Search

Alcoholysis is a promising approach for converting biomass into fuels and/or chemicals under mild conditions. However, the effect of pretreatment on biomass alcoholysis was rarely reported. Herein, the effect of pretreatment with H2O2 on sweet sorghum stalk (SSS) methanolysis was examined. The results show that the pretreatment could markedly improve the bio-oil (BO) yield and decrease the appropriate temperature for obtaining maximum BO yield. The appropriate temperature for pretreated SSS methanolysis was determined to be 280 °C and the maximum BO yield is 44 wt%. In addition, higher heating values of the BOs were also enhanced based on elemental analysis. According to analysis with gas chromatograph/mass spectrometer, phenolic compounds, esters, and sugars are predominant in the BOs, and the yield of phenolic compounds significantly increased from 91.75 to 111.68 mg . g−1 by the pretreatment. Moreover, polar species in the BOs decreased and deoxygenation occurred during pretreated SSS methanolysis. Analyses with scanning electron microscope and N2 physisorption reveal that pretreated SSS has more grooves and higher specific surface area and anomalous porosity than SSS. According to analyses with Fourier transform infrared spectrometer and X-ray photoelectron spectrometer, oxygen functional groups mainly in the forms of C O and COO were introduced into SSS by the pretreatment. The changes of physical and chemical structures should be responsible for enhancing SSS methanolysis and property of the BO.

Deep learning classification based on 3D point clouds has gained considerable research interest in recent years.The classification and quantitative analysis of wood defects are of great significance to the wood processing industry. In order to solve the problems of slow processing and low robustness of 3D data. This paper proposes an improvement based on littlepoint CNN lightweight deep learning network, adding BN layer. And based on the data set made by ourselves, the test is carried out. The new network bnlittlepoint CNN has been improved in speed and recognition rate. The correct rate of recognition for non defect log, non defect log and defect log as well as defect knot and dead knot can reach 95.6%.Finally, the "dead knot" and "loose knot" are quantitatively analyzed based on the "integral" idea, and the volume and surface area of the defect are obtained to a certain extent,the error is not more than 1.5% and the defect surface reconstruction is completed based on the triangulation idea.

In traditional flood frequency analysis, a minimum of 30 observations is required to guarantee the accuracy of design results with an allowable uncertainty, however, there has not been a recommendation for the requirement on the length of data in NFFA (nonstationary flood frequency analysis). Therefore, this study has been carried out with three aims: (i) to evaluate the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths; (ii) to examine the impacts of flood record lengths on the NS and ST design floods and associated uncertainties; and (iii) to recommend the probable requirements of flood record length in NFFA. To achieve these objectives, 20 stations with record length longer than 100 years in Norway were selected and investigated by using both GEV (generalized extreme value)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results indicate that the fitting quality and predictive capabilities of GEV-NS0 outperform those of GEV-ST models when record length is approximately larger than 60 years for most stations, and the stability of the GEV-ST and GEV-NS0 is improved as record lengths increase. Therefore, a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway. HIGHLIGHTS This study evaluates the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths.; This study examines the impacts of flood record lengths on the NS and ST design floods and associated uncertainties.; A minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway.

Curved twin I-girder composite bridge (TGCB) is becoming popular in Chinese highway bridge building. To study its ultimate bearing behavior, in this paper, one 1 : 5 scale intact model of a two-span curved continuous TGCB was tested to failure to evaluate its safety reserve and ductility. Afterwards, based on the experimental result, 3D FE models were developed and validated. At last, using the validated 3D FE models, the effect of construction scheme, radius of curvature, yield strength of steel, concrete compressive strength, crossbeams, and bottom lateral bracings on the ultimate bearing capacity were examined. The experimental results showed that the ultimate load (Pu) is approximate 13.6 times the service equivalent load. The cracking load and yielding load are approximately 0.12 and 0.47 Pu, respectively. The ductility coefficients are 4.06∼4.40. These above may indicate that the TGCB designed according to Chinese codes has good safety reserve and ductility. From parameter analysis results, it was concluded that the TGCB with full-support construction scheme has larger yield load and ultimate load compared with the one with erecting machine construction scheme. On the other hand, the ultimate bearing capacity reduces nonlinearly with the increase of curvature. Besides, the yield strength of steel, crossbeams, and bottom lateral bracings has a significant effect on the ultimate bearing capacity of curved TGCB. And the smaller the radius of curvature, the more obvious the effect of the latter two factors is. Unfortunately, it is unwise to continuous to improve the ultimate load by increasing the grade of steel for the TGCB when steel grade exceeds Q390. Moreover, in consideration of the big difference in bearing capacity between the inner girder and outer girder of the TGCB with small radius of curvature as well as the economy, it is suggested that the inner and outer steel girders of that TGCB should be designed differently.

This article proposes a model-free lossless data compression method for time series in smart grids (SGs), namely, Lossless Coding considering Precision (LCP) method. The LCP method encodes the current datapoint only using the immediate previous datapoint by differential coding, XOR coding, and variable length coding and transmits the encoded data once generated. It does not use the dynamics (e.g., many previous datapoints) or prior knowledge (e.g., mathematical models) of the time series. It considers the patterns, potential applications, and associated precision to preprocess the time series and especially suits high-resolution time series with long steady periods. The LCP method features low-latency and generalizability which enables real-time data communication for different time-critical tasks. Sub-metered load profiles in REDD dataset, high-resolution LIFTED dataset, AMPds dataset and PMU dataset are used to evaluate the performance of the LCP method. The results show that the LCP method demonstrates high compression ratio, low latency, and low complexity compared to state-of-the-art Resumable Data Compression (RDC) method, DEFLATE based on LZ77 & Huffman coding, and Lempel-Ziv-Markov Chain Algorithm (LZMA).

Underwater acoustic communications (UACs) are demanded in a wide range of marine applications. However, one of the main challenges for UACs is unexpected interferences from nearby acoustic activities, especially dynamic and short-period interferences (called shot interference), which are hard to detect, estimate, and mitigate over time- and frequency-selective acoustic channels. In this article, we propose a novel filter that mitigates the interference effects for both single-carrier and multi-carrier systems without any prior knowledge of the interference. An Adaptive Sliding Window Interference Detection (ASWID) algorithm is developed to detect the location and the number of interfered symbols. Meanwhile, a least trimmed squares (LTS) equalizer is presented for the shot interference estimation and mitigation based on robust regression. The proposed algorithms are evaluated through numerical simulations and real channel measurements. Our results show that the proposed designs can detect and mitigate shot interferences effectively for single-carrier and multi-carrier UAC systems.

