Microstructure and thermal properties of dissimilar M300–CuCr1Zr alloys by multi-material laser-based powder bed fusion

Multi-material laser-based powder bed fusion (PBF-LB) allows manufacturing of parts with 3-dimensional gradient and additional functionality in a single step. This research focuses on the combination of thermally-conductive CuCr1Zr with hard M300 tool steel.Two interface configurations of M300 on CuCr1Zr and CuCr1Zr on M300 were investigated. Ultra-fine grains form at the interface due to the low mutual solubility of Cu and steel. The material mixing zone size is dependent on the configurations and tunable in the range of0.1–0.3 mm by introducing a separate set of parameters for the interface layers. Microcracks and pores mainly occur in the transition zone.Regardless of these defects, the thermal diffusivity of bimetallic parts with 50vol% of CuCr1Zr significantly increases by 70%–150%compared to pure M300. The thermal diffusivity of CuCr1Zr and the hardness of M300 steel can be enhanced simultaneously by applying the aging heat treatment.

Highly Active Interfacial Sites in SFT-SnO_(2) Heterojunction Electrolyte for Enhanced Fuel Cell Performance via Engineered Energy Bands:Envisioned Theoretically and Experimentally

Extending the ionic conductivity is the pre-requisite of electrolytes in fuel cell technology for high-electrochemical performance.In this regard,the introduction of semiconductor-oxide materials and the approach of heterostructure formation by modulating energy bands to enhance ionic conduction acting as an electrolyte in fuel cell-device.Semiconductor(n-type;SnO_(2))plays a key role by introducing into p-type SrFe_(0.2)Ti_(0.8)O_(3-δ)(SFT)semiconductor perovskite materials to construct p-n heterojunction for high ionic conductivity.Therefore,two different composites of SFT and SnO_(2)are constructed by gluing p-and n-type SFT-SnO_(2),where the optimal composition of SFT-SnO_(2)(6∶4)heterostructure electrolyte-based fuel cell achieved excellent ionic conductivity 0.24 S cm^(-1)with power-output of 1004 mW cm^(-2)and high OCV 1.12 V at a low operational temperature of 500℃.The high power-output and significant ionic conductivity with durable operation of 54 h are accredited to SFT-SnO_(2)heterojunction formation including interfacial conduction assisted by a built-in electric field in fuel cell device.Moreover,the fuel conversion efficiency and considerable Faradaic efficiency reveal the compatibility of SFT-SnO_(2)heterostructure electrolyte and ruled-out short-circuiting issue.Further,the first principle calculation provides sufficient information on structure optimization and energy-band structure modulation of SFT-SnO_(2).This strategy will provide new insight into semiconductor-based fuel cell technology to design novel electrolytes.

Stabilized Nickel-Rich-Layered Oxide Electrodes for High-Performance Lithium-Ion Batteries

This work made use of the Aalto University Otanano-Nanomicroscopy Center and RAMI infrastructures.Financial support from Business Finland NextGenBat[grant number 211849]is greatly acknowledged.The tomography experiment was performed at the beamline ID16B of the European Synchrotron Radiation Facility(ESRF),Grenoble,France,in the frame of proposal CH-6644.The patent titled“Stabilized Positive Electrode Material to Enable High Energy and Power Density Lithium-Ion Batteries”(IPD3173)is pertinent to this manuscript.It was filed by Zahra Ahaliabadeh and Tanja Kallio,and the patent rights are held by Aalto University.

High-resolution photogrammetry to measure physical aperture of two separated rock fracture surfaces

