In situ Hydrothermal Oxidation of Ternary FeCoNi Alloy Electrode for Overall Water Splitting

Exploring noble metal-free catalyst materials for high efficient electrochemical water splitting to produce hydrogen is strongly desired for renewable energy development.In this article,a novel bifunctional catalytic electrode of insitu-grown type for alkaline water splitting based on FeCoNi alloy substrate has been successfully prepared via a facile one-step hydrothermal oxidation route in an alkaline hydrogen peroxide medium.It shows that the matrix alloy with the atom ratio 4∶3∶3 of Fe∶Co∶Ni can obtain the best catalytic performance when hydrothermally treated at 180℃for 18 h in the solution containing 1.8 M hydrogen peroxide and 3.6 M sodium hydroxide.The as-prepared Fe_(0.4)Co_(0.3)Ni_(0.3)-1.8 electrode exhibits small overpotentials of only 184 and 175 mV at electrolysis current density of 10 mA cm^(-2)for alkaline OER and HER processes,respectively.The overall water splitting at electrolysis current density of 10 mA cm^(-2)can be stably delivered at a low cell voltage of 1.62 V.These characteristics including the large specific surface area,the high surface nickel content,the abundant catalyst species,the balanced distribution between bivalent and trivalent metal ions,and the strong binding of in-situ naturally growed catalytic layer to matrix are responsible for the prominent catalytic performance of the Fe_(0.4)Co_(0.3)Ni_(0.3)-1.8 electrode,which can act as a possible replacement for expensive noble metal-based materials.

Experimental Study on Ammonia Co-Firing with Coal for Carbon Reduction in the Boiler of a 300-MW Coal-Fired Power Station

To reduce CO_(2) emissions from coal-fired power plants,the development of low-carbon or carbon-free fuel combustion technologies has become urgent.As a new zero-carbon fuel,ammonia(NH_(3))can be used to address the storage and transportation issues of hydrogen energy.Since it is not feasible to completely replace coal with ammonia in the short term,the development of ammonia-coal co-combustion technology at the current stage is a fast and feasible approach to reduce CO_(2) emissions from coal-fired power plants.This study focuses on modifying the boiler and installing two layers of eight pure-ammonia burners in a 300-MW coal-fired power plant to achieve ammonia-coal co-combustion at proportions ranging from 20%to 10%(by heat ratio)at loads of 180-to 300-MW,respectively.The results show that,during ammonia-coal co-combustion in a 300-MW coal-fired power plant,there was a more significant change in NO_(x) emissions at the furnace outlet compared with that under pure-coal combustion as the boiler oxygen levels varied.Moreover,ammonia burners located in the middle part of the main combustion zone exhibited a better high-temperature reduction performance than those located in the upper part of the main combustion zone.Under all ammonia co-combustion conditions,the NH_(3) concentration at the furnace outlet remained below 1 parts per million(ppm).Compared with that under pure-coal conditions,the thermal efficiency of the boiler slightly decreased(by 0.12%-0.38%)under different loads when ammonia co-combustion reached 15 t·h^(-1).Ammonia co-combustion in coal-fired power plants is a potentially feasible technology route for carbon reduction.

Deep Learning-Based Emotion Detection

In order to make artificial intelligence smarter by detecting user emotions, this project analyzes and determines the current type of human emotions through computer vision, semantic recognition and audio feature classification. In facial expression recognition, for the problems of large number of parameters and poor real-time performance of expression recognition methods based on deep learning, Wang Weimin and Tang Yang Z. et al. proposed a face expression recognition method based on multilayer feature fusion with light-weight convolutional networks, which uses an improved inverted residual network as the basic unit to build a lightweight convolutional network model. Based on this method, this experiment optimizes the traditional CNN MobileNet model and finally constructs a new model framework ms_model_M, which has about 5% of the number of parameters of the traditional CNN MobileNet model. ms_model_M is tested on two commonly used real expression datasets, FER-2013 and AffectNet, the accuracy of ms_model_M is 74.35% and 56.67%, respectively, and the accuracy of the traditional MovbliNet model is 74.11% and 56.48% in the tests of these two datasets. This network structure well balances the recognition accuracy and recognition speed of the model. For semantic emotion detection and audio emotion detection, the existing models and APIs are used in this experiment.

