不同模具角度下反复弯曲-压平变形工艺对AZ31镁合金板材显微组织演变和变形行为的影响

采用不同角度模具的反复弯曲-压平变形工艺制备AZ31镁合金板材。通过FEM、OM、EBSD和硬度计研究AZ31镁合金在反复弯曲-压平变形过程中的显微组织演变和变形行为。结果表明,150°/150°模具在所有三组实验中都表现出最佳性能。随着道次的增加,合金的等效应变由于剪切和弯曲作用而显著提高。经过4道次后,合金的平均晶粒尺寸显著细化至1.7μm,基面织构被弱化,这是非基面滑移、动态再结晶和孪生引起的;尤其是锥面滑移有利于引发动态再结晶和孪生。合金的硬度值达到HV 77,这是滑移、孪生和动态再结晶竞争产生的显微组织和织构综合作用的结果。

“薛定谔方程–近似模型–核心概念–简单应用”——构建原子/分子结构教学内容的逻辑框架与知识点图谱

针对结构化学课程知识点繁多和概念碎片化、学生难以形成学科框架和完整知识体系的困境,本文提出以“薛定谔方程–近似模型–核心概念–简单应用”为主线,以一维势箱自由粒子模型、单电子类H体系、多电子原子、H_(2)^(+)分子离子、H_(2)分子、多原子分子等“六条分支”来构建学科的逻辑框架与知识点图谱。使碎片化的知识重构关联,形成完整的知识链,帮助学生理解重要概念的逻辑关系、产生脉络及相互联系,并且为学生提供知识迁移的情境,达到知识理解与运用的目的。

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

The History of Controlling and Treating Infectious Diseases in Ancient China

Infectious diseases are the common enemies of mankind.In the course of historical development,they persistently threaten human health and safety.Even today,despite the developments in medical science,we cannot escape the fear and suffering caused by infectious diseases.Whether in ancient or modern times,the source of infection,route of transmission,and a susceptible population are the three key conditions for the prevalence and spread of infectious diseases.All factors closely related to these three conditions can affect the prevalence of infectious diseases.China is one of the cradles of world civilization.The ancient people accumulated a great deal of experience and lessons in the long struggle against infectious diseases.In the face of the current threat posed by widespread infectious disease,it is imperative to review and summarize ancient Chinese ideas and health policies on epidemic prevention and control to inspire contemporary efforts in the prevention and control of infectious disease.The combination of prevention-oriented epidemic prevention ideology and traditional medicine provides valuable insights,especially for impoverished and medically underserved regions.

In-situ defect passivation assisted three-step printing of efficient and stable formamidine-lead bromide solar cells

Perovskite solar cells(PSCs)emerge as the most promising photovoltaics(PV)for their high performance and potential convenient cost-effective production routes comparing to the sophomore PV technologies.The printed PSCs with simplified device architecture and fabrication procedures could further enhance the competitive strength of PSC technology.In this work,we present an in-situ defect passivation(ISDP)assisted full-printing of high performance formamidine-lead bromide(FAPbBr_(3))PSCs.Only three rapid printing steps are involved for electron transporting layer(ETL),perovskite and carbon to form a complete solar cell on the low-cost fluorine-doped tin oxide(FTO)substrate.Long-chain polymer monomethyl ether polyethylene glycol is particularly utilized as the ISDP passivator,leading to conformal coating on the rough FTO and defect passivation for both ETL and perovskite during printing.A high efficiency of 10.85%(certified 10.14%)and a high V_(oc)up to 1.57 V are achieved for the printed device.The unencapsulated PSCs maintain above 90%of the initial efficiency after continuously heating at 85℃for 1000 h and over 80%of the efficiency after the maximum power point tracking for 3500 h.The fully printed semitransparent PSCs with carbon grids(CGs)show average visible light transmittance over 33%and an efficiency of 8.81%.

Revealing the role of calcium ion intercalation of hydrated vanadium oxides for aqueous zinc-ion batteries

Exploring suitable high-capacity V_(2)O_(5)-based cathode materials is essential for the rapid advancement of aqueous zinc ion batteries(ZIBs).However,the typical problem of slow Zn^(2+)diffusion kinetics has severely limited the feasibility of such materials.In this work,unique hydrated vanadates(CaVO,BaVO)were obtained by intercalation of Ca^(2+)or Ba^(2+)into hydrated vanadium pentoxide.In the CaVO//Zn and BaVO//Zn batteries systems,the former delivered up to a 489.8 mAh g^(-1)discharge specific capacity at 0.1 A g^(-1).Moreover,the remarkable energy density of 370.07 Wh kg^(-1)and favorable cycling stability yard outperform BaVO,pure V_(2)O_(5),and many reported cathodes of similar ionic intercalation compounds.In addition,pseudocapacitance analysis,galvanostatic intermittent titration(GITT)tests,and Trasatti analysis revealed the high capacitance contribution and Zn^(2+)diffusion coefficient of CaVO,while an in-depth investigation based on EIS elucidated the reasons for the better electrochemical performance of CaVO.Notably,ex-situ XRD,XPS,and TEM tests further demonstrated the Zn^(2+)insertion/extraction and Zn-storage mechanism that occurred during the cycle in the CaVO//Zn battery system.This work provides new insights into the intercalation of similar divalent cations in vanadium oxides and offers new solutions for designing cathodes for high-capacity aqueous ZIBs.