Photogrammetry,reconstructing three-dimensional(3D)models from overlapping two-dimensional(2D)photos,finds application in rock mechanics and rock engineering to extract geometrical details of reconstructed objects,for example rock fractures.Fracture properties are important for determining the mechanical stability,permeability,strength,and shear behavior of the rock mass.Photogrammetry can be used to reconstruct detailed 3D models of two separated rock fracture surfaces to characterize fracture roughness and physical aperture,which controls the fluid flow,hydromechanical and shear behavior of the rock mass.This research aimed to determine the optimal number of scale bars required to produce high-precision 3D models of a fracture surface.A workflow has been developed to define the physical aperture of a fracture using photogrammetry.Three blocks of Kuru granite(25 cm×25 cm×10 cm)with an artificially induced fracture,were investigated.For scaling 3D models,321 markers were used as ground control points(GCPs)with predefined distances on each block.When the samples were wellmatched in their original positions,the entire block was photographed.Coordinate data of the GCPs were extracted from the 3D model of the blocks.Each half was surveyed separately and georeferenced by GCPs and merged into the same coordinate system.Two fracture surfaces were extracted from the 3D models and the vertical distance between the two surfaces was digitally calculated as physical aperture.Accuracy assessment of the photogrammetric reconstruction showed a 20-30 mm digital control distance accuracy when compared to known distances defined between markers.To attain this accuracy,the study found that at least 200 scale bars were required.Furthermore,photogrammetry was employed to measure changes in aperture under normal stresses.The results obtained from this approach were found to be in good agreement with those obtained using linear variable displacement transducers(LVDTs),with differences ranging from 1 mm to 8μm.

Generation of input spectrum for electrolysis stack degradation test applied to wind power PEM hydrogen production

Hydrogen production by proton exchange membrane electrolysis has good fluctuation adaptability,making it suitable for hydrogen production by electrolysis in fluctuating power sources such as wind power.However,current research on the durability of proton exchange membrane electrolyzers is insufficient.Studying the typical operating conditions of wind power electrolysis for hydrogen production can provide boundary conditions for performance and degradation tests of electrolysis stacks.In this study,the operating condition spectrum of an electrolysis stack degradation test cycle was proposed.Based on the rate of change of the wind farm output power and the time-averaged peak-valley difference,a fluctuation output power sample set was formed.The characteristic quantities that played an important role in the degradation of the electrolysis stack were selected.Dimensionality reduction of the operating data was performed using principal component analysis.Clustering analysis of the data segments was completed using an improved Gaussian mixture clustering algorithm.Taking the annual output power data of wind farms in Northwest China with a sampling rate of 1 min as an example,the cyclic operating condition spectrum of the proton-exchange membrane electrolysis stack degradation test was constructed.After preliminary simulation analysis,the typical operating condition proposed in this paper effectively reflects the impact of the original curve on the performance degradation of the electrolysis stack.This study provides a method for evaluating the degradation characteristics and system efficiency of an electrolysis stack due to fluctuations in renewable energy.

Empowering neuromorphic computing with topological states

In 1980,scientist Klaus von Klitzing discovered the quantum Hall effect[1],a groundbreaking achievement that earned him the Nobel Prize in Physics in 1985.This discovery was a significant milestone in condensed matter physics,representing the first identification of topological quantum states.

Performance analysis of deep borehole heat exchangers for decarbonization of heating systems

Meeting the climate change mitigation targets will require a substantial shift from fossil to clean fuels in the heating sector.Heat pumps with deep borehole exchangers are a promising solution to reduce emissions.Here the thermal behavior of deep borehole exchangers(DBHEs)ranging from 1 to 2 km was analyzed for various heat flow profiles.A strong correlation between thermal energy extraction and power output from DBHEs was found,also influenced by the heating profile employed.Longer operating time over the year typically resulted in higher energy production,while shorter one yielded higher average thermal power output,highlighting the importance of the choice of heating strategy and system design for optimal performance of DBHEs.Short breaks in operation for regenerating the borehole,for example,with waste heat,proved to be favorable for the performance yielding an overall heat output close to the same as with continuous extraction of heat.The results demonstrate the usefulness of deep boreholes for dense urban areas with less available space.As the heat production from a single DBHE in Finnish conditions ranges from half up to even a few GWh a year,the technology is best suitable for larger heat loads.

A survey on blockchain-enabled federated learning and its prospects with digital twin

Digital Twin(DT)supports real time analysis and provides a reliable simulation platform in the Internet of Things(IoT).The creation and application of DT hinges on amounts of data,which poses pressure on the application of Artificial Intelligence(AI)for DT descriptions and intelligent decision-making.Federated Learning(FL)is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model training.Therefore,DT can benefit by combining with FL,successfully solving the"data island"problem in traditional AI.However,FL still faces serious challenges,such as enduring single-point failures,suffering from poison attacks,lacking effective incentive mechanisms.Before the successful deployment of DT,we should tackle the issues caused by FL.Researchers from industry and academia have recognized the potential of introducing Blockchain Technology(BT)into FL to overcome the challenges faced by FL,where BT acting as a distributed and immutable ledger,can store data in a secure,traceable,and trusted manner.However,to the best of our knowledge,a comprehensive literature review on this topic is still missing.In this paper,we review existing works about blockchain-enabled FL and visualize their prospects with DT.To this end,we first propose evaluation requirements with respect to security,faulttolerance,fairness,efficiency,cost-saving,profitability,and support for heterogeneity.Then,we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation requirements.Finally,we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL,based on which we further propose some directions for future research.

