Fe含量对粉末冶金Cu-Fe合金显微组织与性能的影响

研究Fe含量对粉末冶金Cu-Fe合金显微组织及性能的影响。采用电火花等离子烧结、冷轧以及时效处理工艺制备4种不同铁含量(5%、10%、20%和40%,质量分数)的Cu-Fe合金。研究结果表明,随着Fe含量从5%(质量分数)增加到40%(质量分数),Fe相由离散球形分布演变为连续交错分布,Fe相尺寸从0.29μm增加到1.20μm;时效态的Cu-Fe合金的屈服强度从411.5 MPa提高到788.8 MPa,电导率从62.5%(IACS)降低到42.0%(IACS)。在以上结果的基础上,提出一种混合法则计算Cu基体、初级Fe相和次级Fe相对屈服强度的贡献,可较好地预测Fe含量高于10%(质量分数)合金的力学性能。

Origin of nucleation and growth of extension twins in grains unsuitably oriented for twinning during deformation of Mg-1%Al

A large number of anomalous extension twins,with low or even negative twinning Schmid factors,were found to nucleate and grow in a strongly textured Mg-1Al alloy during tensile deformation along the extruded direction.The deformation mechanisms responsible for this behaviour were investigated through in-situ electron back-scattered diffraction,grain reference orientation deviation,and slip trace-modified lattice rotation.It was found that anomalous extension twins nucleated mainly at the onset of plastic deformation at or near grain boundary triple junctions.They were associated with the severe strain incompatibility between neighbour grains as a result from the differentbasal slip-induced lattice rotations.Moreover,the anomalous twins were able to grow with the applied strain due to the continuous activation ofbasal slip in different neighbour grains,which enhanced the strain incompatibility.These results reveal the complexity of the deformation mechanisms in Mg alloys at the local level when deformed along hard orientations.

Radioisotope production using lasers:From basic science to applications

The discovery of chirped pulse amplification has led to great improvements in laser technology,enabling energetic laser beams to be compressed to pulse durations of tens of femtoseconds and focused to a few micrometers.Protons with energies of tens of MeV can be accelerated using,for instance,target normal sheath acceleration and focused on secondary targets.Under such conditions,nuclear reactions can occur,with the production of radioisotopes suitable for medical application.The use of high-repetition lasers to produce such isotopes is competitive with conventional methods mostly based on accelerators.In this paper,we study the production of^(67)Cu,^(63)Zn,^(18)F,and^(11)C,which are currently used in positron emission tomography and other applications.At the same time,we study the reactions^(10)B(p,α)^(7)Be and^(70)Zn(p,4n)^(67)Ga to put further constraints on the proton distributions at different angles,as well as the reaction^(11)B(p,α)^(8)Be relevant for energy production.The experiment was performed at the 1 PW laser facility at VegaⅢin Salamanca,Spain.Angular distributions of radioisotopes in the forward(with respect to the laser direction)and backward directions were measured using a high purity germanium detector.Our results are in reasonable agreement with numerical estimates obtained following the approach of Kimura and Bonasera[Nucl.Instrum.Methods Phys.Res.,Sect.A 637,164–170(2011)].

How Software Engineering Transforms Organizations: An Open and Qualitative Study on the Organizational Objectives and Motivations in Agile Transformations

Agile Transformations are challenging processes for organizations that look to extend the benefits of Agile philosophy and methods beyond software engineering.Despite the impact of these transformations on orga-nizations,they have not been extensively studied in academia.We conducted a study grounded in workshops and interviews with 99 participants from 30 organizations,including organizations undergoing transformations(“final organizations”)and companies supporting these processes(“consultants”).The study aims to understand the motivations,objectives,and factors driving and challenging these transformations.Over 700 responses were collected to the question and categorized into 32 objectives.The findings show that organizations primarily aim to achieve customer centricity and adaptability,both with 8%of the mentions.Other primary important objectives,with above 4%of mentions,include alignment of goals,lean delivery,sustainable processes,and a flatter,more team-based organizational structure.We also detect discrepancies in perspectives between the objectives identified by the two kinds of organizations and the existing agile literature and models.This misalignment highlights the need for practitioners to understand with the practical realities the organizations face.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

Effect of slake durability on time dependent swelling behavior of red-bed siltstone in Sichuan Basin

