The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are e...The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are evaluated.The grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing alloy.Note that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing alloy.The electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical activity.Using In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca alloying.The micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly improved.The In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.展开更多
Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid s...Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.展开更多
As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path pla...As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.展开更多
To optimize their Al_(2)O_(3)-SiO_(2) raw materials,anorthite based insulation refractories were prepared by the in-situ sintering process combined with the foaming method after sintering at 1350℃for 3 h,using green ...To optimize their Al_(2)O_(3)-SiO_(2) raw materials,anorthite based insulation refractories were prepared by the in-situ sintering process combined with the foaming method after sintering at 1350℃for 3 h,using green and pollution-free kaolin,kyanite,andalusite and sillimanite as Al_(2)O_(3)-SiO_(2) raw materials,respectively,and industrial CaCO_(3) as the CaO source.Effects of Al_(2)O_(3)-SiO_(2) raw material types on the physical properties,phase composition and microstructure were investigated.The results are as follows.All samples prepared by different Al_(2)O_(3)-SiO_(2) raw materials have hexagonal flake anorthite and a small amount of mullite and corundum.Their bulk density and thermal conductivity decrease in the order of using kaolin,andalusite,kyanite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,but their apparent porosity increases.Moreover,in the sample with kaolin,the bonding between anorthite crystals on the pore walls is closer than that of the other samples,which is conducive to increasing the cold crushing strength.The bonding between anorthite crystals on pore walls gradually decreases in the order of using kyanite,andalusite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,thus their cold crushing strength decreases accordingly.In comprehensive consideration,the properties of the sample from kyanite are the optimal.Its apparent porosity,thermal conductivity and cold crushing strength are 84.6%,0.141 W·m^(-1)·K^(-1) and 1.89 MPa,respectively.展开更多
Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked...Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.展开更多
The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO...The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.展开更多
Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and c...Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.展开更多
In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling struct...In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.展开更多
Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting ac...Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R<sup>2</sup> values showed that Fuzzy-Γ<sub>1</sub>, Fuzzy-Γ<sub>2</sub>, Fuzzy-Γ<sub>3</sub>, Fuzzy-Γ<sub>4</sub> had good fitting performance, among which Fuzzy-Γ<sub>1</sub> had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ<sub>2</sub> distribution function was the second, and Fuzzy-Γ<sub>4</sub> distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ<sub>5</sub> is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ<sub>1</sub> and Fuzzy-Γ<sub>2</sub>.展开更多
The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have eme...The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.展开更多
Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Parti...Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.展开更多
Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,local...Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.展开更多
The shortage of fresh water in the world has brought upon a serious crisis to human health and economic development.Solar‐driven interfacial photothermal conversion water evaporation including evaporating seawater,la...The shortage of fresh water in the world has brought upon a serious crisis to human health and economic development.Solar‐driven interfacial photothermal conversion water evaporation including evaporating seawater,lake water,or river water has been recognized as an environmentally friendly process for obtaining clean water in a low‐cost way.However,water transport is restricted by itself by solar energy absorption capacity's limits,especially for finite evaporation rates and insufficient working life.Therefore,it is important to seek photothermal conversion materials that can efficiently absorb solar energy and reasonably design solar‐driven