DYNAMIC VARIATIONS OF WATER QUALITY IN TAIHU LAKE AND MULTIVARIATE ANALYSIS OF ITS INFLUENTIAL FACTORS

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A Novel Universal Steganalysis Algorithm Based on the IQM and the SRM

The state-of-the-art universal steganalysis method,spatial rich model(SRM),and the steganalysis method using image quality metrics(IQM)are both based on image residuals,while they use 34671 and 10 features respectively.This paper proposes a novel steganalysis scheme that combines their advantages in two ways.First,filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods.In addition,a total variant(TV)filter is also used due to its good performance of preserving image edge properties during filtering.Second,due to each type of these filters having own advantages,the multiple filters are used simultaneously and the features extracted from their outputs are combined together.The whole steganalysis procedure is removing steganographic noise using those filters,then measuring the distances between images and their filtered version with the image quality metrics,and last feeding these metrics as features to build a steganalyzer using either an ensemble classifier or a support vector machine.The scheme can work in two modes,the single filter mode using 9 features,and the multi-filter mode using 639 features.We compared the performance of the proposed method,the SRM and the maxSRMd2.The maxSRMd2 is the improved version of the SRM.The simulated results show that the proposed method that worked in the multi-filter mode was about 10%more accurate than the SRM and maxSRMd2 when the data were globally normalized,and had similar performance with the SRM and maxSRMd2 when the data were locally normalized.

Eigenvalue Analysis of Thin Plate with Complicated Shapes By a Novel Infinite Element Method

A novel infinite element method(IEM)is presented for solving plate vibration problems in this paper.In the proposed IEM,the substructure domain is partitioned into multiple layers of geometrically similar finite elements which use only the data of the boundary nodes.A convergence criterion based on the trace of the mass matrix is used to determine the number of layers in the IE model partitioning process.Furthermore,in implementing the Craig-Bampton(CB)reduction method,the inversion of the global stiffness matrix is calculated using only the stiffness matrix of the first element layer.The validity and performance of the proposed method are investigated by means of four illustrative problems.The first example considers the case of a simple clamped rectangular plate.It is observed that the IEM results are consistent with the theoretical results for first six natural frequencies.The second example considers the frequency response of a clamped rectangular plate with a crack.The main feature of IEM is that a very fine and good quality virtual mesh can be created around the crack tip.The third and fourth examples consider the natural frequency of a multiple point supported plate and a perforated plate,respectively.The results are obtained just need to adjust the reference point or boundary nodes.The parametric analyses for various geometric profiles are easy to be conducted using these numerical techniques.In general,the results presented in this study have shown that the proposed method provides a direct,convenient and accurate tool for eigenvalue analysis of thin plate structure with complicated shapes.

High-power xenon lamp-pumped Er:YAP pulse laser operated in free-running and acousto-optical Q-switching modes

We demonstrate a high-energy and high-power pulse laser on a xenon lamp-pumped Er:YAP crystal. The laser performance and thermal focal lengths under different working frequencies are discussed. The results show that the thermal lens effect is gradually aggravated with the increase of working frequencies, and even working at 100 Hz, a single pulse energy of 234 m J can be achieved. A maximum average power of 41.5 W is achieved with a working frequency of 20 Hz and slope efficiency of 2.82%. This output power is much higher than other xenon lamp-pumped erbium laser devices.A Q-switched laser is demonstrated by using the TeO2crystal, the maximum output energies of 11.5 m J and 3.5 m J are obtained at 50 and 100 Hz, the corresponding peak powers are 93.4 k W and 17.2 kW, respectively.The laser wavelengths and beam quality factors are also characterized in the free-running and Q-switched modes. A higher pulse energy and peak power laser could be achieved further by improving the damage threshold of TeO2acousto-optical Q-switching. All the experimental results illustrate that the xenon lamp-pumped Er:YAP laser is a promising candidate for high-power and high-frequency mid-infrared laser devices.