The swelling behavior of red-bed rocks is a significant factor in the abnormal uplift of subgrades for high-speed railways constructed on the red stratum in the Sichuan Basin,China.The prime objective of this paper is to investigate the impact of mineralogical composition,moisture content,and overburden load on the time-dependent unconfined and oedometric swelling behavior of red-bed siltstone in the context of differences in the slake durability.Twenty samples were prepared for the swelling test,with eleven used for the unconfined swelling and slake index tests and nine for the oedometric swelling test.The temporal dependency of swelling is characterized by the viscosity coefficient of water absorption in a proposed swelling model.Results indicate that the swelling deformation of red-bed rocks is due to a combination of hydration swelling within the rock matrix and crack expansion caused by air breakage.In the unconfined swelling test,the final axial swelling strain of red-bed rocks decreases linearly with increasing slake index,while the viscosity coefficient increases exponentially with the slake index.In the oedometric swelling test,red-bed rocks with lower slake durability show greater sensitivity to lateral constraint and overburden load compared to those with higher slake durability.

A symmetric substructuring method for analyzing the natural frequencies of conical origami structures

Conical origami structures are characterized by their substantial out-of-plane stiffness and energy-absorptioncapacity.Previous investigations have commonly focused on the static characteristics of these lightweight struc-tures.However,the efficient analysis of the natural vibrations of these structures is pivotal for designing conicalorigami structures with programmable stiffness and mass.In this paper,we propose a novel method to analyzethe natural vibrations of such structures by combining a symmetric substructuring method(SSM)and a gener-alized eigenvalue analysis.SSM exploits the inherent symmetry of the structure to decompose it into a finiteset of repetitive substructures.In doing so,we reduce the dimensions of matrices and improve computationalefficiency by adopting the stiffness and mass matrices of the substructures in the generalized eigenvalue analysis.Finite element simulations of pin-jointed models are used to validate the computational results of the proposedapproach.Moreover,the parametric analysis of the structures demonstrates the influences of the number of seg-ments along the circumference and the radius of the cone on the structural mass and natural frequencies of thestructures.Furthermore,we present a comparison between six-fold and four-fold conical origami structures anddiscuss the influence of various geometric parameters on their natural frequencies.This study provides a strategyfor efficiently analyzing the natural vibration of symmetric origami structures and has the potential to contributeto the efficient design and customization of origami metastructures with programmable stiffness.

Resistive field generation in intense proton beam interaction with solid targets

The Brown-Preston-Singleton(BPS)stopping power model is added to our previously developed hybrid code to model ion beam-plasma interaction.Hybrid simulations show that both resistive field and ion scattering effects are important for proton beam transport in a solid target,in which they compete with each other.When the target is not completely ionized,the self-generated resistive field effect dominates over the ion scattering effect.However,when the target is completely ionized,this situation is reversed.Moreover,it is found that Ohmic heating is important for higher current densities and materials with high resistivity.The energy fraction deposited as Ohmic heating can be as high as 20%-30%.Typical ion divergences with half-angles of about 5°-10°will modify the proton energy deposition substantially and should be taken into account.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.

Analysis of the Supply Chain in Industrial Companies in Guayaquil,Ecuador-A Secondary Publication

This research clearly outlines the supply chain in a process of evolution and digital transformation,where the collaboration of its members is concentrated,having technological tools to help chain management.To analyze the advantages that this transformation produces in the supply chain,as well as its different processes,this study has a systemic perspective that involves precise elements for organizational development,such as the different phases of each process,operations,logistics,and distribution.It must be borne in mind that any strategy in industrial companies grants the automation of procedures in the supply chain and determines a product in any phase of production,making the organization more sensitive to any variation in orders.The methodology included a bibliographic and non-experimental review that allows a descriptive and analytical study,which details the various characteristics of the fact that is being investigated,collecting information through interviews with different people who are involved with industrial companies.Among the results obtained,it was identified that digital transformation helps reduce costs and generates greater profitability.In conclusion,it was obtained that the digital supply chain helps in each of the phases of the processes,these are supervised by devices that help to have fast and effective information.

