Role of heterogenous microstructure and deformation behavior in achieving superior strength-ductility synergy in zinc fabricated via laser powder bed fusion

Zinc(Zn)is considered a promising biodegradable metal for implant applications due to its appropriate degradability and favorable osteogenesis properties.In this work,laser powder bed fusion(LPBF)additive manufacturing was employed to fabricate pure Zn with a heterogeneous microstructure and exceptional strength-ductility synergy.An optimized processing window of LPBF was established for printing Zn samples with relative densities greater than 99%using a laser power range of 80∼90 W and a scanning speed of 900 mm s−1.The Zn sample printed with a power of 80 W at a speed of 900 mm s−1 exhibited a hierarchical heterogeneous microstructure consisting of millimeter-scale molten pool boundaries,micrometer-scale bimodal grains,and nanometer-scale pre-existing dislocations,due to rapid cooling rates and significant thermal gradients formed in the molten pools.The printed sample exhibited the highest ductility of∼12.1%among all reported LPBF-printed pure Zn to date with appreciable ultimate tensile strength(∼128.7 MPa).Such superior strength-ductility synergy can be attributed to the presence of multiple deformation mechanisms that are primarily governed by heterogeneous deformation-induced hardening resulting from the alternative arrangement of bimodal Zn grains with pre-existing dislocations.Additionally,continuous strain hardening was facilitated through the interactions between deformation twins,grains and dislocations as strain accumulated,further contributing to the superior strength-ductility synergy.These findings provide valuable insights into the deformation behavior and mechanisms underlying exceptional mechanical properties of LPBF-printed Zn and its alloys for implant applications.

A review on heterogeneous photocatalysis for environmental remediation:From semiconductors to modification strategies

Heterogeneous photocatalysis,an advanced oxidation process,has garnered extensive attention in the field of environmental remediation because it involves the direct utilization of solar energy for the removal of numerous pollutants.However,the application of heterogeneous photocatalysis in environmental remediation has not achieved the expected consequences due to enormous challenges such as low photocatalytic efficiencies and high costs of heterogeneous photocatalysts in large-scale practical applications.Furthermore,pollutants in the natural environment,including water,air,and solid phases,are diverse and complex.Therefore,extensive efforts should be made to better understand and apply heterogeneous photocatalysis for environmental remediation.Herein,the fundamentals of heterogeneous photocatalysis for environmental remediation are introduced.Then,potential semiconductors and their modification strategies for environmental photocatalysis are systematically presented.Finally,conclusions and prospects are briefly summarized,and the direction for the future development of environmental photocatalysis is explored.This review may provide reference directions toward understanding,researching,and designing photocatalytic remediation systems for various environmental pollutants.

Load-Aware Offloading Strategy in Two-Tier Heterogeneous Network

Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.

Subsurface Temperature and Salinity Structures Inversion Using a Stacking-Based Fusion Model from Satellite Observations in the South China Sea

Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models,which are usually challenging and costly.Recently,dynamical,statistical,or machine learning models have been proposed to invert the OST/OSS from sea surface information;however,these models mainly focused on the inversion of monthly OST and OSS.To address this issue,we apply clustering algorithms and employ a stacking strategy to ensemble three models(XGBoost,Random Forest,and LightGBM)to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset.Subsequently,a fusion of temperature and salinity is employed to reconstruct OST and OSS.In the validation dataset,the depth-averaged Correlation(Corr)of the estimated OST(OSS)is 0.919(0.83),and the average Root-Mean-Square Error(RMSE)is0.639°C(0.087 psu),with a depth-averaged coefficient of determination(R~2)of 0.84(0.68).Notably,at the thermocline where the base models exhibit their maximum error,the stacking-based fusion model exhibited significant performance enhancement,with a maximum enhancement in OST and OSS inversion exceeding 10%.We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model(HYCOM)data and BOA_Argo dataset during the passage of a mesoscale eddy.This study shows that the proposed model can effectively invert the real-time OST and OSS,potentially enhancing the understanding of multi-scale oceanic processes in the SCS.

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.

A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model using Model Regionalization

Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.