Residual stress and deformation are common issues which prevent dimensional accuracy and lead early fatigue of products by laser metal deposition process. Finite element analysis is an efficient means to estimate the temperature history and thermal stress distribution. However, it is extremely challenging to predict thermal history and stress distribution of a practical large and complex geometry if each laser pulse is taken into account as by conventional laser pulse modeling. Therefore, in this study, an efficient predictive finite element model with assumptions on the laser heat source and loading is established to study the evolution of thermal history, residual stresses, and deformations of a test coupon. The efficient predictive model, which is also called as the 2-layer by 2-layer model, simulates two layers at each laser pulse time. This model is compared with conventional laser pulse model in terms of the evolution of thermal history of selected points and residual stresses. Results show that the 2-layer by 2-layer model considerably reduces the simulation time without much compromising the accuracy of the prediction of deformation and thermal stress. In addition, test coupon is designed and fabricated to capture temperature history and observe microstructure change. It is found that microstructure presents certain correlation with cooling rate. Microstructure and residual stress of the test coupon are evaluated and found to be consistent with the prediction by proposed model. This efficient predictive model can shed light on large-scale part fabrication by laser metal deposition process in industry.

Hydroliquefaction behavior of preasphaltenes, derived from direct coal liquefaction, was carried out in a 30 mL autoclave with FeS + S catalyst and tetralin at initial hydrogen of 5.0 MPa, residence time of 0–60 min and reaction temperature of 380–440°C in order to optimize the conditions of direct coal liquefaction and improve oil yield. The products distribution and kinetic parameters of preasphaltenes catalytic hydroliquefaction were investigated. A new kinetic model was established to simulate the preasphalteneshydroliquefaction catalyzed by FeS + S catalyst using lump kinetic model. It was found that preasphaltenes were hydroliquefaction into asphaltenes and char directly, and then asphaltenes were hydrocracked into oil + gas products. Regressive reactions of preasphaltenes to char and asphaltenes to preasphaltenes occurred at higher temperatures. Higher temperature and longer time were favorable for increasing the conversion of preasphaltenes and the oil + gas yield. The hydroliquefaction of preasphaltenes under 440°C and 60 min reached 79.45% with 34.7% of oil + gas yield. The hydroliquefaction conversions calculated from the model agreed well with the experimental data, and the activation energies ranged within 50–245 kJ/mol.

Nitrogen-doped lignin-based carbon microspheres are synthesized using 3-aminophenol as a nitrogen source by the hydrothermal method. The structural change and the effect on the electrochemical properties are systematically investigated. Nitrogen-doped lignin-based carbon microspheres represent well-developed spherical morphology with many active sites, ultramicroporous (< 0.7 nm) structure, and large interlayer spacing. Consistent with the obtained physical structures and properties, the nitrogen-doped carbon microspheres exhibit fast sodium ion adsorption/intercalation kinetic process and excellent electrochemical performance. For example, a reversible specific capacity of 374 mAh g−1 at 25 mA g−1 with high initial coulombic efficiency of 85% and high capacitance retention of 90% after 300 cycles at 100 mA g−1 and stable charge/discharge behavior at different current density is obtained. The additional defects and abundant ultramicroporous structure can enhance sloping capacity, and large interlayer spacing is considered to be the reason for improving plateau capacity.

Laser-assisted tape placement and tape winding represent the most advanced automated manufacturing technologies for thermoplastic composites. It is important to accurately predict the complete thermal history of the manufacturing process, on which many properties of the product are strongly dependent, such as interlaminar bonding strength and crystallization. In this article, a nonlinear two-dimensional transient heat transfer model was established in ANSYS APDL, and a new temperature closed-loop controlled near-infrared laser heat source was described. Most importantly, the thermal contact pairs were introduced following the activation of elements, and the value of interlaminar thermal contact resistance was obtained by fitting an independent experiment and finite element analysis, which greatly optimized the heat transfer process in the direction of thickness. The thermal model was verified by infrared cameras and an online temperature measuring system based on thermocouples. The results of thermal history are in good agreement with the experiment, which were input into the crystallization kinetics model to predict the evolution and distribution of crystallinity in the laser-assisted tape winding process. The calculated results are coincident with the differential scanning calorimeter test very well, and the crystallinity was about 17% higher than that before manufacturing.

Load data is an essential input for power system analysis and management. However, traditional load analysis methods do not fit well with emerging deep learning models that may require image matrix as input, such as Convolutional Neural Network (CNN) and Generative Adversarial Nets (GAN). This article proposes a novel analysis method “Load Photo” to create the required image matrix for various loads in power systems by using the HSV color space. A load photo is a 2-D pixel graphical representation of load, in which the x-axis represents sampling time points (e.g., hours) for every day from Sunday to Saturday, the y-axis represents weeks from the first week till the last week, and the pixel color demonstrates the normalized load. A load photo stores all essential load-related parameters such as start time, end time, and sampling period. A load photo can be used to characterize the energy consumption pattern of any load type, just like taking a photo for the load that clearly demonstrates the load’s behavior. Furthermore, a load photo could work before feature selection as an input to deep learning models. Accordingly, the latest advancement in deep learning models related to image processing can be easily applied to the analysis and data mining of load data.

Low-cost, long-endurance navigation is a crucial universal supporting technology for long-range underwater vehicles while entering, exploring, and developing the deep sea. To overcome the limitations of existing navigation systems (e.g., high cost and unsuitability for long-endurance operation), this study proposes a cooperative navigation system based on “light” marine equipment such as wave glider, underwater acoustic communication modem (UACM), and micro-electro-mechanical system (MEMS) sensor. The focus was to resolve the crucial technical problems faced by the above mentioned “light” marine equipment during deep integration. First, the wave glider path planning based on deep reinforcement learning is studied to ensure continuous and reliable information interaction between the wave glider and underwater vehicle. Second, underwater acoustic communication one-way ranging based on a sound velocity profile (SVP) coupling model and a constant gradient SVP fast algorithm is studied to achieve fast and accurate estimation of the horizontal range between the wave glider and underwater vehicle. Finally, UACM-MEMS sensor cooperative navigation based on decision feedback-denoising auto-encoding and adaptive Kalman filter is studied to ensure the accuracy of navigation. Through deep integration of each “light” marine equipment, low-cost, long-endurance cooperative navigation can be realized in the deep sea.

