Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

Predicting the popularity of online news is essential for news providers and recommendation systems.Time series,content and meta-feature are important features in news popularity prediction.However,there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance.This work proposes a novel deep learning model named Multiple Features Dynamic Fusion(MFDF)for news popularity prediction.For modeling time series,long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity.The typical convolution neural network gets headline semantic representation for modeling news headlines.In addition,a hierarchical attention network is exploited to extract news content semantic representation while using the latent Dirichlet allocation model to get the subject distribution of news as a semantic supplement.A factorization machine is employed to model the interaction relationship between metafeatures.Considering the role of these features at different stages,the proposed model exploits a time-based attention fusion layer to fuse multiple features dynamically.During the training phase,thiswork designs a loss function based on Newton’s cooling law to train the model better.Extensive experiments on the real-world dataset from Toutiao confirm the effectiveness of the dynamic fusion of multiple features and demonstrate significant performance improvements over state-of-the-art news prediction techniques.

Deep Learning Model for News Quality Evaluation Based on Explicit and Implicit Information

Recommending high-quality news to users is vital in improving user stickiness and news platforms’reputation.However,existing news quality evaluation methods,such as clickbait detection and popularity prediction,are challenging to reflect news quality comprehensively and concisely.This paper defines news quality as the ability of news articles to elicit clicks and comments from users,which represents whether the news article can attract widespread attention and discussion.Based on the above definition,this paper first presents a straightforward method to measure news quality based on the comments and clicks of news and defines four news quality indicators.Then,the dataset can be labeled automatically by the method.Next,this paper proposes a deep learning model that integrates explicit and implicit news information for news quality evaluation(EINQ).The explicit information includes the headline,source,and publishing time of the news,which attracts users to click.The implicit information refers to the news article’s content which attracts users to comment.The implicit and explicit information affect users’click and comment behavior differently.For modeling explicit information,the typical convolution neural network(CNN)is used to get news headline semantic representation.For modeling implicit information,a hierarchical attention network(HAN)is exploited to extract news content semantic representation while using the latent Dirichlet allocation(LDA)model to get the subject distribution of news as a semantic supplement.Considering the different roles of explicit and implicit information for quality evaluation,the EINQ exploits an attention layer to fuse them dynamically.The proposed model yields the Accuracy of 82.31%and the F-Score of 80.51%on the real-world dataset from Toutiao,which shows the effectiveness of explicit and implicit information dynamic fusion and demonstrates performance improvements over a variety of baseline models in news quality evaluation.This work provides empirical evidence for explicit and implicit factors in news quality evaluation and a new idea for news quality evaluation.

Analysis and Design of Scheduling Schemes for Wireless Networks with Unsaturated Traffic

Scheduling schemes assign limited resources to appropriate users,which are critical for wireless network performance.Most current schemes have been designed based on saturated traffic,i.e.,assuming users in networks always have data to transmit.However,the user buffer may sometimes be empty in actual network.Therefore,these algorithms will allocate resources to users having no data to transmit,which results in resource waste.In view of this,we propose new scheduling schemes for onehop and two-hop link scenario with unsaturated traffic.Furthermore,this paper analyzes their key network performance indicators,including the average queue length,average throughput,average delay and outage probability.The two scheduling algorithms avoid scheduling the links whose buffers are empty and thus improve the network resource utilization.For the one-hop link scenario,network provides differentiated services via adjusting the scheduling probabilities of the destination nodes(DNs)with different priorities.Among the DNs with same priority,the node with higher data arrival rate has larger scheduling probability.For the two-hop link scenario,we prioritize the scheduling of relay-to-destination(R-D)link and dynamically adjust the transmission probability of source-to-relay(S-R)link,according to the length of remaining buffer.The experiment results show the effectiveness and advantage of the proposed algorithms.

Composite wave-absorbing structure combining thin plasma and metasurface

In order to solve the thickness dependence of plasma absorption of electromagnetic waves and further reduce the backward radar scattering cross section(RCS)of the target,we designed a novel composite structure of a metasurface and plasma.A metasurface with three absorption peaks is designed by means of an equivalent circuit based on an electromagnetic resonance type metamaterial absorber.The reflection and absorption of the composite structure are numerically and experimentally verified.The finite integration method was used to simulate a composite structure of finite size to obtain the RCS.The experimental measurements of electromagnetic wave reflection were conducted by a vector network analyzer(Keysight N5234A)and horn antennas,etc.The research showed that the absorption capacity of this composite structure was substantially improved compared to either the plasma or the metasurface,and it is more convenient for application due to its low plasma thickness requirement and easy fabrication.

