Evolution of nonmetallic inclusions in 80-t 9CrMoCoB large-scale ingots during electroslag remelting process

In combination with theoretical calculations,experiments were conducted to investigate the evolution behavior of nonmetallic inclusions(NMIs)during the manufacture of large-scale heat-resistant steel ingots using 9CrMoCoB heat-resistant steel and CaF_(2)–CaO–Al_(2)O_(3)–SiO_(2)–B_(2)O_(3)electroslag remelting(ESR)-type slag in an 80-t industrial ESR furnace.The main types of NMI in the consumable electrode comprised pure alumina,a multiphase oxide consisting of an Al_(2)O_(3)core and liquid CaO–Al_(2)O_(3)–SiO_(2)–MnO shell,and M_(23)C_(6)carbides with an MnS core.The Al_(2)O_(3)and MnS inclusions had higher precipitation temperatures than the M_(23)C_(6)-type carbide under equilibrium and nonequilibrium solidification processes.Therefore,inclusions can act as nucleation sites for carbide layer precipitation.The ESR process completely removed the liquid CaO–Al_(2)O_(3)–SiO_(2)–MnO oxide and MnS inclusion with a carbide shell,and only the Al_(2)O_(3)inclusions and Al_(2)O_(3)core with a carbide shell occupied the remelted ingot.The M_(23)C_(6)-type carbides in steel were determined as Cr_(23)C_(6)based on the analysis of transmission electron microscopy results.The substitution of Cr with W,Fe,or/and Mo in the Cr_(23)C_(6)lattice caused slight changes in the lattice parameter of the Cr_(23)C_(6)carbide.Therefore,Cr_(21.34)Fe_(1.66)C_(6),(Cr_(19)W_(4)C_(6),Cr_(18.4)Mo_(4.6)C_(6),and Cr_(16)Fe_(5)Mo_(2)C_(6)can match the fraction pattern of Cr_(23)C_(6)carbide.The Al_(2)O_(3)inclusions in the remelted ingot formed due to the reduction of CaO,SiO_(2),and MnO components in the liquid inclusion.The increased Al content in liquid steel or the higher supersaturation degree of Al_(2)O_(3)precipitation in the remelted ingot than that in the electrode can be attributed to the evaporation of CaF_(2)and the increase in CaO content in the ESR-type slag.

Lithium plating-free 1 Ah-level high-voltage lithium-ion pouch battery via ambi-functional pentaerythritol disulfate

Elevating the charge cut-off voltage beyond traditional 4.2 V is a commonly accepted technology to increase the energy density of Li-ion batteries(LIBs) but the risk of Li-dendrites and fire hazard increases as well. The use of ambi-functional additive, which forms stable solid electrolyte interphase(SEI) simultaneously at both cathode and anode, is a key to enabling a dendrites-free and well-working high-voltage LIB. Herein, a novel ambi-functional additive, pentaerythritol disulfate(PEDS), at 1 wt% without any other additive is demonstrated. We show the feasibility and high impacts of PEDS in forming lithium sulfateincorporated robust SEI layers at NCM523 cathode and graphite anode in 1 Ah-level pouch cell under4.4 V, 25 °C and 0.1 C rate, which mitigates the high-voltage instability, metal-dissolution and cracks on NCM523 particles, and prevents Li-dendrites at graphite anode. Improved capacity retention of 83%after 300 cycles is thereby achieved, with respect to 69% with base electrolyte, offering a promising path toward the design of practical high-energy LIBs.

Multi-Agent Deep Reinforcement Learning for Cross-Layer Scheduling in Mobile Ad-Hoc Networks

Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

Graph-Based Feature Learning for Cross-Project Software Defect Prediction

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for CPDP.This paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source code.The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive models.The process involves graph construction,feature learning through graph embedding and LSTM,and defect prediction.Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

3-Multi ranked encryption with enhanced security in cloud computing

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when requested.We refer to a model that satisfies all of the conditions a 3-multi ranked search model.However,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation center.That is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real life.In this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions.The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same keyword.Moreover,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different key.Our evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

Efficient Remote Identification for Drone Swarms

With the advancement of unmanned aerial vehicle(UAV)technology,the market for drones and the cooperation of many drones are expanding.Drone swarms move together in multiple regions to perform their tasks.A Ground Control Server(GCS)located in each region identifies drone swarmmembers to prevent unauthorized drones from trespassing.Studies on drone identification have been actively conducted,but existing studies did not consider multiple drone identification environments.Thus,developing a secure and effective identification mechanism for drone swarms is necessary.We suggested a novel approach for the remote identification of drone swarms.For an efficient identification process between the drone swarm and the GCS,each Reader drone in the region collects the identification information of the drone swarmand submits it to the GCS for verification.The proposed identification protocol reduces the verification time for a drone swarm by utilizing batch verification to verify numerous drones in a drone swarmsimultaneously.To prove the security and correctness of the proposed protocol,we conducted a formal security verification using ProVerif,an automatic cryptographic protocol verifier.We also implemented a non-flying drone swarmprototype usingmultiple Raspberry Pis to evaluate the proposed protocol’s computational overhead and effectiveness.We showed simulation results regarding various drone simulation scenarios.

ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Environmental,social,and governance(ESG)factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value.Recently,non-financial indicators have been considered as important for the actual valuation of corporations,thus analyzing natural language data related to ESG is essential.Several previous studies limited their focus to specific countries or have not used big data.Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG.To address this problem,in this study,the authors used data from two platforms:LexisNexis,a platform that provides media monitoring,and Web of Science,a platform that provides scientific papers.These big data were analyzed by topic modeling.Topic modeling can derive hidden semantic structures within the text.Through this process,it is possible to collect information on public and academic sentiment.The authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic(BERTopic)—a state-of-the-art topic-modeling technique.In addition,changes in subject patterns over time were considered using dynamic topic modeling.As a result,concepts proposed in an international organization such as the United Nations(UN)have been discussed in academia,and the media have formed a variety of agendas.

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.

基于消息代理的OPC UA发布/订阅模式研究与实现

针对目前OPC UA客户端/服务器通信模式中存在的紧耦合、服务器性能瓶颈等问题,进行了OPC UA发布/订阅通信模式总体架构的研究。首先具体分析了UA的核心功能,包括地址空间技术和数据编码技术,并在两者的基础上,利用消息代理机制,完成了发布者和订阅者的功能开发。还通过实验进一步验证了功能的稳定性和对数据的传输能力,结果表明,此模式可以满足大多数工业需求。

Occurrence and distribution of hydroxylated isoprenoid glycerol dialkyl glycerol tetraethers(OH-GDGTs) in the Han River system, South Korea

We investigated the occurrence and distribution of terrestrial-derived hydroxylated isoprenoid glycerol dialkyl glycerol tetraethers(OH-GDGTs) in the Han River system and their potential impact on the application of the ring index of OH-GDGTs(RI-OH) as a sea surface temperature(SST) proxy in the eastern Yellow Sea. Thereby, we analyzed various samples collected along the Han River and from its surrounding areas(South Korea, n = 34). The OHGDGTs were found in all samples investigated. OH-GDGT-0 was the dominant OH-GDGT component in the estuary and marine samples while OH-GDGT-2 was generally dominant in the soils, the lake sediments and the river suspended particulate matter(SPM). Our results thus suggests a possible warm bias of the RI-OH-derived summer SSTs in the coastal zone to which a large amount of terrestrial organic matter is being supplied. Further studies are necessary to better assess the applicability of the RI-OH proxy in the eastern Yellow Sea.

Preparation and characterization of SMA/S-POSS hybrid membranes for direct methanol fuel cell applications

A novel sulfonated polyhedral oligomeric silsesquioxane(S-POSS) monomer was synthesized successfully in this article. S-POSS acted as a donor of sulfonic acid group and reacted with poly(styrene-co-maleic anhydride) (SMA). FT-IR spectroscopy confirmed the modification through introduction of peaks characteristic of ester linkages and carboxylic groups. The SMA/S-POSS hybrid membranes were fabricated from different S-POSS contents. The proton conductivity and methanol permeability of the hybrid membranes were studied with changing S-POSS content from 5wt.% to 30wt.%. It was found that the proton conductivity and the methanol permeability were dependent on the S-POSS content. Both of proton conductivity and methanol permeability properties improve with increasing S-POSS content. The proton conductivities of the hybrid membranes are in the range of 10-3-10-2 S·cm-1, and the range of methanol permeabilities was between 10-8 and 10-7 cm2·s-1. The membranes show good thermal properties characterized by thermogravimetric analysis (TGA).

Solid state synthesis of nano-mineral particles

Many researchers in academia and industries are interested in reducing particle sizes from few submicrometers to nano-meter levels.These nano-particles find application in several areas including ceramics,paints,cosmetics,microelectronics,sensors,textiles and biomedical,etc.This article reviews the present state of the art for solid state synthesis of mineral nano-particles by wet milling,including their operating variables such as ball size,solid mass fraction and suspension stability.This article concludes and recommends with a critical discussion of nano-particles synthesis and a few common strategies to overcome stability issues.

Characterization of plasma electrolytic oxide formed on AZ91 Mg alloy in KMnO_4 electrolyte

The aim of this work is to investigate microstructure,corrosion resistance characteristics and nanohardness of the oxide layer on AZ91 Mg alloy by applying different voltage with KMnO4 contained solution.There are lots of closed pores that are filled with another oxide compound compared with the typical surface morphology with pore coated until 350 V of coating voltage.The thickness of oxide layer increases with increasing coating voltage.The oxide layer formed on AZ91 Mg alloy in electrolyte with potassium permanganate consists of MgO and Mn2O3.Corrosion potential of the oxide layer on AZ91 Mg alloy obtained at different plasma electrolytic oxidation(PEO) reaction stages increases with increasing coating voltage.The corrosion resistance of AZ91 Mg alloy depends on the existence of the manganese oxide in the oxide layer.The inner barrier layer composed of the MgO and Mn2O3 may serve as diffusion barrier to enhance the corrosion resistance and may partially explain the excellent anti-corrosion performance in corrosion test.Nanohardness values increase with increasing coating voltage.The increase in the nanohardness may be due to the effect of manganese oxide in the oxide layer on AZ91 Mg alloy coated from electrolyte containing KMnO4.

