Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

Rheological properties and concentration evolution of thickened tailings under the coupling effect of compression and shear

Cemented paste backfill(CPB)is a key technology for green mining in metal mines,in which tailings thickening comprises the primary link of CPB technology.However,difficult flocculation and substandard concentrations of thickened tailings often occur.The rheological properties and concentration evolution in the thickened tailings remain unclear.Moreover,traditional indoor thickening experiments have yet to quantitatively characterize their rheological properties.An experiment of flocculation condition optimization based on the Box-Behnken design(BBD)was performed in the study,and the two response values were investigated:concentration and the mean weighted chord length(MWCL)of flocs.Thus,optimal flocculation conditions were obtained.In addition,the rheological properties and concentration evolution of different flocculant dosages and ultrafine tailing contents under shear,compression,and compression-shear coupling experimental conditions were tested and compared.The results show that the shear yield stress under compression and compression-shear coupling increases with the growth of compressive yield stress,while the shear yield stress increases slightly under shear.The order of shear yield stress from low to high under different thickening conditions is shear,compression,and compression-shear coupling.Under compression and compression-shear coupling,the concentration first rapidly increases with the growth of compressive yield stress and then slowly increases,while concentration increases slightly under shear.The order of concentration from low to high under different thickening conditions is shear,compression,and compression-shear coupling.Finally,the evolution mechanism of the flocs and drainage channels during the thickening of the thickened tailings under different experimental conditions was revealed.

Semi-supervised surface defect detection of wind turbine blades with YOLOv4

Timely inspection of defects on the surfaces of wind turbine blades can effectively prevent unpredictable accidents.To this end,this study proposes a semi-supervised object-detection network based on You Only Looking Once version 4(YOLOv4).A semi-supervised structure comprising a generative adversarial network(GAN)was designed to overcome the difficulty in obtaining sufficient samples and sample labeling.In a GAN,the generator is realized by an encoder-decoder network,where the backbone of the encoder is YOLOv4 and the decoder comprises inverse convolutional layers.Partial features from the generator are passed to the defect detection network.Deploying several unlabeled images can significantly improve the generalization and recognition capabilities of defect-detection models.The small-scale object detection capacity of the network can be improved by enhancing essential features in the feature map by adding the concurrent spatial and channel squeeze and excitation(scSE)attention module to the three parts of the YOLOv4 network.A balancing improvement was made to the loss function of YOLOv4 to overcome the imbalance problem of the defective species.The results for both the single-and multi-category defect datasets show that the improved model can make good use of the features of the unlabeled images.The accuracy of wind turbine blade defect detection also has a significant advantage over classical object detection algorithms,including faster R-CNN and DETR.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

Defect engineering in transition-metal(Fe,Co,andNi)-based electrocatalysts for water splitting

Electrocatalytic water splitting seems to be an efficient strategy to deal with increasingly serious environmental problems and energy crises but still suffers from the lack of stable and efficient electrocatalysts.Designing practical electrocatalysts by introducing defect engineering,such as hybrid structure,surface vacancies,functional modification,and structural distortions,is proven to be a dependable solution for fabricating electrocatalysts with high catalytic activities,robust stability,and good practicability.This review is an overview of some relevant reports about the effects of defect engineering on the electrocatalytic water splitting performance of electrocatalysts.In detail,the types of defects,the preparation and characterization methods,and catalytic performances of electrocatalysts are presented,emphasizing the effects of the introduced defects on the electronic structures of electrocatalysts and the optimization of the intermediates'adsorption energy throughout the review.Finally,the existing challenges and personal perspectives of possible strategies for enhancing the catalytic performances of electrocatalysts are proposed.An in-depth understanding of the effects of defect engineering on the catalytic performance of electrocatalysts will light the way to design high-efficiency electrocatalysts for water splitting and other possible applications.

Developing an atmospheric aging evaluation model of acrylic coatings:A semi-supervised machine learning algorithm

To study the atmospheric aging of acrylic coatings,a two-year aging exposure experiment was conducted in 13 representative climatic environments in China.An atmospheric aging evaluation model of acrylic coatings was developed based on aging data including11 environmental factors from 567 cities.A hybrid method of random forest and Spearman correlation analysis was used to reduce the redundancy and multicollinearity of the data set by dimensionality reduction.A semi-supervised collaborative trained regression model was developed with the environmental factors as input and the low-frequency impedance modulus values of the electrochemical impedance spectra of acrylic coatings in 3.5wt%NaCl solution as output.The model improves accuracy compared to supervised learning algorithms model(support vector machines model).The model provides a new method for the rapid evaluation of the aging performance of acrylic coatings,and may also serve as a reference to evaluate the aging performance of other organic coatings.