Valley-dependent topological edge states in plasma photonic crystals

Plasma photonic crystals designed in this paper are composed of gas discharge tubes to control the flow of electromagnetic waves.The band structures calculated by the finite element method are consistent with the experimental results which have two distinct attenuation peaks in the ranges of 1-2.5 GHz and 5-6 GHz.Electromagnetic parameters of the plasma are extracted by the Nicolson-Ross-Weir method and effective medium theory.The measured electron density is between 1×1011 cm-3 and1×1012 cm-3,which verifies the correctness of the parameter used in the simulation,and the collision frequency is near 1.5×1010 Hz.As the band structures are corroborated by the measured scattering parameters,we introduce the concept of photonic topological insulator based on the quantum Valley Hall effect into the plasma photonic crystal.A valley-dependent plasma photonic crystal with hexagonal lattice is constructed,and the phase transition of the valley K(K’)occurs by breaking the spatial inversion symmetry.Valley-spin locked topological edge states are generated and excited by chiral sources.The frequency of the non-bulk state can be dynamically regulated by the electron density.This concept paves the way for novel,tunable topological edge states.More interestingly,the Dirac cone is broken when the electron density increases to 3.1×1012 cm-3,which distinguishes from the methods of applying a magnetic field and changing the symmetry of the point group.

Diagnosis of primary clear cell carcinoma of the liver based on Faster region-based convolutional neural network

Background:Distinguishing between primary clear cell carcinoma of the liver(PCCCL)and common hepatocellular carcinoma(CHCC)through traditional inspection methods before the operation is difficult.This study aimed to establish a Faster region-based convolutional neural network(RCNN)model for the accurate differential diagnosis of PCCCL and CHCC.Methods:In this study,we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020.A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients’data in the training validation set,and established a convolutional neural network model to distinguish PCCCL and CHCC.The accuracy,average precision,and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.Results:A total of 4392 images of 121 patients(1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC)were uesd in test set for deep learning and establishing the model,and 1072 images of 30 patients(320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC)were used to test the model.The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962(95%confidence interval[CI]:0.931-0.992).The average precision of the model for diagnosing PCCCL was 0.908(95%CI:0.823-0.993)and that for diagnosing CHCC was 0.907(95%CI:0.823-0.993).The recall of the model for diagnosing PCCCL was 0.951(95%CI:0.916-0.985)and that for diagnosing CHCC was 0.960(95%CI:0.854-0.962).The time to make a diagnosis using the model took an average of 4 s for each patient.Conclusion:The Faster RCNN model can accurately distinguish PCCCL and CHCC.This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.

Decomposition of monazite in Bayan Obo rare earth ore by roasting of Na_(2)CO_(3)pellets

To resolve the issue of rotary kiln agglomeration during the sodium carbonate roasting of dolomite rare earth ore,this study introduces an oxidation-sodization pellet roasting method for decomposing mixed rare earth concentrates.The focus of this paper lies in understanding the bonding and roasting mechanism of sodium polyacrylate as a binder to dolomite ore and examining the process index of Na_(2)CO_(3)pellets roasting-acid leaching using X-ray diffraction(XRD),scanning ele ctron microscopy with energy dispersive spectroscopy(SEM-EDS),and zeta potential analysis,The results indicate that sodium polyacrylate facilitates the bonding of sodium carbonate to monazite via adsorption of positive and negative charges,and upon roasting at 750℃for 1.5 h to obtain rare earth oxides.Under conditions of a hydrochloric acid(HCl)concentration of 9 mol/L,a reaction for 60 min,a solid-to-liquid ratio(g:mL)of 1:5,and reaction temperature of 90℃,the leaching rates of rare earth elements and thorium(Th)reached maxima of 85.14%and 95.53%,respectively.The process results in a yield of 47.61%for fluorine(F)and89.25%for phosphorus(P).This research forms a foundation for the sodium carbonate roasting decomposition of mixed rare earth concentrates.

Phase change,micro-structure and reaction mechanism during high temperature roasting of high grade rare earth concentrate