PurposeDuring the powder bed fusion process, thermal distortion is one big problem owing to the thermal stress caused by the high cooling rate and temperature gradient. For the purpose of avoiding distortion caused by internal residual stresses, support structures are used in most selective laser melting (SLM) process especially for cantilever beams because they can assist the heat dissipation. Support structures can also help to hold the work piece in its place and reduce volume of the printing materials. The mitigation of high thermal gradients during the manufacturing process helps to reduce thermal distortion and thus alleviate cracking, curling, delamination and shrinkage. Therefore, this paper aims to study the displacement and residual stress evolution of SLMed parts.Design/methodology/approachThe objective of this study was to examine and compare the distortion and residual stress properties of two cantilever structures, using both numerical and experimental methods. The part-scale finite element analysis modeling technique was applied to numerically analyze the overhang distortions, using the layer-by-layer model for predicting a part scale model. The validation experiments of these two samples were built in a SLM platform. Then average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model.FindingsThe validation experiments results of average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model. It was found that they matched well with each other. From displacement and residual stress standpoint, by introducing two different support structure, two samples with the same cantilever beam can be successfully printed. In terms of reducing wasted support materials, print time and high surface quality, sample with less support will need less post-processing and waste energy.Originality/valueNumerical modeling in this work can be a very useful tool to parametrically study the feasibility of support structures of SLM parts in terms of residual stresses and deformations. It has the capability for fast prediction in the SLMed parts.

A new approach referred as “the network modeling method” was developed by the authors to analyze the behaviors of marine structures. In this paper the method is briefly described and applied to predict the loads acting on the connectors between the two modules of the Scientific Research and Demonstration Platform (SRDP), which was deployed in a complicated wave environment near islands and reefs in South China Sea. Based on this method, the response amplitude operators (RAOs) of the connector loads of the SRDP in regular waves, and the time variations of the connector loads of the SRDP in an on-site measured random sea state are predicted and presented. The significant stresses at 20 spots of the local connection structure induced by the connector loads in the sea state are further calculated. The comparisons between the predicted and the on-site measured stresses confirm that the network modeling method is feasible to some extent and especially useful for design of the connectors’ arrangement, estimation of the connector loads and the related structural safety of a multi-module floating structure in early design stage.

Most oligonucleotides fail to enter a cell and cannot escape from endosomes after endocytosis because of their negative charge and large molecular weight. More efficient cellular delivery of oligonucleotides should be developed for the widespread implementation of antisense imaging. The purpose of this study was to construct a novel antisense nanoprobe, 99mTc-labeled anti-miRNA oligonucleotides/cell-penetrating peptide PepFect6 (99mTc-AMO/PF6), and to evaluate its efficacy for imaging the miRNA-21 expression in A549 lung adenocarcinoma xenografts. Naked AMO and commercial Lipofectamine 2000-based nanoparticles (AMO/LIP) were used for comparison. The cellular delivery efficiency of AMO/PF6 was first investigated by laser confocal scanning microscopy using Cy5.5-labeled probes and further validated by in vivo fluorescence imaging. Then, the probes were labeled with 99mTc via hydrazinonicotinamide (HYNIC). The cytotoxicity assay, cellular uptake, and retention kinetics of the probes were evaluated in vitro. The biodistribution of the probes was investigated in A549 lung cancer xenografts, and SPECT imaging was performed in vivo. AMO/PF6 showed lower cytotoxicity than AMO/LIP (P < 0.05) but showed no significant difference with naked AMO. Fluorescence microscopy demonstrated more extensive and scattered signal distribution inside the A549 cells by AMO/PF6 than AMO/LIP. The labeling efficiency of 99mTc-AMO/PF6 was 72.6 ± 1.42%, and the specific activity was 11.6 ± 0.13 MBq/ng. The cellular uptake of 99mTc-PF6/AMO peaked at 12 h, with the uptake of 11.24 ± 0.12 mol/cell × 10-16, and the cellular retention of 99mTc-AMO/PF6 was 3.92 ± 0.15 mol/cell × 10-16 at 12 h after interrupted incubation. AMO/PF6 showed higher cellular uptake and retention than naked AMO and AMO/LIP. The biodistribution study showed that the tumor had the highest radioactivity accumulation, with the uptake ratio of tumor/muscle (T/M) increasing from 14.59 ± 0.67 to 21.76 ± 0.98 between 1 and 6 h after injection, followed by the uptake in the kidneys and the liver. The results of in vivo fluorescence and SPECT imaging were consistent with the results of the biodistribution. The tumor was visualized at 6 h after injection of AMO/PF6 with the highest T/M ratio among these probes (P < 0.05). PF6 improves cellular delivery of antisense oligonucleotides via noncovalent nanoparticles. 99mTc-AMO/PF6 shows favorable imaging properties and is promising for miRNAs imaging in vivo.

Diesel engines are widely used in railway systems, particularly in freight trains. Despite their high efficiency in energy conversion, they usually generate high levels of acoustics pollution during operation. In order to mitigate this problem, a series of active/passive acoustics control methods are used to reduce noise. Most of these methods are only effective if the prior knowledge of sources is given. In other words, it is essential to recognize the acoustics source. Variational mode decomposition (VMD) is a signal processing method that enhances the signal corrupted by background noise. However, the decomposed results of VMD depend on their mode parameter and penalty parameter. Therefore, an evaluation method based on system modal parameters (natural frequency and damping ratio) is proposed to select the mode parameter, and the penalty parameter can be selected from the power spectra of signals. In order to increase the accuracy of decomposition for diesel engines and find out the sources of acoustics, a method combining VMD, fast independent component analysis, and Hilbert transformation (VMD-FastICA-HT) is proposed for the separation and identification of different sources for diesel engines. The optimization results indicate that when the penalty parameter value is 1.5 to 16 times the maximum signal amplitude, better decomposition results can be achieved. Therefore, the separated independent acoustics are more accurate in source identification. Furthermore, both simulation data and in situ operational data of diesel engines for vehicles are used to validate the effectiveness of the proposed method.

Multi-parent crossover algorithms (MCAs) are widely used in solving optimization problems in many fields relying on encoding, crossover, variation and choice operators to produce iterative offspring chromosome. In this paper, a real-coded schema to support this genetic optimization process is considered. At each crossover stage, a linear combination of coefficients at the same scale hybridizes a fixed number of parent chromosomes. If parent chromosomes are iteratively selected, then coefficients will indicate these parent chromosomes: how to propagate. Essentially, all MCAs differentiate one algorithm from others through coefficient restriction. However, little work exists on analyzing techniques that efficiently create within-bound coefficients. The existing MCAs build coefficients following a uniform distribution, but at the same time, these coefficients violate constraints, thus propagating error. The error will cascade exponentially as the hybrid scale rises even slightly, leading to increased time consumption. To address this problem, an empirical distribution-based framework (EDBF) is proposed which takes multiple MCAs as its constituent members. Numerical results showed that proposed EDBF outperforms its members in terms of time consumption. As a general framework rather than a specific algorithm, EDBF is easy to implement and can easily accommodate any existing MCA.