Micro-nano-fabrication of green functional materials by multiphase microfluidics for environmental and energy applications

Multiphase microfluidic has emerged as a powerful platform to produce novel materials with tailor-designed functionalities,as microfluidic fabrication provides precise controls over the size,component,and structure of resultant materials.Recently,functional materials with well-defined micro-/nanostructures fabricated by microfluidics find important applications as environmental and energy materials.This review first illustrated in detail how different structures or shapes of droplet and jet templates are formed by typical configurations of microfluidic channel networks and multiphase flow systems.Subsequently,recent progresses on several representative energy and environmental applications,such as water purification,water collecting and energy storage,were overviewed.Finally,it is envisioned that integrating microfluidics and other novel materials will play increasing important role in contributing environmental remediation and energy storage in near future.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Genetically-and environmentally-dependent processes drive interspecific and intraspecific divergence in the Chinese relict endemic genus Dipteronia

China is a hotspot of relict plant species that were once widespread throughout the Northern Hemisphere.Recent research has demonstrated that the occurrence of long-term stable refugia in the mountainous regions of central and south-western China allowed their persistence through the late Neogene climate fluctuations.One of these relict lineages is Dipteronia,an oligotypic tree genus with a fossil record extending to the Paleocene.Here,we investigated the genetic variability,demographic dynamics and diversification patterns of the two currently recognized Dipteronia species(Dipteronia sinensis and D.dyeriana).Molecular data were obtained from 45 populations of Dipteronia by genotyping three cpDNA regions,two single copy nuclear genes and 15 simple sequence repeat loci.The genetic study was combined with niche comparison analyses on the environmental space,ecological niche modeling,and landscape connectivity analysis.We found that the two Dipteronia species have highly diverged both in genetic and ecological terms.Despite the incipient speciation processes that can be observed in D.sinensis,the occurrence of long-term stable refugia and,particularly,a dispersal corridor along Daba Shan-west Qinling,likely ensured its genetic and ecological integrity to date.Our study will not only help us to understand how populations of Dipteronia species responded to the tectonic and climatic changes of the Cenozoic,but also provide insight into how Arcto-Tertiary relict plants in East Asia survived,evolved,and diversified.

Prediction of Coefficient of Restitution for Impact Elastoplastic Spheres Considering Finite Plate Thickness

Collisions between objects are a relatively common phenomenon in nature.Analyses of collision processes can greatly contribute to solving problems such as impact-rub faults and particle impacts.The coefficient of restitution is a critical parameter in the analysis of collision processes.Many experiments have shown that the coefficient of restitution is closely related to the plate thickness,and the smaller the plate thickness,the more inaccurate the coefficient of restitution predicted by the existing model,which seriously affects the process of collision analysis.To remedy this shortcoming,this paper proposes a plate thickness influence factor with the ratio of sphere diameter to plate thickness as the variable.The plate thickness influence factor can optimize the coefficient of restitution model to effectively predict the coefficient of restitution of impacting elastoplastic spheres with finite plate thickness.Finally,the validity of the new model is verified using a large amount of experimental data.

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions.To solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this paper.Firstly,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate lesions.Secondly,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after downsampling.Thirdly,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic information.Finally,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local attention.The proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,respectively.It is of great significance for computer-aided diagnosis.

Colorectal Cancer Segmentation Algorithm Based on Deep Features from Enhanced CT Images

Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

罕见双峰织构在提高AZ31镁合金板材冷轧成形性能中的作用

对基极由板材法向向轧向偏离±40°的罕见双峰织构AZ31镁合金板材进行多道次冷轧实验研究。该板材由等径角轧制−连续弯曲−退火工艺制备。实验结果表明,该罕见双峰织构能大幅度提高板材的冷轧累积减薄率至39.2%,是传统强基面织构板材冷轧累积减薄率的2倍以上(18.3%)。金相和电子背散射衍射分析表明,该罕见双峰织构板材在冷轧过程中的显微组织及织构演化与传统强基面织构板材的区别显著。施密特因子(SF)分析表明,在冷轧过程中,基面<a>滑移和{1012}拉伸孪晶的SF值较大,而柱面<a>滑移和锥面<c+a>滑移的SF值较小。因此,基面<a>滑移和{1012}拉伸孪晶在该罕见双峰织构AZ31镁合金板材冷轧过程中将被大量激活来承载塑性应变,进而显著提高板材的冷轧成形性能。