Joint alignment and steering to manage interference

In wireless communication networks,mobile users in overlapping areas may experience severe interference,therefore,designing effective Interference Management(IM)methods is crucial to improving network performance.However,when managing multiple disturbances from the same source,it may not be feasible to use existing IM methods such as Interference Alignment(IA)and Interference Steering(IS)exclusively.It is because with IA,the aligned interference becomes indistinguishable at its desired Receiver(Rx)under the cost constraint of Degrees-of-Freedom(DoF),while with IS,more transmit power will be consumed in the direct and repeated application of IS to each interference.To remedy these deficiencies,Interference Alignment Steering(IAS)is proposed by incorporating IA and IS and exploiting their advantages in IM.With IAS,the interfering Transmitter(Tx)first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx,and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference.Moreover,two improved versions of IAS,i.e.,IAS with Full Adjustment at the Interfering Tx(IAS-FAIT)and Interference Steering and Alignment(ISA),are presented.The former considers the influence of IA on the interfering user-pair's performance.The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components,thus ensuring the Spectral Efficiency(SE)of the interfering communication pairs.Under ISA,the power cost for IS at the interfered Tx is minimized,hence improving SE performance of the interfered communication-pairs.Since the proposed methods are realized at the interfering and interfered Txs cooperatively,the expenses of IM are shared by both communication-pairs.Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.

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.

Mechanical stimulation devices formechanobiology studies:a market,literature,and patents review

Significant advancements in various research and technological fields have contributed to remarkable findings on the physiological dynamics of the human body.Tomore closely mimic the complex physiological environment,research has moved from two-dimensional(2D)culture systems to more sophisticated three-dimensional(3D)dynamic cultures.Unlike bioreactors or microfluidic-based culture models,cells are typically seeded on polymeric substrates or incorporated into 3D constructs which are mechanically stimulated to investigate cell response to mechanical stresses,such as tensile or compressive.This review focuses on the working principles of mechanical stimulation devices currently available on the market or custom-built by research groups or protected by patents and highlights the main features still open to improvement.These are the features which could be focused on to perform,in the future,more reliable and accurate mechanobiology studies.

Photon-counting computed tomography thermometry via material decomposition and machine learning

Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.

Image Steganalysis Based on Deep Content Features Clustering

The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis.The existing methods try to reduce this effect by discarding some features related to image contents.Inevitably,this should lose much helpful information and cause low detection accuracy.This paper proposes an image steganalysis method based on deep content features clustering to solve this problem.Firstly,the wavelet transform is used to remove the high-frequency noise of the image,and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.Then,the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification.Finally,the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis.We experimented on publicly available combined datasets of Bossbase1.01,Bows2,and ALASKA#2 with a quality factor of 75.The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84%for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion(J-UNIWARD)at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient(bpnzAC).Furthermore,at the payload of 0.2 bpnzAC,the improvement effect is minimal but also reaches 1.39%.Compared with the previous steganalysis based on deep learning,this method considers the differences between the training contents.It selects the proper detector for the image to be detected.Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images.The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis.

Joint optimization for secure ambient backscatter communication in NOMA-enabled IoT networks

Non-Orthogonal Multiple Access(NOMA)has emerged as a novel air interface technology for massive connectivity in Sixth-Generation(6G)era.The recent integration of NOMA in Backscatter Communication(BC)has triggered significant research interest due to its applications in low-powered Internet of Things(IoT)networks.However,the link security aspect of these networks has not been well investigated.This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system.Our system model takes into account the simultaneous operation of NOMA IoT users and the Backscatter Node(BN)in the presence of multiple EavesDroppers(EDs).The EDs in the surrounding area can overhear the communication of Base Station(BS)and BN due to the wireless broadcast transmission.Thus,the chief aim is to enhance link security by optimizing the BN reflection coefficient and BS transmit power.To gauge the performance of the proposed scheme,we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes.Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without the BC and conventional orthogonal multiple access schemes in terms of system secrecy rate.