Strategies for enhancing peroxymonosulfate activation by heterogenous metal-based catalysis:A review

Sulfate radical-advanced oxidation processes(SR-AOPs)are promising technologies for organic pollutants elimination.Heterogeneous metal-based catalysis has been widely studied and applied to activate peroxymonosulfate(PMS)for producing sulfate radicals.Developing highly efficient catalysts is crucial for future extensive use.Importantly,the catalytic activity is mainly determined by mass and electron transfer.This paper aims to overview the recent enhancement strategies for developing heterogeneous metalbased catalysts as effective PMS activators.The main strategies,including surface engineering,structural engineering,electronic modulation,external energy assistance,and membrane filtration enhancement,are summarized.The potential mechanisms for improving catalytic activity are also introduced.Finally,the challenges and future research prospects of heterogenous metal-based catalysis in SR-AOPs are proposed.This work is hoped to guide the rational design of highly efficient heterogenous catalysts in SR-AOPs.

NON-SYNDROMIC HEARING LOSS AND HIGH- THROUGHPUT STRATEGIES TO DECIPHER ITS GENETIC HETEROGENEITY

Hearing loss (HL) is the most common sensory disorder, affecting all age groups, ethnicities, and gen-ders. According to World Health Organization (WHO) estimates in 2005, 278 million people worldwide have moderate to profound HL in both ears. Results of the 2002 National Health Interview Survey indicate that nearly 31 million of all non-institutionalized adults (aged 18 and over) in the United States have trouble hearing. Epidemiological studies have estimated that approximately 50%of profound HL can be attributed to genetic causes. With over 60 genes implicated in nonsyndromic hearing loss, it is also an extremely het-erogeneous trait. Recent progress in identifying genes responsible for hearing loss enables otolaryngologists and other clinicians to apply molecular diagnosis by genetic testing. The advent of the $1000 genome has the potential to revolutionize the identification of genes and their mutations underlying genetic disorders. This is especially true for extremely heterogeneous Mendelian conditions such as deafness, where the muta-tion, and indeed the gene, may be private. The recent technological advances in target-enrichment methods and next generation sequencing offer a unique opportunity to break through the barriers of limitations im-posed by gene arrays. These approaches now allow for the complete analysis of all known deafness-causing genes and will result in a new wave of discoveries of the remaining genes for Mendelian disorders. This re-view focuses on describing genotype-phenotype correlations of the most frequent genes including GJB2, which is responsible for more than half of cases, followed by other common genes and on discussing the im-pact of genomic advances for comprehensive genetic testing and gene discovery in hereditary hearing loss.

Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.

Dynamic Modeling of Heterogeneous Spatial Analysis for Energy and Natural Resources in Grid Integration Environment

With the high-tech industrialization of earth observation satellite remote sensing and the implementation of digital earth strategy,the energy and natural resources have been decided to be the key research fields in China.In these fields,from the model based on topology data,through simple feature data model to rule-based data model,the basic spatial analysis algorithms have been developed

Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global model.Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution.Nonetheless,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model performance.To tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local classifiers.Additionally,FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters.Through experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of FedFCD.For instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning approaches.Furthermore,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

基于异构数据的患者术后非计划内再入院预测

非计划内再入院是医院风险管理的重要信号,也是医疗质量的重要指标。目前,再入院预测已经成为医疗系统的一项重要任务,大量学者结合机器学习技术提出非常多有效的预测方法,但大多仅以单一结构数据为研究对象或仅使用串联方法融合异构数据。前者未能充分利用电子病历中丰富的数据与信息,后者则未能更好地融合异构数据的信息。基于上述问题,本文提出了一种基于CTFN异构数据融合方法,结合患者出院小结文本与住院期间产生的横断面数据预测患者再入院风险。预测模型的构建分为3个步骤。首先,利用RoBerta模型提取患者出院小结中的特征信息并得到表征矩阵;其次,使用CNN模型学习患者横断面特征信息,得到表征矩阵;最后,通过CTFN方法融合两个表征矩阵,得到异构数据的表征矩阵并通过线性层分类器得到最后的预测结果。CTFN融合方法利用张量外积融合多个单模态表征矩阵,并增加CNN模型及残差结构设计加强异构数据模态内与模态间的信息学习。根据某公立医院的临床数据对上述方法进行验证,实验结果表明其表现出色,其中,召回率达到了76.1%,ROC曲线下面积达到了71.5%,均高于所对比的基线模型。证实了异构数据能提升分类器预测效果,且CTFN融合方法能够更好地融合异构数据间的信息,进一步提升分类器预测效果。