Personnel Localization of Real-Time Kinematic Based on Pedestrian Heading Projection Compensation in Substation Signal Interference Environment

To address the intermittent positioning and drift of personnel positioning RTK in the high-frequency signal interference environment of substations, we propose to use IMU as the positioning compensation module of RTK and adopt the joint RTK/PDR positioning method to solve the positioning results. The heading angle is easily scattered in the pedestrian heading projection (PDR) process and the heading angles calculated from the output data of the gyroscope, accelerometer and magnetometer after denoising are input into the complementary filter for fusion. To improve the accuracy of step estimation in the PDR process, an improved step estimation model is used. For RTK/PDR data fusion, the extended Kalman filter (EKF) method is used, which helps to achieve outdoor full-scene high-accuracy positioning. The final simulation results show that RTK can be effectively compensated by PDR under the interference of high-frequency signals, and the positioning accuracy reaches 0.02 m.

An Improved Particle Filter Map Matching Algorithm for Personal Inertial Positioning

The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algorithm for the inertial positioning of personnel. The historical moment position constraint and feasible region constraint of particles were introduced in this paper. A resampling method based on multi-stage backtracking of particles was proposed. Therefore, the effectiveness of newly generated particles could be guaranteed. The utilization rate of map information could be improved, thus enhancing the accuracy of personnel localization. The walking experiment results showed that, compared with the traditional PDR algorithm, the proposed method had higher localization accuracy and better repeatability of the localization trajectory for multi-turn paths. Under the total travel of 480 meters, the deviation of the starting end point was less than 2 meters, which was about 0.4% of the total travel.

Intelligent Detection Method of Substation Environmental Targets Based on MD-Yolov7

The complex operating environment in substations, with different safety distances for live equipment, is a typical high-risk working area, and it is crucial to accurately identify the type of live equipment during automated operations. This paper investigates the detection of live equipment under complex backgrounds and noise disturbances, designs a method for expanding lightweight disturbance data by fitting Gaussian stretched positional information with recurrent neural networks and iterative optimization, and proposes an intelligent detection method for MD-Yolov7 substation environmental targets based on fused multilayer feature fusion (MLFF) and detection transformer (DETR). Subsequently, to verify the performance of the proposed method, an experimental test platform was built to carry out performance validation experiments. The results show that the proposed method has significantly improved the performance of the detection accuracy of live devices compared to the pairwise comparison algorithm, with an average mean accuracy (mAP) of 99.2%, which verifies the feasibility and accuracy of the proposed method and has a high application value.

硼化物原位自生增强TiAl基复合材料的组织演化、热变形行为及加工工艺设计

硼元素添加造成的相转变和硼化物析出等因素会对原位TiAl基复合材料显微组织演化及热变形行为产生影响。利用等温压缩实验、扫描电子显微技术以及透射电子显微技术等研究材料的动态再结晶和动态回复机制,并计算出其表现变形激活能为691.506 k J/mol。在1100~1200℃温度区间,再结晶γ和α晶粒的形核长大分别主导α2→α相转变温度上、下的热变形行为。α相的动态回复主导材料在1250℃低应变速率下的热变形行为;同时,硼元素会提高α相含量,降低γ→α和α2→α相转变温度,进而促进加载过程中回复α相晶粒的形核长大。根据新建的本构模型,对TiAl基复合材料的变形机制和加工工艺进行详细阐述.

Identification of XLPE cable insulation defects based on deep learning

The insulation aging of cross-linked polyethylene(XLPE)cables is the main reason for the reduction in cable life.There is currently a lack of rapid and effective methods for detecting cable insulation defects in power-related sectors.To this end,this paper presents a method for identifying insulation defects in XLPE cables based on deep learning algorithms.First,the principle of the harmonic method for detecting cable insulation defects is introduced.Second,the ANSYS software is used to simulate the cable insulation layer containing bubbles,protrusions,and water tree defects,and the effects of each type of defect on the magnetic field strength and eddy loss current of the cable insulation layer are analyzed.Then,a total of 10 characteristic quantities of the total harmonic content and 2nd to 10th harmonic currents are constructed to establish a database of cable insulation defects.Finally,the deep learning algorithm,long short-term memory(LSTM),is used to accurately identify the types of insulation defects in cables.The results indicate that the LSTM algorithm can effectively diagnose and identify insulation defects in cables with an accuracy of 95.83%.