With the development of electric vehicles, more and more attention has been paid to the kinetic performance of batteries, which is related to rapid charge/discharge and safety issues. To improve this aspect, Cu2Nb34O87 nanowires covered by nitrogen and sulfur co-doped carbon (Cu2Nb34O87/NSC nanowires) are prepared by electrospinning combined with surface coating. As-prepared Cu2Nb34O87/NSC nanowires present a high ion diffusion coefficient and electronic conductivity, showing good a kinetic performance. Specifically, they deliver a splendid capacity of 311.2 mA h g−1 at 1 C and a superior cycling stability with a capacity fading of 0.031% per cycle upon 1000 cycles. At the same time, the electrochemical and structural reversibility is fully discussed and demonstrated by ex situ XRD, ex situ SEM and ex situ XPS based on the redox couples of Cu2+/Cu+, Nb5+/Nb4+, and Nb4+/Nb3+. Contributing to peculiar physico-chemical properties, Cu2Nb34O87/NSC nanowires are expected to be a candidate material for the anode in lithium-ion batteries.

Polymer-based dielectric films with desirable heat dissipation property hold great promise as thermal management materials in advanced electronics and high power devices. Herein, we report a new st...

In order to investigate the mechanical behavior, ultimate load carrying capacity, and failure mode of the intact curved continuous twin I-girder composite bridge (TGCB) with a precast concrete slab, one curved continuous composite bridge model with a scale ratio of 1 : 5 of a prototype bridge was designed and manufactured considering the influence of the construction sequence. Four symmetric point loads’ test was carried out. In this paper, load-deflection relationship and strain development of steel girders, concrete slab, and reinforcement at key sections were tested and analyzed. Failure mode, crack development, and major crack width at the top surface of the concrete slab in the hogging moment region were also reported. The experimental results demonstrated that the load capacity under the initial cracking level, cracking level with the width of 0.2 mm, and steel girder yielding state is about 1.7, 5.0, and 6.3 times of the design load, respectively. Due to the influence of curvature, the stiffness of the external girder is less than that of the internal girder. However, the ultimate bearing capacity is basically the same, approximately 13.6 times of the design load. During the loading process, plastic hinge was first observed at the intermediate support section as a result of the hogging moment which should be emphasized in design. The local buckling took place after yielding, indicating a class 2 section according to Eurocode 4. In addition, the TGCB had good ductility since the displacement ductility coefficients of the external and internal girders were 4.40 and 4.06, respectively.

Phenol···phenol, phenol···benzene, phenol···oxydibenzene, phenol···quinoline, and benzoic acid···benzoic acid were selected as lignite-related complexes to investigate different hydrogen bonds formed by self-associated OH, OH−π,OH−ether O, OH−N, and COOH−COOH using density functional theory with dispersion correction, respectively. Moreover, the effects of substituents (CH3−, CH3O−, OH−, NH2−, COOH−, and NO2−) in donors on the hydrogen bonds were investigated. Geometry optimization, energy, Mulliken population, and frequency of all thecomplexes were calculated. It can be seen from optimized structures that there indeed arehydrogen bonds in the different complexes. Bond lengths of all O−H bonds in the different complexes become longer than that of free OH in phenol, which implies that intermolecular interactions exist in all the complexes. Among of them, bond lengths of O−H bonds in benzoic acid···benzoic acid are the longest. In addition, charge transfer can be observed via Mulliken population. Based on frequency analysis, all O−H stretching vibrations have obvious red shift, especially O−H bonds in benzoic acid···benzoic acid and phenol···quinoline, which gives evidence of using the infrared spectroscopyto analyze hydroxyl groups of coals. According to bond energies, the strength of the different hydrogen bonds decreases in the order: COOH−COOH > OH−N > self-associated OH ≈ OH−ether O > OH−πwhich is consistent with the reported experimental results. Different substituents have distinct effects on thehydrogen bonds.

Hypercytokines cause acute respiratory distress syndrome (ARDS) in coronavirus disease 2019 (COVID-19) patients, which is the main reason for intensive care unit treatment and the leading cause of death in COVID-19 patients. Cytokine storm is a critical factor in the development of ARDS. This study evaluated the efficacy and safety of Oxiris filter in the treatment of COVID-19 patients. Five patients with COVID-19 who received continuous renal replacement therapy (CRRT) in Henan provincial people's hospital between January 23, 2019 and March 28, 2020, were enrolled in this study. Heart rate (HR), mean arterial pressure (MAP), oxygenation index (PaO2 /FiO2 ), renal function, C-reactive protein (CRP), cytokines, procalcitonin (PCT), acute physiology and chronic health evaluation II (APACHE II), sequential organ failure score (SOFA), and prognosis were compared after CRRT. Five COVID-19 patients, three males and two females, aged 70.2 ± 19.6 years, were enrolled. After treatment, HR (101.4 ± 14.08 vs. 83.8 ± 6.22 bpm/min), CRP (183 ± 25.21 vs. 93.78 ± 70.81 mg/L), IL-6 (3234.49 (713.51, 16038.36) vs. 181.29 (82.24, 521.39) pg/mL), IL-8 (154.86 (63.97, 1476.1) vs. 67.19 (27.84, 85.57) pg/mL), and IL-10 (17.43 (9.14, 41.22) vs. 4.97 (2.39, 8.70) pg/mL), APACHE II (29 ± 4.92 vs. 18.4 ± 2.07), and SOFA (17.2 ± 1.92 vs. 11.2 ± 3.4) significantly decreased (P < .05), while MAP (75.8 ± 4.92 vs. 85.8 ± 6.18 mm Hg), and PaO2 /FiO2 (101.2 ± 7.49 vs. 132.6 ± 26.15 mm Hg) significantly increased (P < .05). Among the five patients, negative conversion of nucleic acid test was found in three cases, while two cases died. No adverse events occurred during the treatment. Our study observed a reduced level of overexpressed cytokines, stabilization of hemodynamic status, and staged improvement of organ function during the treatment with Oxiris filter.

Two-dimensional boron nitride nanosheets (BNNSs) hold great promise as thermal management materials because of their ultrahigh thermal conductivity and wide band gap. However, the scalable exfoliat...