形变导致的蜘蛛大壶状腺丝力学行为的记忆与变异

纤维材料的形变与力学行为之间的关系密切。在自然条件下蛛丝会被反复拉伸,但其被拉伸后的力学行为被搁置的时间与形变效应鲜见报道。为此,本工作采用METS电子万能试验机测试研究了反复定伸长拉伸松弛后的间隔时间与形变对蜘蛛大壶状腺丝的力学行为的影响。结果表明,不论天然的还是经水最大限度超收缩干燥后的大壶状腺丝被定伸长反复拉伸过屈服点和屈服区甚至拉至加强区的力学行为曲线都能很好地重叠;经长时间(≥20 min)间隔后也只需一次拉伸就能不受拉伸历史的影响重现之前的力学行为,具有良好的纵向拉伸的形状与力学行为记忆。通过增加应变值对蜘蛛大壶状腺丝进行一系列的加载-卸载循环试验,并分析这些循环中其应力-应变曲线,计算每个循环中它的弹性模量、屈服应力、吸收和耗散的能量,以评估这些力学性能参数随循环增加的微观进化与演变。研究表明,蜘蛛丝力学行为的记忆与变异可以通过不可逆和可逆两种变形微观机制及其组合来解释,其中材料的粘弹性起主导作用。这些研究对人们进行新型功能纤维材料的仿生设计具有重要的指导意义。

运动表现分析:过去、现在与未来

对运动表现分析的发展历程、现状、未来进行研究发现,运动表现分析起源于标记分析,历经一个多世纪的实践与研究完成了由方法向方法论的转型,进入21世纪后逐渐发展成为运动科学(Sports Science)的一个新兴分支学科。在运动表现分析实践中采用定性、定量以及两者相结合的方法对“人的运动”进行直接描述和分析,分析手段主要包括标记分析和移动分析,通常需要从方法和学科2个层面来理解其定义。技术和战术表现评估、信效度分析、负荷监控以及学科交叉研究是近年来运动表现分析领域的研究热点。运动表现分析相关专业的设立以及运动表现分析师这一新职业的出现有力地推动了运动训练的科学化。当前,运动表现分析的理论基础、技术手段、人才培养和学科影响力等方面还面临挑战,未来其理论体系将趋向完善,向分析智能化、服务对象大众化和人才培养专业化等方向发展。

Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.

MCRO-PUF:A Novel Modified Crossover RO-PUF with an Ultra-Expanded CRP Space

With the expanding use of the Internet of Things(IoT)devices and the connection of humans and devices to the Internet,the need to provide security in this field is constantly growing.The conventional cryptographic solutions need the IoT device to store secret keys in its non-volatile memory(NVM)leading the system to be vulnerable to physical attacks.In addition,they are not appropriate for IoT applications due to their complex calculations.Thus,physically unclonable functions(PUFs)have been introduced to simultaneously address these issues.PUFs are lightweight and easy-toaccess hardware security primitives which employ the unique characteristics of integrated circuits(ICs)to generate secret keys.Among all proposed PUFs,ring oscillator PUF(RO-PUF)has had amore suitable structure for hardware implementation because of its high reliability and easier providing of circuital symmetry.However,RO-PUF has not been so attractive for authentication purposes due to its limited supported challenge-response pairs(CRPs).A few efforts have been made in recent years that could successfully improve the RO-PUF CRP space,such as configurable RO-PUF(CRO-PUF).In this paper,by considerably improving the CRO-PUF structure and adding spare paths,we propose a novel strong RO-PUF structure that exponentially grows the CRP space and dramatically reduces the hardware cost.We implement our design on a simple and low-cost FPGA chip named XC6SLX9-2tqg144,stating that the proposed design can be used in IoT applications.In addition,to improve the CRP space,our design creates a suitable improvement in different security/performance terms of the generated responses,and dramatically outperforms the state-of-the-art.The average reliability,uniqueness,and uniformity of the responses generated are 99.55%,48.49%,and 50.99%,respectively.