Edge effect during microwave plasma chemical vapor deposition diamond-film:Multiphysics simulation and experimental verification

This study focused on the investigation of the edge effect of diamond films deposited by microwave plasma chemical vapor de-position.Substrate bulge height△h is a factor that affects the edge effect,and it was used to simulate plasma and guide the diamond-film deposition experiments.Finite-element software COMSOL Multiphysics was used to construct a multiphysics(electromagnetic,plasma,and fluid heat transfer fields)coupling model based on electron collision reaction.Raman spectroscopy and scanning electron microscopy were performed to characterize the experimental growth and validate the model.The simulation results reflected the experimental trends observed.Plasma discharge at the edge of the substrate accelerated due to the increase in△h(△h=0-3 mm),and the values of electron density(n_(c)),molar concentration of H(C_(H)),and molar concentration of CH_(3)(C_(CH_(3)))doubled at the edge(for the special concave sample with△h=−1 mm,the active chemical groups exhibited a decreased molar concentration at the edge of the substrate).At=0-3 mm,a high diamond growth rate and a large diamond grain size were observed at the edge of the substrate,and their values increased with.The uniformity of film thickness decreased with.The Raman spectra of all samples revealed the first-order characteristic peak of dia-mond near 1332 cm^(−1).When△h=−1 mm,tensile stress occurred in all regions of the film.When△h=1-3 mm,all areas in the film ex-hibited compressive stress.

Microbiologically influenced corrosion resistance enhancement of coppercontaining high entropy alloy Fe_(x)Cu_(1−x)CoNiCrMn against Pseudomonas aeruginosa

To enhance the microbiologically influenced corrosion(MIC)resistance of FeCoNiCrMn high entropy alloy(HEAs),a series of Fe_(x)Cu_((1−x))CoNiCrMn(x=1,0.75,0.5,and 0.25)HEAs were prepared.Microstructural characteristics,corrosion behavior(morphology observation and electrochemical properties),and antimicrobial performance of Fe_(x)Cu_((1−x))CoNiCrMn HEAs were evaluated in a medium inoculated with typical corrosive microorganism Pseudomonas aeruginosa.The aim was to identify copper-containing FeCoNiCrMn HEAs that balance corrosion resistance and antimicrobial properties.Results revealed that all Fe_(x)Cu_((1−x))CoNiCrMn(x=1,0.75,0.5,and 0.25)HEAs exhibited an FCC(face centered cubic)phase,with significant grain refinement observed in Fe_(0.75)Cu_(0.25)CoNiCrMn HEA.Electrochemical tests indicated that Fe_(0.75)Cu_(0.25)CoNiCrMn HEA demonstrated lower corrosion current density(i_(corr))and pitting potential(E_(pit))compared to other Fe_(x)Cu_((1−x))CoNiCrMn HEAs in P.aeruginosa-inoculated medium,exhibiting superior resistance to MIC.Anti-microbial tests showed that after 14 d of immersion,Fe_(0.75)Cu_(0.25)CoNiCrMn achieved an antibacterial rate of 89.5%,effectively inhibiting the adhesion and biofilm formation of P.aeruginosa,thereby achieving resistance to MIC.

Rheological properties of a multiscale granular system during mixing of cemented paste backfill:A review

The technology of cemented paste backfill(CPB)is an effective method for green mining.In CPB,mixing is a vital process aiming to prepare a paste that meets the non-stratification,non-segregation,and non-bleeding requirements.As a multiscale granular system,homogenization is one of the challenges in the paste-mixing process.Due to the high shearing,high concentration,and multiscale characteristics,paste exhibits complex rheological properties in the mixing process.An overview of the mesomechanics and structural evolution is presented in this review.The effects of various influencing factors on the paste's rheological properties were investigated,and the rheological models of the paste were outlined from the macroscopic and mesoscopic levels.The results show that the mechanical effects and structural evolution are the fundamental factors affecting the rheological properties of the paste.Existing problems and future development trends are presented to change the practice where the CPB process comes first and the theory lags.