An Efficient Steganalysis Model Based on Multi-Scale LTP and Derivative Filters

Local binary pattern(LBP)is one of the most advanced image classification recognition operators and is commonly used in texture detection area.Research indicates that LBP also has a good application prospect in steganalysis.However,the existing LBP-based steganalysis algorithms are only capable to detect the least significant bit(LSB)and the least significant bit matching(LSBM)algorithms.To solve this problem,this paper proposes a steganalysis model called msdeLTP,which is based on multi-scale local ternary patterns(LTP)and derivative filters.The main characteristics of the msdeLTP are as follows:First,to reduce the interference of image content on features,the msdeLTP uses derivative filters to acquire residual images on which subsequent operations are based.Second,instead of LBP features,LTP features are extracted considering that the LTP feature can exhibit multiple variations in the relationship of adjacent pixels.Third,LTP features with multiple scales and modes are combined to show the relationship of neighbor pixels within different radius and along different directions.Analysis and simulation show that the msdeLTP uses only 2592-dimensional features and has similar detection accuracy as the spatial rich model(SRM)at the same time,showing the high steganalysis efficiency of the method.

The Impact of Auditor Quality on Audit Quality--Evidence from China Based on Data from 2014-2016

This paper studies the impact of auditors'own quality on audit quality.Through the test of relevant models,it is necessary to find out whether the conclusions of predecessors have practical significance in improving the quality of auditing as time goes by and other relevant influencing factors of the market.The paper collects relevant data from multiple dimensions such as the age level of the auditors,the accumulation of audit experience,and the level of education by collecting the relevant data of the 2016 A-share listed companies and the top 100 CPA firms in 2014-2016.An empirical test was conducted.According to the results of the empirical test,the discussion and research will be carried out,and further opinions and suggestions on how to improve the quality of the auditor should be put forward.The innovation of the research is that the level of earnings management is used as a measure of audit quality,and non-recurring gains and losses are used as explanatory variables.On the basis of previous studies,time and environmental institutional variables were further introduced for further verification.

Robust,self-adhesive,and low-contact impedance polyvinyl alcohol/polyacrylamide dual-network hydrogel semidry electrode for biopotential signal acquisition

Herein,we fabricated a flexible semidry electrode with excellent mechanical performance,satisfactory self-adhesiveness,and low-contact impedance using physical/chemical crosslinked polyvinyl alcohol/polyacrylamide dual-network hydrogels(PVA/PAM DNHs)as an efficient saline reservoir.The resultant PVA/PAM DNHs showed admirable adhesive and compliance to the hairy scalp,facilitating the establishment of a robust electrode/skin interface for biopotential signal transmission.Moreover,the PVA/PAM DNHs steadily released trace saline onto the scalp to achieve the minimized potential drift(1.47±0.39 mV/min)and low electrode–scalp impedance(18.2±8.9 kΩ@10 Hz).More importantly,the application feasibility of real-world brain−computer interfaces(BCIs)was preliminarily validated by 10 participants using two classic BCI paradigms.The mean temporal cross-correlation coefficients between the semidry and wet electrodes in the eyes open/closed and the N200 speller paradigms are 0.919±0.054 and 0.912±0.050,respectively.Both electrodes demonstrate anticipated neuroelectrophysiological responses with similar patterns.This semidry electrode could also effectively capture robust P-QRS-T peaks during electrocardiogram recording.Considering their outstanding advantages of fast setup,user-friendliness,and robust signals,the proposed PVA/PAM DNH-based electrode is a promising alternative to wet electrodes in biopotential signal acquisition.