SCIM: Incorporating secure communication and interference management in one operation

Due to the broadcast nature of wireless communications,users’data transmitted wirelessly is susceptible to security/privacy threats.Meanwhile,as a result of the limitation of spectrum resources,massive wireless connections will incur serious interference,which may damage the efficiency of data transmission.Therefore,improving both efficiency and secrecy of data transmission is of research significance.In this paper,we propose a wireless transmission scheme by taking both Secure Communication(SC)and Interference Management(IM)into account,namely SCIM.With this scheme,an SCIM signal is generated by the legitimate transmitter(Tx)and sent along with the desired signal,so that the SCIM signal can interact with and suppress the environmental interference at the legitimate receiver(Rx).Meanwhile,the SCIM signal may interfere with the eavesdropper in the coverage of legitimate transmission so as to deteriorate the eavesdropping performance.Therefore,the secrecy of desired transmission is improved.In this way,both the transmission efficiency and privacy are enhanced.Then,by taking various transmission preferences into account,we develop different implementations of SCIM,including Interference Suppression First SCIM(ISF-SCIM),Data Transmission First SCIM(DTF-SCIM),Anti-Eavesdropping First SCIM(AEF-SCIM),and Secrecy Rate Maximization SCIM(SRM-SCIM).Our in-depth simulation results have shown the proposed methods to effectively improve the efficiency and secrecy of the legitimate transmission.

Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years now.Confidential information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the IIoT.There are also significant security risks,concerns,and difficulties associated with cloud computing.To address these challenges,we propose merging BC and SDN into a cloud computing platform for the IIoT.This paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT applications.The proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and scalable.Customers in the industrial sector benefit from the dispersed or decentralized,and efficient environment of BC.Additionally,we described an SDN method to improve the durability,stability,and load balancing of cloud infrastructure.The efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.

LNG空温式气化器换热机理及结霜工况下的换热计算

ILNG空温式气化器在运行时时常伴有结霜现象,霜层恶化了传热,影响了气化器的性能。探寻霜层在翅片管表面随时间变化的规律及对气化器换热产生的影响,对LNG空温式气化器的设计和安全稳定运行具有重要意义。为此,建立了霜层内部能量和质量守恒方程,并引入霜层物性参数经验公式,得到了封闭的方程组,进而计算出了冷表面结霜后的霜层热阻随时间的变化规律。然后再考虑霜层热阻的影响,计算LNG空温式气化器内外传热传质的相互耦合规律随时间变化的动态过程,模拟结霜工况下LNG空温式气化器的运行情况,获得了不同时刻翅片管内外物性参数沿管长的分布规律。结果表明:①霜层在翅片管表面的覆盖面积可以达到80%,霜层对LNG空温式气化器换热的影响不可忽略;②除了局部由于霜层的肋片作用使换热增强,绝大多数情况下霜层会使翅片管的换热效率大幅降低,最大可降低85%,结霜后的总传热系数为80~220 W/(m^2·K);③结霜后,如果环境适宜,气化器会在很短的时间内达到另一个稳态稳定运行。

光学显微镜下微小物体的三维重构

本文提出了基于数字图像处理和精密机械动作大于光学显微镜观察视野(0.647mm×0.483mm)的微小物体的三维重构方法。该方法首先运用光学聚焦原理采集不同焦平面上的图像组序列,采用图像拼接算法,拼接出研究对象的整体图像。然后采用图像块分割算法和灰度差分算子对图像序列进行分析。最后结合聚焦合成的叠合算法快速地实现了微小物体的三维重构。实验表明,该方法简单有效,所生成的三维重构效果图提供了微小物体的更加丰富的信息。

中国品牌形象设计的国际化表达研究

对中国品牌形象的国际化设计进行研究。用视觉人种志的研究方法,通过笔者在欧洲市场对中国品牌的形象设计现状进行研究,得出经济全球化、信息全球化的背景下,中国品牌进入全球市场,要实现品牌价值的提升,从而达到"Made in china"到"Design in china"的目的,在这个过程中,视觉传达设计到了很重要的作用。有效提升中国品牌形象设计的方法是对品牌名称进行翻译和对品牌形象进行Redesign。在当代视觉审美语境下,品牌形象的视觉传达设计应将中国审美与西方审美相融合,最终实现品牌形象的国际化设计。