In this study, experiments were carried out to investigate aerodynamic characteristics of a high-speed train on viaducts in turbulent crosswinds using a 1:25 scaled sectional model wind-tunnel testing. Pressure measurements of two typical sections, one train-head section and one train-body section, at the windward and leeward tracks were conducted under the smooth and turbulence flows with wind attack angles between −6° and 6°, and the corresponding aerodynamic force coefficients were also calculated using the integral method. The experimental results indicate that the track position affects the mean aerodynamic characteristics of the vehicle, especially for the train-body section. The fluctuating pressure coefficients at the leeward track are more significantly affected by the bridge interference compared to those at the windward track. The effect of turbulence on the train-head section is less than that on the train-body section. Additionally, the mean aerodynamic force coefficients are almost negatively correlated to wind attack angles, which is more prominent for vehicles at the leeward track. Moreover, the lateral force plays a critical role in determining the corresponding overturning moment, especially on the train-body section.

Solar evaporation has emerged as a facile and attractive technology for clean water production, desalination, and organic solvent purification by virtue of abundant solar energy. However, developing a high-performance, environment friendly, and scalable solar evaporator remains a significant challenge. Herein, a one-step, low-cost, and easy-to-manufacture synthesis of a three-dimensional macroporous solar steam generator is reported based on polypyrrole coated natural latex (PPy-NL) foam, offering a sustainable solution to the ever-growing issues of the energy and water crises. The as-prepared foam exhibits good wettability, acid and alkali resistance, high mechanical strength, low thermal conductivity (0.2257 W m–¹ K–¹) and excellent light absorption of ∼95% owing to the introduction of PPy coating. Among polymer photothermal materials, PPy-NL foam gives a vapor generation rate of 1.76 kg m–² h–¹ with a superb solar thermal conversion efficiency of 98% under 1 sun illumination. Furthermore, PPy-NL foam can be directly used to purify various types of wastewater and organic solvent with a high rejection of ions (nearly 100%), oil, and dye. This simple fabrication process with renewable polymer resources and photothermal materials provides a fundamental guidance and practical application value toward developing high-performance solar evaporation technologies for remote areas and individuals.

In this work, polyacrylonitrile/sulfonated poly(ether ether ketone) (PAN/SPEEK) composite fiber membrane as separator for lithium-sulfur battery was first prepared by electrospinning. In order to reduce the shuttle effect, multilayered polyelectrolyte complexes (CS/CNTs)n as surface modification agents were prepared by alternating deposition of oppositely charged hydroxylated multi-walled carbon nanotubes (MWCNTs-COOH) and chitosan (CS) via the layer-by-layer method. The as-prepared fiber multilayered complex separator (FMCS) was finally treated by heating at 120 °C to form cross-linked network for better thermal stability. FMCS possessed much higher porosity than that of the commercial PP separator (44%). As expected, the assembled membranes remained intact even at 150 °C, while PP separator shrunk severely. Compared with PP, PAN/SPEEK/(CS/MWNTs-COOH)10 showed the discharge capacity of 956.9 mAh/g at 0.1 C and maintained 463 mAh/g after 100 cycles with the capacity retention rate of 48.4% which is higher than that of the commercial PP separator. Thus, this work can provide a simple method for modifying separator for long-life lithium-sulfur battery.

Given a changing environment, estimating a flood magnitude corresponding to a desired return period considering nonstationarity is crucial for hydrological engineering designs. Four nonstationary design methods, namely expected waiting time (EWT), expected number of exceedances (ENE), equivalent reliability (ER), and average design life level (ADLL) have already been proposed in recent years. Among them, the EWT method needs to estimate design flood magnitudes by solving numerically. In addition, EWT requires estimating design quantiles for infinite lifespan, or extrapolation time (textra), to guarantee the convergence of the EWT solution under certain conditions. However, few studies have systematically evaluated pros and cons of the EWT method as to how to determine the textra and what kinds of misunderstandings on the applicability of the EWT method exist. In this study, we aim to provide the first investigation of various factors that influence the value of textra in the EWT method, and provide comprehensive comparison of the four methods from the perspectives of textra, design values and associated uncertainties. The annual maximum flood series (AMFS) of 25 hydrological stations, with increasing and decreasing trends, in Pearl River and Weihe River were chosen for illustrations. The results indicate that: (1) the textra of EWT is considerably affected by the trend of AMFS and the choice of extreme distributions. In other words, the textra of stations with increasing trends was significantly smaller than that of stations with decreasing trends, and the textra was also larger for distributions with heavier tail; (2) EWT produced larger design values than ENE for increasing trends, and both EWT and ENE yielded larger design values than ER and ADLL for higher return periods, while complete opposite results were obtained for decreasing trends.

Nowadays, advanced smart mobile devices equipped with multi-core central processing units for handling multithreaded (MT) applications. However, existing research mainly uses single-thread (ST) computing to deal with applications, which limits the performance of mobile computing. To make full use of multi-core resources, this study proposes a fine-grained MT offloading strategy to solve the offloading problem of MT application. The strategy jointly schedules cloud computing resources, as well as local multi-core computing and communication resources. Precisely, the authors first formulate the minimum energy consumption problem for ST offloading. Then, they prove that the problem is convex and solve it by standard convex optimisation technique. Thirdly, they extend the optimisation goals from ST applications to MT applications, and design calculation rules for MT applications to reduce computing costs. Finally, based on these calculation rules and the optimal solution for ST offloading, they develop a MT offloading strategy to solve the computation offloading problem of MT applications. Simulation results show that the proposed fine-grained MT offloading strategy effectively reduces the minimum delay requirement of mobile computing.

Herein, we report a new strategy to effectively enhance the thermal conductivity of epoxy composites by constructing a three-dimensional thermally conductive network with 2D boron nitride nanosheet...

In this work, the nitrogen and fluorine co-doped graphene hydrogel (NFGH) with three-dimensional (3D) porous structure was prepared through a simple one-step hydrothermal method using graphene oxide and ammonium fluoride. The interconnected 3D skeleton porous structure synthesized by the self-assembly of graphene can effectively inhibit the agglomeration of graphene, which will provide more diffusion paths for the electrolyte ions. Benefiting from the wettability of nitrogen functional groups, the nitrogen doping effectively reduces the hydrophobicity of electrode caused by the doping of fluorine. Due to the 3D porous structure of NFGH and the incorporation of nitrogen and fluorine, the NFGH exhibits excellent supercapacitive performance. The maximum specific capacitance of NFGH electrode is up to 366 F g−1 in the aqueous electrolyte, and 98% capacitance can be maintained even after 10,000 cycles. These excellent supercapacitive performances demonstrate that the NFGH has a great potential for application in high-performance supercapacitors.