interfacial photothermal conversion water evaporation devices.This paper reviews the research progress of carbon‐based photothermal conversion materials and the mechanism for solar‐driven interfacial photothermal conversion water evaporation,as well as the summary of the design and development of the devices.Based on the research progress and achievements of photothermal conversion materials and devices in the fields of seawater desalination and photothermal electric energy generation in recent years,the challenges and opportunities faced by carbon‐based photothermal conversion materials and devices are discussed.The prospect of the practical application of solar‐driven interfacial photothermal conversion evaporation technology is foreseen,and theoretical guidance is provided for the further development of this technology.展开更多
The practical application of magnesium hydride(MgH_(2))was seriously limited by its high desorption temperature and slow desorp-tion kinetics.In this study,a bullet-like catalyst based on vanadium related MOFs(MOFs-V)...The practical application of magnesium hydride(MgH_(2))was seriously limited by its high desorption temperature and slow desorp-tion kinetics.In this study,a bullet-like catalyst based on vanadium related MOFs(MOFs-V)was successfully synthesized and doped with MgH_(2) by ball milling to improve its hydrogen storage performance.Microstructure analysis demonstrated that the as-synthesized MOFs was consisted of V_(2)O_(3) with a bullet-like structure.After adding 7wt%MOFs-V,the initial desorption temperature of MgH_(2) was reduced from 340.0 to 190.6℃.Besides,the MgH_(2)+7wt%MOFs-V composite released 6.4wt%H_(2) within 5 min at 300℃.Hydrogen uptake was started at 60℃under 3200 kPa hydrogen pressure for the 7wt%MOFs-V containing sample.The desorption and absorption apparent activity energies of the MgH_(2)+7wt%MOFs-V composite were calculated to be(98.4±2.9)and(30.3±2.1)kJ·mol^(-1),much lower than(157.5±3.3)and(78.2±3.4)kJ·mol^(−1) for the as-prepared MgH_(2).The MgH_(2)+7wt%MOFs-V composite exhibited superior cyclic property.During the 20 cycles isothermal dehydrogenation and hydrogenation experiments,the hydrogen storage capacity stayed almost unchanged.X-ray diffraction(XRD)and X-ray photoelectron spectrometer(XPS)measurements confirmed the presence of metallic vanadium in the MgH_(2)+7wt%MOFs-V composite,which served as catalytic unit to markedly improve the hydrogen storage properties of Mg/MgH_(2) system.展开更多
Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wi...Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.展开更多
For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over ti...For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over time.Decaying has been proved to enhance generalization as well as optimization.Other parameters,such as the network’s size,the number of hidden layers,drop-outs to avoid overfitting,batch size,and so on,are solely based on heuristics.This work has proposed Adaptive Teaching Learning Based(ATLB)Heuristic to identify the optimal hyperparameters for diverse networks.Here we consider three architec-tures Recurrent Neural Networks(RNN),Long Short Term Memory(LSTM),Bidirectional Long Short Term Memory(BiLSTM)of Deep Neural Networks for classification.The evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate(CLR),Hyperbolic Tangent Decay(HTD),and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts(T-HTR)techniques.Experimental results have shown the performance improvement on the 20Newsgroup,Reuters Newswire and IMDB dataset.展开更多
Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspe...Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.展开更多
Oily wastewater pollution is an urgent problem to be solved.Complicated preparation process,toxic hydrophobic modifiers and poor mechanical properties limit the application of polysaccharide-based aerogel in oil–wate...Oily wastewater pollution is an urgent problem to be solved.Complicated preparation process,toxic hydrophobic modifiers and poor mechanical properties limit the application of polysaccharide-based aerogel in oil–water separation.Inspired by the Strider’s Leg structure in nature,an eco-friendly and reusable polysaccharide-based composite aerogel was prepared by hydrophobic modification with zein for efficient oil–water separation.The introduction of hydrophobic zein into aerogel by simple immersion method without the use of toxic modifiers can build micro/nanostructures similar to the villi on a water strider’s leg to increase the surface roughness and the hydrophobicity.And three degradable,non-toxic and economical polysaccharides including chitosan,carboxylated cellulose nanofibers and starch were used to construct aerogel skeleton,endowing aerogel with porous structures and good mechanical properties.The resulting composite aerogel(ZOMA)showed low density(0.11 g/cm^(3)),good oil absorption capacity(9 g/g),high flux oil–water separation(5595 L m^(−2)h^(−1))and excellent oil–water separation performance(99.8%).And ZOMA still had good tensile strength and elasticity after 50 compression cycles.After 10 cycles of absorption and desorption,ZOMA aerogel remained still more than 90%of its initial absorption capacity.This study provides new insight for the design of environmentally friendly and efficient adsorbents for oil–water separation.展开更多
