Effect of FeSi additive in dual-chamber sample cup on thermal analysis characteristic values and vermiculating rate of compacted graphite iron

Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.

Feasibility of measuring moisture content of green sand by a low frequency multiprobe detector based on dielectric characteristics

Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of green sand and transmission line theory,a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed.A system was constructed,where six detectors with different arrangements and probes were designed.The experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz.A voltage difference platform occurs in the range of 29-35 MHz,which is suitable for measuring the moisture content due to its insensitivity to frequency.The electric field intensity gradually decreases with the increase of the probe depth,and the intensity of central probe is always greater than that of the edge probe.When the distance of the probe away from the sand sample surface is 80 mm,the electric field intensity of the edge probe is found to be very weak.The optimal excitation frequency for measuring the moisture content of green sand is 29-33 MHz.The optimal detector is the one with one center probe and three edge probes,and their lengths are 80 mm and 60 mm,respectively.The distance between the center and edge probes is 25 mm,and the diameter of probes is 5 mm.Taking the voltage difference of transmission line,bentonite content,coal powder content and compactability as parameters of the input layer,and the moisture content as a parameter of the output layer,a three-layer BP artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 MHz.The prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%-7wt.%.

Effect of printing parameters on properties of 3D printing sand samples

Three-dimensional sand printing(3DSP)is widely applied in sand mold fabrication.In this study,the effects of printing parameters including the resolution of printehead holes,activator content,layer thickness,and recoating speed on the tensile and bending strengths,gas evolution,and loss-on-ignition(LOI)of 3DSP samples were investigated by changing single parameter,and the dimension deviation was also measured.As the resolution increases,the tensile strength,bending strength,gas evolution,LOI,and deviations at X-and Y-axis directions decrease gradually while the deviation at Z-axis direction firstly increases and then deceases.The gas evolution and LOI drops by 13.02%and 8.13%respectively,but the strength only reduces by 2.2% when the resolution increases from 0.08 mm to 0.09 mm.The strengths of samples rise at first and then decline while the gas evolution and LOI rise gradually with the increasing activator content or recoating speed.The activator content is found to have little effect on the gas evolution as the activator increases from 0.14%to 0.34%,the gas evolution is increased by 7.3%which is far less than the LOI increment of 24.1%.As the layer thickness increases,the tensile and bending strengths firstly rise and then drop while gas evolution and LOI descend.Under the optimal printing parameters of 0.09 mm resolution,0.18%activator,-10.28 mm layer thickness and 160 mm·s^(-1) recoating speed,the tensile strengths for X-sample and Y-sample are 1.48 MPa and 1.37 MPa,the bending strengths are 1.84 MPa and 1.75 MPa,the gas evolution and LOI are-19.62 mL·g^(-1) and 1.92%,respectively.

Generation of breathing solitons in the propagation and interactions of Airy–Gaussian beams in a cubic–quintic nonlinear medium

Using the split-step Fourier transform method, we numerically investigate the generation of breathing solitons in the propagation and interactions of Airy–Gaussian（AiG） beams in a cubic–quintic nonlinear medium in one transverse dimension. We show that the propagation of single AiG beams can generate stable breathing solitons that do not accelerate within a certain initial power range. The propagation direction of these breathing solitons can be controlled by introducing a launch angle to the incident AiG beams. When two AiG beams accelerated in opposite directions interact with each other,different breathing solitons and soliton pairs are observed by adjusting the phase shift, the beam interval, the amplitudes,and the light field distribution of the initial AiG beams.

The Effect of Bi_2O_3 on the Structure and Microwave Dielectric Properties of Ba_(6-3x)Nd_(8+2x)Ti_(18)O_(54)System(x=2/3)

The structures and dielectric properties of Ba6-3xNd8＋2xTi18O54 system（x=2/3） doped with different contents of Bi2O3, whose final molecular formula is Ba6-3x（Nd1-yBiy）8＋2xTi18O54 were investigated. It is indicated that the dielectric constant increases greatly whereas Q value（f0=4 GHz） decreases with the increase of Bi2O3 content. However, the temperature coefficient could be controlled below 0±30×10^-6/℃ in the experiment. These phenomena are related to the appearance of a new phase, Bi4Ti3O12, which has high dielectric constant. Also, that Bi^3＋（0.13 nm） substitutes for Nd^3＋（0.099 5 nm） will increase the unit cell volume, which will lead to the enlargement of the octahedron B site occupied by Ti^4＋. So the spontaneous polarization of Ti^4＋ ions will be strengthened. Besides, Bi^3＋ will fill up some vacancies which Ba^2＋ or Nd^3＋ ions leave in two A1 sites and four A2 sites. More positive ions polarize, which also contributes to higher dielectric constant. The samples got with the optimium properties are sintered at 1 200 ℃ for 4 h, when y=0.25, ε≈110, Q≈5 400（f0=4 GHz）, TCC=-4.7×10^-6/℃; When y=0.3, ε≈120, Q≈5 000（f0=4 GHz）, TCC=-24×10^-6/℃.