Numerical evaluation of new Austrian tunneling method excavation sequences: A case study

The main aspects that require attention in tunnel design in terms of safety and economy are the precise estimation of probable ground conditions and ground behavior during construction. The variation in rock mass behavior due to tunnel excavation sequence plays an important role during the construction stage.The purpose of this research is to numerically evaluate the effect of excavation sequence on the ground behavior for the Lowari tunnel project, Pakistan. For the tunnel stability, the ground behavior observed during the actual partial face excavation sequence is compared with the top heading and bench excavation sequence. For this purpose, the intact rock parameters are used along with the characterization of rock mass joints related parameters to provide input for numerical modelling via FLAC 2D. The in-situ stresses for the numerical modelling are obtained using empirical equations. From the comparison of the two excavation sequences, it was observed that the actual excavation sequence used for Lowari tunnel construction utilized more support than the top heading and bench method. However, the actual excavation sequence provided good results in terms of stability.

Distribution characteristics of inclusions along with the surface sliver defect on the exposed panel of automobile:A quantitative electrolysis method

The specific distribution characteristics of inclusions along with the sliver defect were analyzed in detail to explain the formation mechanism of the sliver defect on the automobile exposed panel surface.A quantitative electrolysis method was used to compare and evaluate the three-dimensional morphology,size,composition,quantity,and distribution of inclusions in the defect and non-defect zone of automobile exposed panel.The Al2O3 inclusions were observed to be aggregated or chain-like shape along with the sliver defect of about 3–10μm.The aggregation sections of the Al2O3 inclusions are distributed discretely along the rolling direction,with a spacing of 3–7 mm,a length of 6–7 mm,and a width of about 3 mm.The inclusion area part is 0.04%–0.16%with an average value of 0.08%,the inclusion number density is 40 mm−2 and the inclusion average spacing is 25.13μm.The inclusion spacing is approximately 40–160μm,with an average value of 68.76μm in chain-like inclusion parts.The average area fraction and number density of inclusions in the non-defect region were reduced to about 0.002%and 1–2 mm^−2,respectively,with the inclusion spacing of 400μm and the size of Al2O3 being 1–3μm.

Automated Facial Expression Recognition and Age Estimation Using Deep Learning

With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is necessary.The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments.The proposed system first takes an input image pre-process it and then detects faces in the entire image.After that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group.The proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups dataset.The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time.The proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.

Thick free-standing electrode based on carbon-carbon nitride microspheres with large mesopores for high-energy-density lithium-sulfur batteries

The development of sulfur cathodes with high areal capacity and high energy density is crucial for the practical application of lithium-sulfur batteries(LSBs).LSBs can be built by employing(ultra)high-loading sulfur cathodes,which have rarely been realized due to massive passivation and shuttling.Herein,microspheres of a carbon-carbon nitride composite(C@CN)with large mesopores are fabricated via molecular cooperative assembly.Using the C@CN-based electrodes,the effects of the large mesopores and N-functional groups on the electrochemical behavior of sulfur in LSB cells are thoroughly investigated under ultrahigh sulfur-loading conditions(>15 mgS cm^(-2)).Furthermore,for high-energy-density LSBs,the C@CN powders are pelletized into a thick free-standing electrode(thickness:500^m;diameter:11 mm)via a simple briquette process;here,the total amount of energy stored by the LSB cells is 39 mWh,corresponding to a volumetric energy density of 440 Wh L-1 with an areal capacity of 24.9 and 17.5 mAh cm^(-2) at 0.47 and 4.7 mA cm^(-2),respectively(at 24mgS cm^(-2)).These results have significantly surpassed most recent records due to the synergy among the large mesopores,(poly)sulfide-philic surfaces,and thick electrodes.The developed strategy with its potential for scale-up successfully fills the gap between laboratory-scale cells and practical cells without sacrificing the high areal capacity and high energy density,providing a solid foundation for the development of practical LSBs.

Effect of Post-Rolling after ECAP on Superplastic Behavior of Commercial Al-Mg Alloy

A commercial Al-Mg alloy was subjected to equal channel angular pressing of 4 passes with and without postrolling, and the effects of post-rolling on the deformation characteristics of the alloy at 723 K were examined. Post-rolling was found to influence the deformation behavior significantly. The deformation behavior of the alloy processed only by equal channel angular pressing was characterized by (a) localized deformation indicated by severe surface prominence and depression, (b) the strain rate sensitivity of 0.33, and (c) moderate high strain rate superplastic elongations. By contrast, that of the alloy processed by equal channel angular pressing and post-rolling (70 % thickness reduction) was manifested by (a) uniform deformation associated with grain boundary sliding throughout the sample, (b) a sigmoidal behavior showing the strain rate sensitivity of 0.45 at the intermediate strain rates in the logarithmic stress-strain rate curve, and (c) very large high strain rate superplastic elongations.