Fagus sylvatica and Quercus pyrenaica:Two neighbors with few things in common

Background:The Iberian Peninsula comprises one of the largest boundaries between Mediterranean and Eurosiberian vegetation,known as sub-Mediterranean zone.This ecotone hosts many unique plant species and communities and constitutes the low-latitude(warm)margin of numerous central European species which co-occur with Mediterranean vegetation.Two of the main species found in this region are the Eurosiberian European beech(Fagus sylvatica L.)and the Mediterranean Pyrenean oak(Quercus pyrenaica Willd.).It remains unclear how the different physiological and adaptive strategies of these two species reflect their niche partitioning within a subMediterranean community and to what extent phenotypic variation(intraspecific variability)is driving niche partitioning across Eurosiberian and Mediterranean species.Methods:We quantified functional niche partitioning,based on the n-dimensional hypervolume to nine traits related to resource acquisition strategies(leaf,stem and root)plus relative growth rate as an additional wholeplant trait,and the environmental niche similarity between Pyrenean oak and European beech.Further,we analyzed the degree of phenotypic variation of both target species and its relationship with relative growth rates(RGR)and environmental conditions.Plant recruitment was measured for both target species as a proxy for the average fitness.Results:Species’functional space was highly segregated(13.09%overlap),mainly due to differences in niche breadth(59.7%)rather than niche replacement(25.6%),and beech showed higher trait variability,i.e.,had larger functional space.However,both species shared the environmental space,i.e.,environmental niches were overlapped.Most plant traits were not related to abiotic variables or RGR,neither did RGR to plant traits.Conclusions:Both target species share similar environmental space,however,show notably different functional resource-use strategies,promoting a high complementarity that contributes to maintaining a high functionality in sub-Mediterranean ecosystems.Therefore,we propose that conservation efforts be oriented to preserve both species in these habitats to maximize ecosystem functionality and resilience.

Application of the extended Fourier amplitude sensitivity testing(FAST)method to inflated,axial stretched,and residually stressed cylinders

This paper is dedicated to applying the Fourier amplitude sensitivity test(FAST)method to the problem of mixed extension and inflation of a circular cylindrical tube in the presence of residual stresses.The metafunctions and the Ishigami function are considered in the sensitivity analysis(SA).The effects of the input variables on the output variables are investigated,and the most important parameters of the system under the applied pressure and axial force such as the axial stretch and the azimuthal stretch are determined.

The influence of CEO’s financial literacy on SMEs technological innovation:the mediating effects of MCS and risk‑taking

Previous literature showed mixed results about the impact of CEOs’financial literacy(CFL)on small and medium-sized enterprises’(SMEs)innovation.This relationship can be motivated by relevant variables,which are missing in the previous literature and make a difference as mediators.In this sense,based on the theoretical framework related to upper echelon theory and resource-based view,this study focuses on the mediating effect of risk-taking attitude and management control systems(MCS)varia-bles.Empirical data from 310 SMEs gathered using a qualitative research questionnaire are analyzed using structural equation modeling methodology.Specifically,estimations are carried out considering the partial least square method.Findings show that MCS and managers’risk attitudes fully mediate the relationship between financial literacy(FL)and innovation.Between these two mediating variables,the implementation of MCS stands out because it also enables the mediating effect of CEOs’risk-taking in the CFL–technological innovation relationship.As the results do not support the significant direct relationship between FL and risk attitude,they confirm an indirect effect through MCS.Furthermore,based on the study findings,SMEs’directors and owners,business associations,and public authorities can improve SMEs’technological innovation by implementing training programs and policies to foster CFL.They can also acknowledge the interdependency between organizational factors and individual characteristics to enhance SMEs’technological innovation.

追加反馈在运动技能中的学习机制与实践方向

目的:探讨追加反馈在运动技能学习过程中的学习机制,为未来实践活动提供更深入、科学的指导,以促进运动技能的进一步发展和提高。方法:检索数据库包括中国知网,Web of Science,PubMed等数据库,检索年限均从建库至2023年6月1日,收集追加反馈在不同运动技能中应用的相关实验文献,提取文献内容,对追加反馈在运动技能中的学习机制、在未来的实践路径等方面进行综述。结果:纳入18篇文献。研究主要集中在构建追加反馈的训练理论和研究框架,以验证不同追加反馈在不同运动技能项目的学习效果,以及评估训练方案的有效性。结论:追加反馈的学习机制是一种闭环反馈系统,通过将学习者表现与目标进行比较和分析,反馈信息为学习者提供正确和及时的指导,使学习者能够逐步接近目标并达到最佳运动表现。并且根据目前的研究现状,提出了追加反馈在未来的实践方向。