Anaerobic digestion of cattle manure has a low efficiency due to the high hydraulic retention time (HRT) required to degrade the abundant degradation-resistant compositions, co-digestion with food waste is effective at improving the methane production. Lowering the HRT can therefore increase the methanogenic efficiency during co-digestion. This study considered the effects of different HRTs (25, 20, 15, 10, 7, 5, and 4 days) on cattle manure and food waste co-digestion. The highest methane production was achieved at 1.48 L/L/d with an HRT of 5 days. The maximum methane yields (236–257 mL/g-VS) were attained at HRT ≥15 days and decreasing the HRT to 10-5 days resulted in low methane yields and complete process failure at HRT 4 days, due to volatile fatty acids accumulated and microorganisms washed out. From a high HRT of 20 days to a low HRTs of 5 days, Bacteroidetes and Firmicutes were the dominant bacteria and the percentage of syntrophic acetate oxidizing bacteria (mainly Pelotomaculum and Pseudothermotoga) clearly increased. The dominant methanogen changed from the acetotrophic Methanosaeta to the hydrogenophilic Methanobrevibacter. These results enable biogas plants to utilize surplus amounts of cow manure and food waste in a sustainable manner with high process capacity and methane recovery.

Methanolysis of biomass has attracted increasing attention due to it has some advantages, such as low critical points and strong hydrogen donation ability. In this paper, methanolysis of cellulose, cellobiose (sucrose), and monosaccharide (glucose) were performed to obtain methanol-soluble portions (MSPs). The relatively volatile or less polar species in the MSPs were analyzed with a gas chromatograph/mass spectrometer (GC/MS), and the polar species were identified with a negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometer (FTICRMS). According to analysis with GC/MS, the MSPs include ethers, esters, ketones, acids, sugars, phenols, and furans. The analysis with FTICRMS shows that the polar compounds in the MSPs are O1–O15 class species with 1–14 double bond equivalent (DBE) and 5–35 carbon atom number (CAN). The most abundant class species in MSPs from cellulose, sucrose, and glucose methanolysis are O5, O7, and O7, respectively. The species in MSPs from sucrose and glucose methanolysis center at higher DBE value and more CAN than those in MSP from cellulose methanolysis. Meanwhile, the oxygen atom number of O4–O10 class species in the MSPs were negatively correlated with the average DBE value. The results indicate that condensation intensively proceeded with the specific regularity during sucrose and glucose methanolysis.

Adaptive orthogonal frequency-division multiplexing (OFDM) is a promising technology for underwater acoustic sensor networks (UASNs) to facilitate robust and reliable transmission. This paper deals with an adaptive UASN-OFDM multi-parameter allocation problem in a strongly incomplete information scenario. Specifically, an adversarial multi-armed bandit (MAB) formalism is first proposed, whereby no prior knowledge about channel conditions is required and the reward sequences are not restrained by any statistical assumptions. Second, considering the curse of dimensionality caused by exponentially large number of feasible strategies, we tailor orthogonal learning strategy to reinforce learning for initial decision set and achieve filtration by abandoning some inferior levels. Third, under strictly limited prior information, we design a time-based dynamic exploration mechanism to adjust exploration factor adaptively, which improves algorithm learning ability effectively. Thank to aforementioned efforts, a low-complexity, high-efficiency OD-Exp3 algorithm is presented to handle the complex adaptive OFDM problem in UASNs. Lastly, we show the upper regret bound and the convergence of OD-Exp3 algorithm. Comparative results demonstrate that the proposed algorithm is superior to the existing algorithms.

With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function; then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints.

Polarization remote sensing can be used to obtain polarization data for a given target. It can be acquire not only radiation data, as also measured by conventional remote sensing, but also to quant...

Most rechargeable batteries suffer from severe capacity loss at low temperature, which limits their applications in cold environments. Herein, we propose an original proton battery, which involves ...

Hydronium-ion batteries are regarded as one of the most promising energy technologies as next-generation power sources, benefiting from their cost effectivity and sustainability merits. Herein, we propose a hydronium-ion battery which is based on an organic pyrene-4,5,9,10-tetraone anode and an inorganic MnO2@graphite felt cathode in an acid electrolyte. Its operation involves a quinone/hydroquinone redox reaction on anode and a MnO2/Mn2+ conversion reaction on cathode, in parallel with the transfer of H3O+ between two electrodes. The distinct operation mechanism affords this hydronium-ion battery an energy density up to 132.6 Wh kg−1 and a supercapacitor-comparable power density of 30.8 kW kg−1, along with a long-term cycling life over 5000 cycles. Furthermore, surprisingly, this hydronium-ion battery works well even with a frozen electrolyte under −40 °C, and superior rate performance and cycle stability remain at −70 °C., The authors show a hydronium-ion battery with an organic pyrene-4,5,9,10-tetraone anode and a MnO2@graphite cathode and H3O+ as the charge carrier. In addition to exhibiting promising energy density and power density, this battery works well even under low temperatures ranging from −40 °C to −70 °C.

Dual-arm space robot is a promising autonomous system to remove the increasing space debris actively. Due to its limited on-orbit energy, dual-arm space robot should approach and capture the target with minimum time and base disturbance. In this paper, the multi-objective configuration optimization is proposed for maximizing manipulability and minimizing base disturbance of dual-arm space robot during the pre-contact phase. Based on virtual base modelling and coordinated planning, the free-floating dual-arm space robots can be controlled to track and approach the target satellite. In addition, in the null-space of tracking motion, the configuration optimization of dual-arm space robot can be realized by arm-angle adjustment of 7 DOF redundant manipulator. Therefore, the expressions of arm angle and arm-angle Jacobian are first derived. Then the arm-angle trajectory is parameterized by adopting 5th order polynomial interpolation. The optimal arm-angle trajectory can be obtained by Multiple Objective Particle Swarm Optimization (MOPSO), in which the objective function of configuration optimization is set to the vector consisted of base disturbance and manipulability. Therefore, in the null space of dual-arm space robot approaching the tumbling target satellite, the configuration optimization is realized through following the optimal arm-angle trajectory. Finally, several single-objective and multi-objective optimization simulations are carried out to verify the proposed method.

To establish a simplified analysis model of the shear bearing capacity of underpinning beams and study the shear bearing mechanism of underpinning beams, two underpinning beams adopting the roughening + planting bar + building glue connection method were tested and analyzed. The experimental and numerical analysis results show that the initial slip bearing capacity and initial slip amount of the underpinning beam are small, indicating that the connection method of roughening + planting bar + building glue is effective and feasible.The failure mode and mechanical performance of the underpinning beams are similar to the bending shear failure of a cast-in-place deep beam, and the crack development rule and failure mode of the specimen are in good agreement with the mechanical characteristics of the truss-arch model. Moreover, the theoretical and numerical results show that by increasing the reinforcement ratio of the stirrups, the ultimate bearing capacity of the underpinning beam can be improved, and the slippage between the new-to-old concrete of the beam can be effectively reduced. Finally, based on the truss-arch model theory and considering the concrete arch effect and softening effect, the shear bearing capacity calculation formula of the underpinning beam is established. The results show that the truss-arch model is applicable to the bearing capacity analysis of underpinning beams and provides a new perspective for the bearing capacity calculation of similar underpinning beams.