Photocatalysis is an effective way to solve the problems of environmental pollution and energy shortage.Numerous photocatalysts have been developed and various strategies have been proposed to improve the photocatalyt...Photocatalysis is an effective way to solve the problems of environmental pollution and energy shortage.Numerous photocatalysts have been developed and various strategies have been proposed to improve the photocatalytic performance.Among them,Bi-based photocatalysts have become one of the most popular research topics due to their suitable band gaps,unique layered structures,and physicochemical properties.In this review,Bi-based photocatalysts(BiOX,BiVO_(4),Bi_(2)S_(3),Bi_(2)MoO_(6),and other Bi-based photocatalysts)have been summarized in the field of photocatalysis,including their applications of the removal of organic pollutants,hydrogen production,oxygen production etc.The preparation strategies on how to improve the photocatalytic performance and the possible photocatalytic mechanism are also summarized,which could supply new insights for fabricating high-efficient Bi-based photocatalysts.Finally,we summarize the current challenges and make a reasonable outlook on the future development direction of Bi-based photocatalysts.展开更多
Fe-Mn based layer oxides cathode materials have attracted widespread attention as a potential candidate for sodium-ion batteries(SIBs)owing to the earth abundance,cost-effectiveness and acceptable specific capacity.Ho...Fe-Mn based layer oxides cathode materials have attracted widespread attention as a potential candidate for sodium-ion batteries(SIBs)owing to the earth abundance,cost-effectiveness and acceptable specific capacity.However,the irreversible phase transition often brings rapid capacity decay,which seriously hinders the practical application in large-scale energy storage.Herein,we design a nickel-doped Na_(0.70)Fe_(0.10)Cu_(0.20)Ni_(0.05)Mn_(0.65)O_(2)(NFCNM-0.05)cathode material of SIBs with activated anionic redox reaction,and then inhibit the harmful phase transition.The ex-situ X-ray diffraction patterns demonstrate the NFCNM-0.05 always keeps the P2 phase during the sodiation/desodiation process,indicating a high structure stability.The ex-situ X-ray photoelectron spectroscopy implies the redox reactions between O2-and O-occur in the charging process,which offers extra specific capacity.Thus,the NFCNM-0.05 electrode delivers a high initial discharge capacity of 148 mA h g-1and remains a prominent cycling stability with an excellent capacity retention of 95.9%after 200 cycles at 1 C.In addition,the electrochemical impedance spectroscopy and galvanostatic intermittent titration technique show the NFCNM-0.05 electrode possesses fast ion diffusion ability,which is beneficial for the enhancement of rate performance.Even at 10 C,the NFCNM-0.05 can offer a reversible discharge capacity of 81 mA h g-1.DFT calculation demonstrates the doping of appropriate amount of Ni ions is benefit for the enhancement of the electrochemical performance of the layer oxides.This work provides an effective strategy to enhance the electrochemical performance of Fe-Mn based cathode materials of SIBs.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.:51901153)Shanxi Scholarship Council of China(Grant No.:2019032)+1 种基金Natural Science Foundation of Shanxi(Grant No.:202103021224049)the Science and Technology Major Project of Shanxi Province(Grant No.:20191102008,20191102007)。
文摘The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are evaluated.The grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing alloy.Note that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing alloy.The electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical activity.Using In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca alloying.The micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly improved.The In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.
基金supported by the National Natural Science Foundation of China(Grant Nos.51975227 and 12272144).
文摘Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.
基金supported by National Natural Science Foundation of China(No.62073212)Shanghai Science and Technology Commission(No.23ZR1426600).
文摘As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.