Phase Structure and Dielectric Properties of Doped BaTiO_3 Ceramics

The effects of two rare earth oxides such as CeO2 and Sm2O3 on the phase structure and dielectric properties of BaTiO3 ceramic were investigated. Results indicate that the dielectric constant of this system will increase greatly with the increasing content of these two oxides, and Ce^4＋ substitutes for Ba^2＋ located at A-site in ABO3 structure. Quantitative XRD analysis shows that c/a ratio in the sample with addition of CeO2 will increase, which implies the increase of tetragonality in system, causing the augment of dielectric constant, and the decrease of the crystal＇s geometrical symmetry results in curie-temperature moving towards low temperature; Sm^3＋（0.096 nm） substitutes for Ba^2＋（0.135 nm） possessing larger radius in A-site and the electrovalency in A-site increases, the mutual effect is strengthened, so the polarization is enhanced, and the dielectric constant increases notablely.

Accurate Analyses of the On-Axis Fields of a Circular Disk by Transient and Harmonic Excitation

The computation of near zone fields is very important in many practical applications.Accurate analyses of the on-axis fields of a circular disk by transient and harmonic excitation show that there is an energy uprising zone on the axis rather than a non-decaying one,provided that the disk is excited by a rectangular pulse,the duration of which is less than the ratio of the radius to the light velocity.Similar results corresponding to the time-harmonic excitation are also given,which are quite different from the conclusions derived by Silver.

Impediments of Cognitive System Engineering in Machine-Human Modeling

A comprehensive understanding of human intelligence is still an ongoing process,i.e.,human and information security are not yet perfectly matched.By understanding cognitive processes,designers can design humanized cognitive information systems(CIS).The need for this research is justified because today’s business decision makers are faced with questions they cannot answer in a given amount of time without the use of cognitive information systems.The researchers aim to better strengthen cognitive information systems with more pronounced cognitive thresholds by demonstrating the resilience of cognitive resonant frequencies to reveal possible responses to improve the efficiency of human-computer interaction(HCI).Apractice-oriented research approach included research analysis and a review of existing articles to pursue a comparative research model;thereafter,amodel development paradigm was used to observe and monitor the progression of CIS during HCI.The scope of our research provides a broader perspective on how different disciplines affect HCI and how human cognitive models can be enhanced to enrich complements.We have identified a significant gap in the current literature on mental processing resulting from a wide range of theory and practice.

Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

Recent progress on Sn_(3)O_(4)nanomaterials for photocatalytic applications

Tin(IV)oxide(Sn_(3)O_(4))is layered tin and exhibits mixed valence states.It has emerged as a highly promising visible-light pho-tocatalyst,attracting considerable attention.This comprehensive review is aimed at providing a detailed overview of the latest advance-ments in research,applications,advantages,and challenges associated with Sn_(3)O_(4)photocatalytic nanomaterials.The fundamental con-cepts and principles of Sn_(3)O_(4)are introduced.Sn_(3)O_(4)possesses a unique crystal structure and optoelectronic properties that allow it to ab-sorb visible light efficiently and generate photoexcited charge carriers that drive photocatalytic reactions.Subsequently,strategies for the control and improved performance of Sn_(3)O_(4)photocatalytic nanomaterials are discussed.Morphology control,ion doping,and hetero-structure construction are widely employed in the optimization of the photocatalytic performance of Sn_(3)O_(4)materials.The effective imple-mentation of these strategies improves the photocatalytic activity and stability of Sn_(3)O_(4)nanomaterials.Furthermore,the review explores the diverse applications of Sn_(3)O_(4)photocatalytic nanomaterials in various fields,such as photocatalytic degradation,photocatalytic hydro-gen production,photocatalytic reduction of carbon dioxide,solar cells,photocatalytic sterilization,and optoelectronic sensors.The discus-sion focuses on the potential of Sn_(3)O_(4)-based nanomaterials in these applications,highlighting their unique attributes and functionalities.Finally,the review provides an outlook on the future development directions in the field and offers guidance for the exploration and de-velopment of novel and efficient Sn_(3)O_(4)-based nanomaterials.Through the identification of emerging research areas and potential avenues for improvement,this review aims to stimulate further advancements in Sn_(3)O_(4)-based photocatalysis and facilitate the translation of this promising technology into practical applications.