Convolutional neural networks (CNN) with their deep feature extraction capability have recently been applied in numerous image fusion tasks. However, the image fusion of infrared and visible images leads to loss of fine details and degradation of contrast in the fused image. This deterioration in the image is associated with the conventional “averaging” rule for base layer fusion and relatively large feature extraction by CNN. To overcome these problems, an effective fusion framework based on visual saliency weight map (VSWM) combined with CNN is proposed. The proposed framework first employs VSWM method to improve the contrast of an image under consideration. Next, the fine details in the image are preserved by applying multi-resolution singular value decomposition (MSVD) before further processing by CNN. The promising experimental results show that the proposed method outperforms state-of-the-art methods by scoring the highest over different evaluation metrics such as Q0, multiscale structural similarity (MS_SSIM), and the sum of correlations of differences (SCD).

Polarized reflectance ( $R_{\mathrm {p}}$ ) and degree of linear polarization (DOLP) provide essential information about polarized characteristics of land surfaces. For a given target, DOLP determines the magnitude of $R_{\mathrm {p}}$ . It has been proved that DOLP can be used for some remote monitoring cases that cannot be well detected with either non-polarized or polarized reflectance. Several bidirectional polarization distribution function (BPDF) models have been proposed in the last several decades to reproduce the angular distribution of $R_{\mathrm {p}}$ , but much less attention has been devoted to modeling and analyzing of DOLP. In this study, the Nadal–Breon BPDF model was transferred for calculating the DOLP of earth targets, and characteristics of DOLP were analyzed based on the modeling results. To evaluate the model’s feasibility, two experiments were executed: a fitting and a a priori modeling. The results showed good correlations ( $r>0.9$ ) between estimated and measured DOLP when the model was fitted with POLDER/PARASOL (a space-borne multi-angle multi-spectral polarimetric sensor) measurements. An increase of accuracy from 490 nm to 865 nm for fitting modeling was achieved and the highest accuracy was found at 865 nm for both experiments, with overall relative root mean square errors of 1.1 and 1.3 for fitting and a priori modeling, respectively. Class-based free parameters can be used for the a priori model of DOLP. The dispersion of the target-based free parameters controls the correlation of the a priori modeling results. Moreover, the maximum DOLP was found to be strongly determined by the corresponding bidirectional reflectance factor for every surface type ( $R^{2}=0.86$ ). This study provides an additional approach for obtaining DOLP from remote sensing platform and is helpful for studies of typical land surfaces.

As a promising anode material of sodium ion batteries (SIBs), hard carbon (HC) still faces challenges of inadequate active storage sites and sluggish ion-diffusion kinetics. To improve these issues, herein, we introduce a solvent-induced phase separation (SIPS) method to produce multi-channel carbon nanofibers (MCCFs) as a freestanding anode for SIBs. The channel structures can be precisely designed by simply tuning the mass ratio of the soluble polymer and insoluble carbon precursor, thereby affecting the microstructures of carbon nanofibers. Such tactics permit the first-displayed multi-channel structures in carbon nanofibers with expected defects/pores, and greater ascending rate performance compared to conventional materials. In particular, an outstanding reversible capacity (365.4 mA h g−1 at 0.1C) and excellent cycling stability (90% retention rate after 300 cycles at 0.4C) benefit from the increased active sites and enhanced electrode kinetics. Meanwhile, abundant ultra-micropores allow only the entry of sodium ions and the expanded interlayer spacing makes the sodium insertion easier, and a high plateau capacity (231.2 mA h g−1) below 0.1 V is acquired. When matched with an O3-NaNi1/3Fe1/3Mn1/3O2 cathode, the full cell delivers excellent energy density of ca. 270 W h kg−1 with reliable cycle performance. Moreover, relevant sodium storage behaviors and kinetics mechanisms are elaborated in detail. This work not only delivers a feasible strategy to construct multi-channel carbon-based materials, but also serves as a theory guide for designing other superb HC electrodes.

Currently, black phosphorus (BP) is widely applied in a variety of fields. Concerning the potential toxicity of BP nanosheets towards mammalian cells and bacteria, there is a great need to evaluate their ecological effects. This study was focused on the effect of BP nanosheets on enzyme activity, bacterial community structure, and bacterial function of black soil and burozem. We found BP nanosheets had no significant impact on enzyme activities in black soil during the 60 day exposure. In contrast, the activities of urease and catalase in burozem were significantly inhibited at day 10 and day 30, respectively, which were gradually recovered at day 60. The introduction of BP nanosheets significantly decreased the community richness in burozem at day 10, but had no effect either on the diversity or the richness in black soil. The discrepancies were attributed to the stronger interaction of BP nanosheets with organic matter-rich black soil, leading to a lowered bioavailability of BP nanosheets. Nevertheless, BP nanosheets were predicted to have negligible effect on bacterial function in both soils. It is worthwhile to further investigate the in-depth mechanisms and dose-dependent effect in order to lay the foundation for safe application and discharge of BP nanosheets.

With the increase in interest in energy storage for grid applications, a rechargeable battery, as an efficient energy storage/conversion system, has been receiving great attention. However, its development has largely been stalled by the issues of high cost, safety and energy density. Here, we report an aqueous manganese–lead battery for large-scale energy storage, which involves the MnO2/Mn2+ redox as the cathode reaction and PbSO4/Pb redox as the anode reaction. The redox mechanism of MnO2/Mn2+ was investigated to improve reversibility. All materials are inexpensive and the assembled battery can work well during the penetration test. The battery shows a discharge voltage of around 1.55 V, high rate capability, and no obvious capacity decay over 10 000 cycles. Furthermore, a high volumetric low energy density of 187 W h L−1 was obtained for a pouch battery using a high-concentration electrolyte comprising 3 M MnSO4. Meanwhile, the low freezing point of the high-concentration electrolyte endowed the battery with capability to work at a low temperature of −40 °C. Owing to the low cost and high electrochemical performance, the Mn–Pb battery has great potential for grid energy storage.