基金This work was supported by the National Natural Science Foundation of China(5180021223)Henan Provice Science&Technology Programs(232102231046 and 232102231051)Cultivation Programme for Yong Backbone Teachers in Henan University to Technology(2142121).
文摘To optimize their Al_(2)O_(3)-SiO_(2) raw materials,anorthite based insulation refractories were prepared by the in-situ sintering process combined with the foaming method after sintering at 1350℃for 3 h,using green and pollution-free kaolin,kyanite,andalusite and sillimanite as Al_(2)O_(3)-SiO_(2) raw materials,respectively,and industrial CaCO_(3) as the CaO source.Effects of Al_(2)O_(3)-SiO_(2) raw material types on the physical properties,phase composition and microstructure were investigated.The results are as follows.All samples prepared by different Al_(2)O_(3)-SiO_(2) raw materials have hexagonal flake anorthite and a small amount of mullite and corundum.Their bulk density and thermal conductivity decrease in the order of using kaolin,andalusite,kyanite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,but their apparent porosity increases.Moreover,in the sample with kaolin,the bonding between anorthite crystals on the pore walls is closer than that of the other samples,which is conducive to increasing the cold crushing strength.The bonding between anorthite crystals on pore walls gradually decreases in the order of using kyanite,andalusite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,thus their cold crushing strength decreases accordingly.In comprehensive consideration,the properties of the sample from kyanite are the optimal.Its apparent porosity,thermal conductivity and cold crushing strength are 84.6%,0.141 W·m^(-1)·K^(-1) and 1.89 MPa,respectively.
文摘Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.
基金financial support extended for this academic work by the Beijing Natural Science Foundation(Grant 2232066)the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant LSL-2212).
文摘The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.
基金supported by the Outstanding Youth Team Project of Central Universities(QNTD202308)the Ant Group through CCF-Ant Research Fund(CCF-AFSG 769498 RF20220214).
文摘Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.
基金supported by the Natural Science Foundation of China underGrant 61833016 and 61873293the Shaanxi OutstandingYouth Science Foundation underGrant 2020JC-34the Shaanxi Science and Technology Innovation Team under Grant 2022TD-24.
文摘In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.
文摘Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R<sup>2</sup> values showed that Fuzzy-Γ<sub>1</sub>, Fuzzy-Γ<sub>2</sub>, Fuzzy-Γ<sub>3</sub>, Fuzzy-Γ<sub>4</sub> had good fitting performance, among which Fuzzy-Γ<sub>1</sub> had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ<sub>2</sub> distribution function was the second, and Fuzzy-Γ<sub>4</sub> distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ<sub>5</sub> is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ<sub>1</sub> and Fuzzy-Γ<sub>2</sub>.
基金financial support received from DST-SERBSRG/2020/000997,Indiathe initiation grant received from IIT Kanpur。
文摘The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.
文摘Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.
文摘Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.
基金Natural Science Foundation of Shandong Province,Grant/Award Number:ZR2019MB019National Natural Science Foundation of China,Grant/Award Numbers:22075122,52071295Research Foundation for Talented Scholars of Linyi University,Grant/Award Number:Z6122010。
文摘The shortage of fresh water in the world has brought upon a serious crisis to human health and economic development.Solar‐driven interfacial photothermal conversion water evaporation including evaporating seawater,lake water,or river water has been recognized as an environmentally friendly process for obtaining clean water in a low‐cost way.However,water transport is restricted by itself by solar energy absorption capacity's limits,especially for finite evaporation rates and insufficient working life.Therefore,it is important to seek photothermal conversion materials that can efficiently absorb solar energy and reasonably design solar‐driven interfacial photothermal conversion water evaporation devices.This paper reviews the research progress of carbon‐based photothermal conversion materials and the mechanism for solar‐driven interfacial photothermal conversion water evaporation,as well as the summary of the design and development of the devices.Based on the research progress and achievements of photothermal conversion materials and devices in the fields of seawater desalination and photothermal electric energy generation in recent years,the challenges and opportunities faced by carbon‐based photothermal conversion materials and devices are discussed.The prospect of the practical application of solar‐driven interfacial photothermal conversion evaporation technology is foreseen,and theoretical guidance is provided for the further development of this technology.