Knowledge Graph and Knowledge Reasoning:A Systematic Review

The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Estimation of detection threshold in multiple ship target situations with HF ground wave radar

A credible method of calculating the detection threshold is presented for the multiple target situations, which appear frequently in the lower Doppler velocity region during the surveillance of sea with HF ground wave radar. This method defines a whole-peak-outlier elimination （WPOE） criterion, which is based on in-peak-samples correlation of each target echo spectra, to trim off the target signals and abnormal disturbances with great amplitude from the complex spectra. Therefore, cleaned background noise samples are obtained to improve the accuracy and reliability of noise level estimation. When the background noise is nonhomogeneous, the detection samples are limited and often occupied heavily with outliers. In this case, the problem that the detection threshold is overvalued can be solved. In applications on experimental data, it is verified that this method can reduce the miss alarm rate of signal detection effectively in multiple target situations as well as make the adaptability of the detector better.

The Asymptotic Study of Smooth Entropy Support Vector Regression

In this paper, a novel formulation, smooth entropy support vector regression (SESVR), is proposed, which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with an ε-insensitive support vector regression. An entropy penalty function is substituted for the plus function to make the objective function con- tinuous ,and a new algorithm involving the Newton-Armijo algorithm proposed to solve the SESVR converge globally to the solution. Theoretically, we give a brief convergence proof to our algorithm. The advantages of our presented algorithm are that we only need to solve a system of linear equations iteratively instead of solving a convex quadratic program, as is the case with a conventional SVR, and lessen the influence of the penalty parameter C in our model. In order to show the efficiency of our algorithm, we employ it to forecast an actual electricity power short-term load. The experimental results show that the presented algorithm, SESVR, plays better precisely and effectively than SVMlight and LIBSVR in stochastic time series forecasting.

A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID-19 since its outbreak.The Internet of Things(IoT)along with other technologies like Machine Learning can revolutionize the traditional healthcare system.Instead of reactive healthcare systems,IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services.In this study,a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection.The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency,short response time,and optimal energy consumption.In this paper,the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models.The proposed models were validated using kcross-validation to ensure the consistency of models.Based on the experimental results,our proposed models have recorded good accuracies with highest of 97.767%by Support Vector Machine.According to the findings of experiments,the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects,as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.

An HLA/RTI Architecture Based on Multi-thread Processing

In order to improve the real-time performance of the real-time HLA(high level architecture) in the application of massive data communication volume,multi-thread processing was adopted,thread pool structure was introduced into the system,different threads to handle corresponding message queues was utilized to respond different message requests.Furthermore,an allocation strategy of semi-complete deprivation of priority was adopted,which reduces thread switching cost and processing burden in the system,provided that the message requests with high priority can be responded in time,thus improves the system's overall performance.The design and experiment results indicate that the method proposed in this paper can improve the real-time performance of HLA in distributed system applications greatly.

A parallel complex divider architecture based on DCD iterations for computing complex division in MVDR beamformer

This paper presents a hardware architecture using mixed pipeline and parallel processing for complex division based on dichotomous coordinate descent(DCD) iterations. The objective of the proposed work is to achieve low-latency and resource optimized complex divider architecture in adaptive weight computation stage of minimum variance distortionless response(MVDR)algorithm. In this work, computation of complex division is modeled as a 2×2 linear equation solution problem and the DCD algorithm allows linear systems of equations to be solved with high degree of computational efficiency. The operations in the existing DCD algorithm are suitably parallel pipelined and the performance is optimized to 2 clock cycles per iteration. To improve the degree of parallelism, a parallel column vector read architecture is devised.The proposed work is implemented on the field programmable gate array(FPGA) platform and the results are compared with state-of-art literature. It concludes that the proposed architecture is suitable for complex division in adaptive weight computation stage of MVDR beamformer. We demonstrate the performance of the proposed architecture for MVDR beamformer employed in medical ultrasound imaging applications.

Knowledge-based recommendation with contrastive learning

Knowledge Graphs(KGs)have been incorporated as external information into recommendation systems to ensure the high-confidence system.Recently,Contrastive Learning(CL)framework has been widely used in knowledge-based recommendation,owing to the ability to mitigate data sparsity and it considers the expandable computing of the system.However,existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge:(1)For the knowledge view generation,they only perform simple data augmentation operations on KGs,resulting in the introduction of noise and irrelevant information,and the loss of essential information.(2)For the knowledge view encoder,they simply add the edge information into some GNN models,without considering the relations between edges and entities.Therefore,this paper proposes a Knowledge-based Recommendation with Contrastive Learning(KRCL)framework,which generates dual views from user–item interaction graph and KG.Specifically,through data enhancement technology,KRCL introduces historical interaction information,background knowledge and item–item semantic information.Then,a novel relation-aware GNN model is proposed to encode the knowledge view.Finally,through the designed contrastive loss,the representations of the same item in different views are closer to each other.Compared with various recommendation methods on benchmark datasets,KRCL has shown significant improvement in different scenarios.