Precise detection of low-dose X-radiation using purely organic direct detectors is vital for tissue-equivalent dosimeters and safety control in medical radiation treatment, but it still remains a challenge. Here, we report a promising organic radiation detector based on 4-hydroxycyanobenzene (4HCB, C7H5NO) single crystals. Plate-like 4HCB single crystals up to 18 × 15 × 1.2 mm3 in size are obtained by an optimized solvent evaporation method, thanks to the clarification of the two-dimensional nucleation growth mechanism. After post surface treatment, the leakage current of the 4HCB detector is no larger than 0.1 pA under an electric field of 600 V cm−1. The fabricated detectors show a capability of detecting 241Am 5.49 MeV α particles with a well resolved full energy peak. The calculated hole mobility (μh) and hole mobility lifetime product (μτ)h are 3.40 cm2 V−1 s−1 and 8.50 × 10−5 cm2 V−1, respectively. Simultaneously, under a 50 kVp X-ray beam, a detection limit as low as 0.29 μGyair s−1 with a high sensitivity of 10 μC Gyair−1 cm−2 is achieved in the bias range of 40–100 V, contributing to a superior X-ray imaging capability with a spatial resolution of 0.9 lp mm−1 at a low-dose rate (below 150 μGyair s−1) of exposure.

Supercapacitors have high power density and a long lifespan but poor energy density in contrast with rechargeable batteries, restricting their widespread applications. Adding soluble redox-active ingredients into electrolyte is an effective strategy to increase specific energy. However, an ion-selective membrane is generally needed in such supercapacitors to avoid the mixing of anolyte and catholyte, which significantly increases the cost. Here we report a supercapacitor that consists of a modified solid Ti3C2Tx anode and an active catholyte containing Mn2+, where the conversion between soluble Mn2+ and solid MnO2 occurs at the cathode, and the redox of Ti–O with the bonding/de-bonding of H3O+ occurs at the anode. Impressively, this hybrid supercapacitor displays a gratifying specific energy of 43.4 W h kg−1, without using any ion-selective membrane, and excellent cycling stability over 20 000 cycles. Moreover, we also demonstrate its superior low-temperature performance even though the electrolyte has been frozen at −70 °C.

Uncertainty in renewable energy and load is a great challenge for microgrid operation, especially in islanded mode as the microgrid may be small in size and has limited flexible resources. In this paper, a multi-timescale, two-stage robust unit commitment and economic dispatch model is proposed to optimize the microgrid operation. The first stage is a combination of day-ahead hourly and real-time sub-hourly models, which means the day-ahead dispatch result must also satisfy the real-time condition at the same time. The second stage is to verify the feasibility of the day-ahead dispatch result in worst-case condition considering high-level uncertainty in renewable energy dispatch and load consumptions. In the proposed model, battery energy storage system (BESS) and solar PV units are integrated as a combined solar-storage system. The BESS plays an essential role to balance the variable output of solar PV units, which keeps the combined solar-storage system output unchanged on an hourly basis. In this way, it largely neutralizes the impact of solar uncertainty and makes the microgrid operation grid friendly. Furthermore, in order to enhance the flexibility and resilience of the microgrid, both BESS and thermal units provide regulating reserve to manage solar and load uncertainty. The model has been tested in a controlled hardware in loop (CHIL) environment for the Bronzeville Community Microgrid system in Chicago. The simulation results show that the proposed model works effectively in managing the uncertainty in solar PV and load and can provide a flexible dispatch in both grid-connected and islanded modes.

Oceanographic plankton classification for many images is time consuming, especially for low-contrast images obtained when the water is dirty and opaque due to impurities. A novel, overcomplete dictionary algorithm is studied by analyzing the sparse characteristics of the image matrix using pixel values. The features in hyperspace are mapped onto a specifically designed vector space. Thus, mathematically, the algorithm exhibits faster calculating convergence and has a strong expressivity for the selective signal in the vector space with a lower signal loss rate. The clustering method based on the dictionary can classify planktons for species counting, which can enable high-speed, multi-object recognition of planktons in turbid water. The experimental results demonstrate that if less data (up to 60%) is processed for each image, a recall rate and accuracy greater than 75% and a structural similarity for the reconstructed image greater than 0.9 can be achieved.

Image fusion integrates complex information about a target scene from multiple sensors into a single image. The fused image can further be utilized for human perception or different machine vision tasks. In the case of infrared and visible images, infrared images have the advantage of capturing thermal radiation intensity, whereas visible images are superior in gradient texture. In order to effectively fuse thermal intensity of infrared image and texture advantage of visible image, we propose a novel fusion method based on L0 decomposition and intensity mask. The proposed method first acquires base and detail layers of images (visible & infrared) using L0 decomposition. Next, an intensity mask is obtained using the basic global thresholding method on base layers of infrared image. The layers (base layers and detail layers) and visible images are divided images into three parts by the use of intensity mask, namely, mask-base layers, mask-detail layers, and texture-background. The first and second parts effectively achieve intensity blending, whereas the third part achieves the fused image with a clear gradient texture. The proposed method shows superior performance when compared with five state-of-the-art methods (on publicly available databases).

Lower Paleozoic carbonate rocks are an important exploration area in craton area of the Tarim Basin, with the proven oil and gas reserves of more than 2.2×108 t, but no large-scale discovery has been made in the Gucheng area so far. The key issues restricting exploration are that the source rock, reservoir scale and law of oil and gas enrichment are unclear. By systematically examining the petroleum geological conditions of Lower Paleozoic carbonate rocks, the following findings are reached: (1) Source rocks of slope-basin facies developed in Cambrian-Lower Ordovician in the Gucheng area. (2) The dolomitized beach in the lower part of Ordovician Yingshan Formation has large-scale reservoirs, good reservoir-cap assemblage and developed gas source faults, and is an important field for increasing reserves and production in the near future; hydrocarbon enrichment is controlled by reservoir and gas source faults, and the central dolomitized beach zone is the main exploration area. (3) The Cambrian platform margin reef beach, large in scale, good in physical properties and close to source rocks, has the possibility to form monolithic gas field; the caprock and preservation conditions are the key factors for hydrocarbon enrichment; the northern part of the phases I and II platform margin reefs has better sealing conditions, and is the main direction of next exploration. (4) Limestone fault solution reservoirs in the upper part of Ordovician Yingshan Formation, controlled by faults and small in scale, but good in reservoir-cap combination, worth exploring. (5) The granular limestone beach of Ordovician Yijianfang Formation is well developed and gas-bearing, but short in exposure dissolution time, and the reservoirs are strongly heterogeneous, and are a potential exploration field. Key words: Tarim Basin, Gucheng Area, Lower Paleozoic, carbonate rock, petroleum geological conditions, exploration potential