基金financially supported by the National Natural Science Foundation of China (No. 51801078)the Natural Science Foundation of Jiangsu Province (No. BK20180986)
文摘The practical application of magnesium hydride(MgH_(2))was seriously limited by its high desorption temperature and slow desorp-tion kinetics.In this study,a bullet-like catalyst based on vanadium related MOFs(MOFs-V)was successfully synthesized and doped with MgH_(2) by ball milling to improve its hydrogen storage performance.Microstructure analysis demonstrated that the as-synthesized MOFs was consisted of V_(2)O_(3) with a bullet-like structure.After adding 7wt%MOFs-V,the initial desorption temperature of MgH_(2) was reduced from 340.0 to 190.6℃.Besides,the MgH_(2)+7wt%MOFs-V composite released 6.4wt%H_(2) within 5 min at 300℃.Hydrogen uptake was started at 60℃under 3200 kPa hydrogen pressure for the 7wt%MOFs-V containing sample.The desorption and absorption apparent activity energies of the MgH_(2)+7wt%MOFs-V composite were calculated to be(98.4±2.9)and(30.3±2.1)kJ·mol^(-1),much lower than(157.5±3.3)and(78.2±3.4)kJ·mol^(−1) for the as-prepared MgH_(2).The MgH_(2)+7wt%MOFs-V composite exhibited superior cyclic property.During the 20 cycles isothermal dehydrogenation and hydrogenation experiments,the hydrogen storage capacity stayed almost unchanged.X-ray diffraction(XRD)and X-ray photoelectron spectrometer(XPS)measurements confirmed the presence of metallic vanadium in the MgH_(2)+7wt%MOFs-V composite,which served as catalytic unit to markedly improve the hydrogen storage properties of Mg/MgH_(2) system.
文摘Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.
文摘For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over time.Decaying has been proved to enhance generalization as well as optimization.Other parameters,such as the network’s size,the number of hidden layers,drop-outs to avoid overfitting,batch size,and so on,are solely based on heuristics.This work has proposed Adaptive Teaching Learning Based(ATLB)Heuristic to identify the optimal hyperparameters for diverse networks.Here we consider three architec-tures Recurrent Neural Networks(RNN),Long Short Term Memory(LSTM),Bidirectional Long Short Term Memory(BiLSTM)of Deep Neural Networks for classification.The evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate(CLR),Hyperbolic Tangent Decay(HTD),and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts(T-HTR)techniques.Experimental results have shown the performance improvement on the 20Newsgroup,Reuters Newswire and IMDB dataset.
文摘Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.
基金supported by the National Nature Science Foundation of China(no.51735013).
文摘Oily wastewater pollution is an urgent problem to be solved.Complicated preparation process,toxic hydrophobic modifiers and poor mechanical properties limit the application of polysaccharide-based aerogel in oil–water separation.Inspired by the Strider’s Leg structure in nature,an eco-friendly and reusable polysaccharide-based composite aerogel was prepared by hydrophobic modification with zein for efficient oil–water separation.The introduction of hydrophobic zein into aerogel by simple immersion method without the use of toxic modifiers can build micro/nanostructures similar to the villi on a water strider’s leg to increase the surface roughness and the hydrophobicity.And three degradable,non-toxic and economical polysaccharides including chitosan,carboxylated cellulose nanofibers and starch were used to construct aerogel skeleton,endowing aerogel with porous structures and good mechanical properties.The resulting composite aerogel(ZOMA)showed low density(0.11 g/cm^(3)),good oil absorption capacity(9 g/g),high flux oil–water separation(5595 L m^(−2)h^(−1))and excellent oil–water separation performance(99.8%).And ZOMA still had good tensile strength and elasticity after 50 compression cycles.After 10 cycles of absorption and desorption,ZOMA aerogel remained still more than 90%of its initial absorption capacity.This study provides new insight for the design of environmentally friendly and efficient adsorbents for oil–water separation.