企业数字化转型前因组态及演进研究——基于战略三角观的动态fsQCA分析

数字经济背景下,企业数字化转型前因组态及转型路径时序性演进规律研究较为缺乏。基于战略三角观,以A股上市公司为样本,运用动态fsQCA方法探究制度、产业和动态能力与数字化转型的关系,结果表明:第一,数字化转型前因组态多重并发,战略三角协同匹配共同作用于数字化转型;第二,存在制度突出型、产业利用型、制度产业并驱型和战略三角配合型4种高数字化转型模式;第三,2014—2015、2016—2017、2018—2019三阶段数字化转型经历了“宏观制度导向—中观产业推动—微观企业内驱”的下沉过程;第四,区域异质性分析显示,东部地区企业主要依赖先行产业试点优势实现高水平数字化转型,中部地区企业借助制度条件实现高水平数字化转型,西部地区缺乏产业与制度优势,需要借助产业、制度、资源3方面条件实现高水平数字化转型。

数字基建驱动绿色创新的效应与机制研究——基于“宽带中国”战略的政策实验

推进以数字基础设施为代表的新型基础设施建设可以对城市的绿色创新发展产生重要影响并提供转型发展的新机遇。本文采用多期双重差分模型对中国278个城市在2010—2022年的面板数据进行实证分析,检验“宽带中国”战略对城市绿色创新的直接影响和作用机制。实证结果表明,“宽带中国”战略对试点城市的绿色专利申请数量和质量均有促进作用。机制检验结果表明,试点政策主要通过增强环境规制、促进产业结构升级和提升信息化水平影响城市的绿色创新水平。城市类型异质性分析结果表明,数字基础设施的建设对各类型试点城市的绿色创新数量均有促进作用,但是仅对东部地区、非资源型城市的绿色创新质量有显著提升;城市产业异质性分析结果表明,数字基础设施建设对数字产业、新能源产业的绿色创新发展起到了“数质齐升”的效果,有助于城市向绿色创新发展方向转型,进一步分析发现在绿色创新上仍存在“重数量、轻质量”的偏好,从而限制了对城市绿色全要素生产率的提升作用。

汽车线控转向系统齿条力观测方法研究

齿条力是线控转向系统路感设计和转向跟随控制中的关键参数,但是由于其在量产车中难以被直接测量,因此,对其观测成为线控转向中的一个关键环节。采用两种方法观测齿条力,一种是基于车辆动力学的观测方法,采用龙伯格观测器观测车辆侧向速度,并结合轮胎刷子模型分析转向阻力矩;另一种是基于转向动力学的观测方法,通过建立转向执行机构动力学模型,并采用卡尔曼滤波实现对齿条力的直接观测。在此基础上,通过硬件在环台架试验比较两种方法的齿条力观测效果,提出基于多变量的规则式融合策略对两种方法进行优势互补。通过台架试验验证所提出的融合策略能在不同工况下有效提高齿条力观测的准确性和实时性。

基于分层Logistic回归模型的高速公路货车差异化收费策略

针对高速公路货车差异化收费,大多以经济效益为导向,忽视社会效益及路网整体通行效率的问题。本文以综合效益(经济效益、社会效益)最大化为目标,构建适用于多地区的含有不同地区路段类别因子的分层Logistic回归模型。基于甘肃和新疆差异化收费项目数据对模型进行标定,假设3种收费情景并制定多样化的收费策略,分析货车行驶不同距离高速公路的分担率变化情况;针对目标高速公路对费率变化进行敏感性分析。结果表明,新疆G0711和G0612高速公路在制定的3种差异化收费策略下,高速公路货车分担率分别提升了4.99%、14.01%、8.34%。