A writable lithium metal ink

The recent revival of Li metal anodes(LMA)leads to a renewed interest in LMA as the ultimate choice for rechargeable lithium batteries towards high energy density.However,multiple challenges stand in the way of using LMA,of which high reactivity,dendrite growth,the difficulty of fabricating Li thin foils,and the flammability of organic liquid electrolytes are typical.Here,a writable Li metal ink(LMI)prepared by introducing biomass-derived carbon particles into molten Li is presented.Due to the significantly decreased surface tension,LMI is able to directly write on copper foils or other substrates that ultrathin Li foils with a remarkably small thickness(<10μm)can be achieved.The versatility of LMI is further demonstrated in addressing the interface issue between LMA and garnet-type solid-state electrolytes,where directly writing LMI on the garnet offers a perfect contact and enables an extremely low interfacial resistance of 6Ωcm^2,in sharp contrast to 939Ωcm^2 between the pure Li and the garnet.Due to the successful partnership with non-flammable solid-state electrolytes,ink-based technology may have a chance to bring us very close to the use of solid-state lithium metal batteries(SSLMBs)with high safety and high energy density.

A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM

Light Detection and Ranging(LiDAR)sensors are popular in Simultaneous Localization and Mapping(SLAM)owing to their capability of obtaining ranging information actively.Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy.However,before employing LiDAR intensities in SLAM,a calibration operation is usually carried out so that the intensity is independent of the incident angle and range.The range is determined from the laser beam transmitting time.Therefore,the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface.In a complex environment,it is difficult to obtain the incident angle robustly.This procedure also complicates the data processing in SLAM and as a result,further application of the LiDAR intensity in SLAM is hampered.Motivated by this problem,in the present study,we propose a Hyperspectral LiDAR(HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM.HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements.Owing to the design of the laser,the eight-channel range and intensity were collected with the same incident angle and range.According to the laser beam radiation model,the ratio values between two randomly selected channels’intensities at an identical target are independent of the range information and incident angle.To test the proposed method,the HSL was employed to scan a wall with different coloured papers pasted on it(white,red,yellow,pink,and green)at four distinct positions along a corridor(with an interval of 60 cm in between two consecutive positions).Then,a ratio value vector was constructed for each scan.The ratio value vectors between consecutive laser scans were employed to match the point cloud.A classic Iterative Closest Point(ICP)algorithm was employed to estimate the HSL motion using the range information from the matched point clouds.According to the test results,we found that pink and green papers were distinctive at 650,690,and 720 nm.A ratio value vector was constructed using 650-nm spectral information against the reference channel.Furthermore,compared with the classic ICP using range information only,the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation.For the best case in the field test,the proposed method enhanced the heading angle estimation by 72%,and showed an average 25.5%improvement in a featureless spatial testing environment.The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.

Glutamine synthetase-negative hepatocellular carcinoma has better prognosis and response to sorafenib treatment after hepatectomy

Background:Glutamine synthetase(GS)and arginase 1(Arg1)are widely used pathological markers that discriminate hepatocellular carcinoma(HCC)from intrahepatic cholangiocarcinoma;however,their clinical significance in HCC remains unclear.Methods:We retrospectively analyzed 431 HCC patients:251 received hepatectomy alone,and the other 180 received sorafenib as adjuvant treatment after hepatectomy.Expression of GS and Arg1 in tumor specimens was evaluated using immunostaining.mRNA sequencing and immunostaining to detect progenitor markers(cytokeratin 19[CK19]and epithelial cell adhesion molecule[EpCAM])and mutant TP53 were also conducted.Results:Up to 72.4%(312/431)of HCC tumors were GS positive(GS+).Of the patients receiving hepatectomy alone,GS negative(GS-)patients had significantly better overall survival(OS)and recurrence-free survival(RFS)than GS+patients;negative expression of Arg1,which is exclusively expressed in GS-hepatocytes in the healthy liver,had a negative effect on prognosis.Of the patients with a high risk of recurrence who received additional sorafenib treatment,GS-patients tended to have better RFS than GS+patients,regardless of the expression status of Arg1.GS+HCC tumors exhibit many features of the established proliferation molecular stratification subtype,including poor differentiation,high alpha-fetoprotein levels,increased progenitor tumor cells,TP53 mutation,and upregulation of multiple tumor-related signaling pathways.Conclusions:GS-HCC patients have a better prognosis and are more likely to benefit from sorafenib treatment after hepatectomy.Immunostaining of GS may provide a simple and applicable approach for HCC molecular stratification to predict prognosis and guide targeted therapy.