基金We gratefully acknowledge the support of this research by the National Natural Science Foundation of China(52172206,21871078)the Heilongjiang Province Natural Science Foundation of China(JQ2019B001)+4 种基金the Shandong Province Natural Science Foundation(ZR2021MB016)the Heilongjiang Provincial Institutions of Higher Learning Basic Research Funds Basic Research Projects(2021-KYYWF-0007)the Heilongjiang Postdoctoral Startup Fund(LBH-Q14135)the Heilongjiang University Science Fund for Distinguished Young Scholars(JCL201802)the Development plan of Youth Innovation Team in Colleges and Universities of Shandong Province.
文摘Photocatalysis is an effective way to solve the problems of environmental pollution and energy shortage.Numerous photocatalysts have been developed and various strategies have been proposed to improve the photocatalytic performance.Among them,Bi-based photocatalysts have become one of the most popular research topics due to their suitable band gaps,unique layered structures,and physicochemical properties.In this review,Bi-based photocatalysts(BiOX,BiVO_(4),Bi_(2)S_(3),Bi_(2)MoO_(6),and other Bi-based photocatalysts)have been summarized in the field of photocatalysis,including their applications of the removal of organic pollutants,hydrogen production,oxygen production etc.The preparation strategies on how to improve the photocatalytic performance and the possible photocatalytic mechanism are also summarized,which could supply new insights for fabricating high-efficient Bi-based photocatalysts.Finally,we summarize the current challenges and make a reasonable outlook on the future development direction of Bi-based photocatalysts.
基金supported by the National Natural Science Foundation of China(U1960107)the Natural Science Foundation of Hebei Province(E2022501014)+3 种基金the “333”Talent Project of Hebei Province(A202005018)the Fundamental Research Funds for the Central Universities(N2123034)the Science and Technology Research Youth Fund Project of Higher Education Institutions of Hebei Province(QN2022196)the Performance subsidy fund for Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province(22567627H)。
文摘Fe-Mn based layer oxides cathode materials have attracted widespread attention as a potential candidate for sodium-ion batteries(SIBs)owing to the earth abundance,cost-effectiveness and acceptable specific capacity.However,the irreversible phase transition often brings rapid capacity decay,which seriously hinders the practical application in large-scale energy storage.Herein,we design a nickel-doped Na_(0.70)Fe_(0.10)Cu_(0.20)Ni_(0.05)Mn_(0.65)O_(2)(NFCNM-0.05)cathode material of SIBs with activated anionic redox reaction,and then inhibit the harmful phase transition.The ex-situ X-ray diffraction patterns demonstrate the NFCNM-0.05 always keeps the P2 phase during the sodiation/desodiation process,indicating a high structure stability.The ex-situ X-ray photoelectron spectroscopy implies the redox reactions between O2-and O-occur in the charging process,which offers extra specific capacity.Thus,the NFCNM-0.05 electrode delivers a high initial discharge capacity of 148 mA h g-1and remains a prominent cycling stability with an excellent capacity retention of 95.9%after 200 cycles at 1 C.In addition,the electrochemical impedance spectroscopy and galvanostatic intermittent titration technique show the NFCNM-0.05 electrode possesses fast ion diffusion ability,which is beneficial for the enhancement of rate performance.Even at 10 C,the NFCNM-0.05 can offer a reversible discharge capacity of 81 mA h g-1.DFT calculation demonstrates the doping of appropriate amount of Ni ions is benefit for the enhancement of the electrochemical performance of the layer oxides.This work provides an effective strategy to enhance the electrochemical performance of Fe-Mn based cathode materials of SIBs.
