhibiting diverse extended structures and unique properties. Among these materials, zero-dimensional structures, including fullerenes, carbon nano-onions, carbon nanodiamonds, and carbon dots, possess excellent bioaffinities and superior fluorescence properties that make these structures suitable for application to environmental and biological sensing, imaging, and therapeutics. This review provides a systematic overview of the classification and structural properties, design principles and preparation methods, and optical properties and sensing applications of zero-dimensional carbon nanomaterials. Recent interesting breakthroughs in the sensitive and selective sensing and imaging of heavy metal pollutants, hazardous substances, and bioactive molecules as well as applications in information encryption, super-resolution and photoacoustic imaging, and phototherapy and nanomedicine delivery are the main focus of this review. Finally, future challenges and prospects of these materials are highlighted and envisaged. This review presents a comprehensive basis and directions for designing, developing, and applying fascinating fluorescent sensors fabricated based on zero-dimensional carbon nanomaterials for specific requirements in numerous research fields.

Layered α-Ni(OH) 2 and its derivative bimetallic hydroxides (e.g., α-(Ni/Co)(OH) 2 ) have attracted much attention due to their high specific capacitance, although their insufficient cycling stability has blocked their wide application in various technologies. In this work, we demonstrate that the cycling performance of α-(Ni/Co)(OH) 2 can be obviously enhanced via the intrinsic pillar effect of metaborate. Combining the high porosity feature of the metaborate stabilized α-(Ni/Co)(OH) 2 and the improved electronic conductivity offered by graphene substrate, the average capacitance fading rate of the metaborate stabilized α-(Ni/Co)(OH) 2 is only ∼0.0017% per cycle within 10 000 cycles at the current density of 5 A g –1 . The rate performance is excellent over a wide temperature range from −20 to 40 °C. We believe that the enhancements should mainly be ascribed to the excellent structural stability offered by the metaborate pillars, and the detailed mechanism is discussed.

A novel kind of quantum dots, sulfur quantum dots (S dots), is synthesized by simply treating sublimated sulfur powders with alkali using polyethylene glycol-400 as passivation agents. The synthesized S dots exhibit excellent aqueous dispersibility, eminent photostability and temperature dependent photoluminescence (PL). An "assemble-fission" mechanism is proposed for the S dots formation in which "assembling" and "fission" are involved and contest each other. The ultimate morphologies of the S dots are dependent on the balance of the two forces. Guided by the assemble-fission mechanism, weakening the assembling effect is beneficial for obtaining monodisperse S dots, which can be achieved by pretreating of sulfur powder with nitric acid. PL wavelength of the S dots has been successfully tuned between green and blue light (from 550 to 440 nm) by simply controlling reaction time. A satisfactory quantum yield of 3.8% is obtained. Significant electrochemiluminescence of the S dots is observed in an annihilation reaction. Chemiluminescence from the S dots has been observed by direct oxidation. Taking advantage of unique and inherent antimicrobial activity of the sulfur particles, it is believed that this new emerging luminescent nanomaterial is highly promising in the development of new types of optoelectronic devices and tracer for live cells, in vivo imaging and diagnostics.

As an effective method, heteroatom doping is widely used to improve the electrochemical performance of carbon materials. However, the influence of oxygen-containing functional groups in carbon materials is often neglected. Therefore, we use buckwheat hulls as the precursor to prepare oxygen-doped hard carbon by simple carbonization. The buckwheat hull at a pyrolysis temperature of 1100 °C has the highest reversible capacity of 400 mA h g –1 at 50 mA g –1, and the capacity can maintain 96% of the initial capacity after 3000 cycles at 2A g –1 . These results confirm that the natural pore structure and proper interlayer spacing of the BPC-1100 contribute to the transport and insertion of sodium ions. In addition, the first principle proves that the role of oxygen atoms cannot be ignored in the storage of sodium ions. In particular, the improvement of the C═O bond is helpful to improve the adsorption capacity of hard carbon to sodium ions and enhance the reversible capacitance.