The deposit of Bayan Obo in Inner Mongolia is the world’s largest rare earth element(abbreviated as REE)resource.The exploration of the theory of mineral formation of Bayan Obo is an important foundation for mineralogical research,and is the scientific basis for mining,industrial beneficiation,smelting and extraction,and processing and utilization.With the rapid development of science and technology,the demand for the utilization of rare earth elements is increasing,and the separation process between rare earth elements needs to be developed.The purpose of this paper is to provide high temperature experimental information for the formation and application of rare earth minerals.To this end,the mineral evolution of high-grade rare earth concentrates with increasing temperature and the migration of rare earths at different stages and their reaction mechanisms were studied.According to thermogravimetric analysis and differential scanning calorimetry(TG-DSC),calcination was carried out at different temperature ranges,and the calcined products were characterized by X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FT-IR),scanning electron micro scope and energy dispers ive spectrometer(SEM-EDS)and other analytical techniques.The re sults are shown in this process,the ra re earth phase is first converted into rare earth oxide and rare earth oxyfluoride.As the temperature increases,Ca5(PO4)3 F and a large number of self-shaped spherical Ca-RE-OF and Ca-RE-PO4 particles are formed,and the separation of La and Ce elements is discovered.Acco rding to the phase diagram analysis,the production of Ca5(PO4)3 F is due to the reaction of monazite and fluorite,and the phases CeF2 and Ce F3 are formed during the reaction.When it reaches 1500℃,barium ferrite is produced and a new substance containing Ba2+is formed.

Decomposition of mixed rare earth concentrate by NaOH roasting and kinetics of hydrochloric acid leaching process

A new clean extraction technology for the decomposition of Bayan Obo mixed rare earth concentrate by NaOH roasting is proposed.The process mainly includes NaOH roasting to decompose rare earth concentrate and HCl leaching roasted ore.The effects of roasting temperature,roasting time,NaOH addition amount on the extraction of rare earth and factors such as HCl concentration,liquid-solid ratio,leaching temperature and leaching time on the dissolution kinetics of roasted ore were studied.The experimental results show that when the roasting temperature is 550℃and the roasting time is 60 min,the mass ratio of NaOH:rare earth concentrate is 0.60:1,the concentration of HCl is 6.0 mol/L,the ratio of liquid to solid(L/S)6.0:1.0,and the leaching temperature 90℃,leaching time 45 min,stirring speed 200 r/min,and the extraction of rare earth can reach 92.5%.The relevant experimental data show that the process of HCl leaching roasted ore conforms to the shrinking core model,but the control mechanism of the che mical reaction process is different when the leaching temperature is different.When the leaching temperature is between 40 and 70℃,the chemical reaction process is controlled by the diffusion of the product through the residual layer of the inert material.The average surface activation energy of the rare earth element is E_a=9.96 kJ/mol.When the leaching temperature is 75-90℃,the chemical reaction process is controlled by the interface transfer across the product layer(product layer interface mass transfer)and diffusion.The average surface activation energy of rare earth elements is E_a=41.65 kJ/mol.The results of this study have certain significance for the green extraction of mixed rare earth ore.

High-responsivity,self-driven photodetectors based on monolayer WS2/GaAs heterojunction

Constructing two-dimensional(2D)layered materials with traditional three-dimensional(3D)semiconductors into complex heterostructures has opened a new platform for the development of optoelectronic devices.Herein,large-area high performance self-driven photodetectors based on monolayer WS2∕GaAs heterostructures were successfully fabricated with a wide response spectrum band ranging from the ultraviolet to near-infrared region.The detector exhibits an overall high performance,including high photoresponsivity of 65.58 A/W at 365 nm and 28.50 A/W at 880 nm,low noise equivalent power of 1.97×10^−15 W∕Hz1∕2,high detectivity of 4.47×10^12 Jones,and fast response speed of 30/10 ms.This work suggests that the WS2∕GaAs heterostructure is promising in future novel optoelectronic device applications,and also provides a low-cost,easy-to-process method for the preparation of 2D/3D heterojunction-based devices.

A New Recursive Composite Adaptive Controller for Robot Manipulators

In this paper,a new recursive implementation of composite adaptive control for robot manipulators is proposed.We investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form,which,to our knowledge,is the first result on this point in the literature.The proposed algorithm has an amount of computation O(n),which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree manipulators.The manipulator of the Chinese Space Station is employed as a simulation example,and the results verify the effectiveness of this proposed recursive algorithm.

Voronoi Cell Finite Element Study on Particle-Reinforced Composites Containing Interphases Considering Both Interfacial Debonding and Thermal Stress

This paper derives the complementary energy functional based on the Voronoi element of particle-reinforced composites containing interphases to compute the interfacial debonding and thermal stress.When calculating interfacial debonding stress,it is assumed that the surface force is zero at the interface where debonding occurs,and a new modified complementary energy functional is derived with this boundary condition.When considering the thermal stress due to temperature change,the thermal strain is introduced into the complementary energy functional,and the thermal stress is then calculated.According to the derived formula,a Fortran program named Voronoi cell finite element model(VCFEM)is written.The interfacial debonding and thermal stress is calculated using both VCFEM and the finite element software MARC,and the calculation results are compared.It shows that the calculation results of the VCFEM are roughly comparable to those of the MARC,verifying the effectiveness of the VCFEM.