面向多源异构的电力工程数据融合处理技术研究

随着电力工程中自动化、信息化设备的日益增多,对电力工程数据的分析与处理能力有了更高的要求。针对这一问题,文中开展了面向多源异构的电力工程数据融合处理技术研究。通过预先设计好的数据框架进行关联操作,实现数据监测与处理分析。为了提高整体数据与局部数据之间的协调性,对融合数据进行边缘自适应增强处理,结合电力工程定值数据处理方法将样本数据分解为多个子数据集,利用神经网络模型分类融合,采用Reduce机制对融合后的数据进行合并处理,输出结果,从而提高数据融合的效率。以某地区电力工程数据集为样本进行的分析结果表明,所提方法在处理数据时具有更高的效率,产生的绝对误差仅为1.675%,且更适用于大容量数据的场景。

Enhanced Grain Refining Effect of Mg–Zr Master Alloy on Magnesium Alloys via a Synergistic Strategy Involving Heterogeneous Nucleation and Solute‑Driven Growth Restriction

Zirconium(Zr)emerges as the most effective grain refiner for magnesium(Mg)alloys incorporating Zr.Typically,Zr is introduced in the form of an Mg–Zr master alloy.However,within Mg–Zr master alloys,Zr predominantly exists in a particle form,which tends to aggregate due to attractive van der Waals forces.The clustered Zr is prone to settling,thereby reducing its refining impact on Mg alloys.In this work,a combined pretreatment process for Mg–Zr master alloys was proposed,encompassing the introduction of a physical field to intervene the agglomeration of particle Zr and the employ of high-temperature dissolution and peritectic reactions to promote the solid solution of Zr.The results demonstrate that the particle Zr within the pretreated Mg–Zr master alloy is effectively dispersed and refined,and greater solute Zr levels can be achieved.The subsequent grain refinement ability was studied on a typical Mg–6Zn–0.6Zr(wt%)alloy.The outcome highlights that an improvement in the grain refinement efficacy(32.4%)of Mg–Zr master alloys was obtained with a holding time of 60 min.The pretreated Mg–Zr master alloy significantly augments the efficiency of grain refinement for Mg alloys through a synergistic strategy involving heterogeneous nucleation and solute-driven growth restriction.The crucial factor in achieving effective grain refinement of Zr in Mg alloys lies in regulating the presence and morphology of Zr in the Mg–Zr master alloy,distinguishing between particle Zr and solute Zr.This study introduces a novel method for developing more efficient Mg–Zr refiners.

改进遗传算法求解含单转运系统车间调度问题

研究单转运系统分布式置换流水线调度问题,任一工厂内连续两台机器间有一台运输能力有限的转运机器人。基于此,提出一种多策略融合改进遗传算法以最小化最大完工时间。引入Logistic-tent混沌搜索、基于K-均值聚类的NEH算法和修正NEH算法以改善初始工厂加工序列群的质量,运用结合均匀多点交叉和互换变异的自适应交叉变异算子或工厂内/间交叉变异算子进行解的调整,设计一种基于主工厂的邻域搜索(key-factory-based local search,KFLS)和半初始化策略进行再次优化。仿真结果表明了该算法的有效性。

基于图的异构数据集成方法研究

企业各部门对数据施行分散管理,烟囱式的系统建设使数据散落在异构数据库中,异构数据给当前数据集成工作带来了系列挑战。为解决企业异构系统数据汇聚融合的问题,提出一种基于图的端到端的数据集成框架。首先,根据关系型数据模型的主外键关系将表和字段的实体关系构建成网络图,将表名和字段名称分别看作图中不同类型的实体。然后,将构建的图输入图神经网络,经过图卷积得到图中各节点的向量表征,基于节点向量可计算任意所需匹配的两个图的节点映射关系。完成图中表和字段的对齐后,再将不同字段值标准化,即将每个单元格的值映射为标准值。最后,将以上结果工程化为数据库可执行的查询语句,从而实现异构数据融合。在企业内部的真实数据上进行验证,实验结果表明,文中所提框架能提高数据集成的开发效率,且该模型不受业务领域限制,具有较强的移植性。