Effect of samarium on the N_(2) selectivity of Sm_(x)Mn_(0.3-x)Ti catalysts during selective catalytic reduction of NO_(x) with NH_(3)

This work aims to study the improvement effect of Sm on Mn-based catalysts for selective catalytic reduction (SCR) of NO with NH3.A series of Sm_(x)Mn_(0.3-x)-xTi catalysts (x=0,0.1,0.15,0.2,and 0.3) were prepared by co-precipitation.Activity tests indicated that the Sm_(0.15)Mn_(0.15)Ti catalyst showed superior performances,with a NO conversion of 100%and N_(2)selectivity above 87%at 180–300℃.The characterizations showed that Sm doping suppressed the crystallization of TiO_(2)and Mn2O3phases and increased the specific surface area and acidity.In particular,the surface area increased from 152.2 m^(2)·g^(-1)for Mn0.3Ti to 241.7 m^(2)·g^(-1)for Sm_(0.15)Mn_(0.15)Ti.These effects contributed to the high catalytic activity.The X-ray photoelectron spectroscopy (XPS) results indicated that the relative atomic ratios of Sm^(3+)/Sm and Oβ/O of Sm_(0.15)Mn_(0.15)Ti were 76.77at%and 44.11at%,respectively.The presence of Sm contributed to an increase in surface-absorbed oxygen (Oβ) and a decrease in Mn^(4+)surface concentration,which improved the catalytic activity.In the results of hydrogen temperature-programmed reduction(H_(2)-TPR),the presence of Sm induced a higher reduction temperature and lower H_(2)consumption (0.3 mmol·g^(-1)) for the Sm_(0.15)Mn_(0.15)Ti catalyst compared to the Mn0.3Ti catalyst.The decrease in Mn^(4+)weakened the redox property of the catalysts and increased the N_(2)selectivity by suppressing N_(2)O formation from NH3oxidation and the nonselective catalytic reduction reaction.The in situ diffuse reflectance infrared Fourier transform spectra (DRIFTs) revealed that NH3-SCR of NO over the Sm_(0.15)Mn_(0.15)Ti catalyst mainly followed the Eley–Rideal mechanism.Sm doping increased surface-absorbed oxygen and weakened the redox property to improve the NO conversion and N_(2)selectivity of the Sm_(0.15)Mn_(0.15)Ti catalyst.

Influences of oxide content and sintering temperature on microstructures and mechanical properties of intragranular-oxide strengthened iron alloys prepared by spark plasma sintering

How to increase strength without sacrificing ductility has been developed as a key goal in the manufacture of high-performance metals or alloys. Herein, the double-nanophase intragranular yttrium oxide dispersion strengthened iron alloy with high strength and appreciable ductility was fabricated by solution combustion route and subsequent spark plasma sintering, and the influences of yttrium oxide content and sintering temperature on microstructures and mechanical properties were investigated. The results show at the same sintering temperature,with the increase of yttrium oxide content, the relative density of the sintered alloy decreases and the strength increases. For Fe–2wt%Y_(2)O_(3)alloy, as the sintering temperature increases gradually, the compressive strength decreases, while the strain-to-failure increases. The Fe–2wt%Y_(2)O_(3)alloy with 15.5 nm Y_(2)O_(3)particles uniformly distributed into the 147.5 nm iron grain interior sintered at 650℃ presents a high ultimate compressive strength of 1.86 GPa and large strain-to-failure of 29%. The grain boundary strengthening and intragranular second-phase particle dispersion strengthening are the main dominant mechanisms to enhance the mechanical properties of the alloy.

Jasmonic acid-mediated stress responses share the molecular mechanism underlying male sterility induced by deficiency of ZmMs33 in maize

Plant male reproduction is a fine-tuned developmental process that is susceptible to stressful environments and influences crop grain yields.Phytohormone signaling functions in control of plant normal growth and development as well as in response to external stresses,but the interaction or crosstalk among phytohormone signaling,stress response,and male reproduction in plants remains poorly understood.Cross-species comparison among 514 stress-response transcriptomic libraries revealed that ms33-6038,a genic male sterile mutant deficient in the Zm Ms33/Zm GPAT6 gene,displayed an excessive drought stress-like transcriptional reprogramming in anthers triggered mainly by disturbed jasmonic acid(JA)homeostasis.An increased level of JA appeared in Zm Ms33-deficient anthers at both meiotic and postmeiotic stages and activated genes involved in JA biosynthesis and signaling as well as genes functioning in JA-mediated drought response.Excessive accumulation of JA elevated expression level of a gene encoding a WRKY transcription factor that activated the Zm Ms33 promoter.These findings reveal a feedback loop of Zm Ms33-JA-WRKY-Zm Ms33 in controlling male sterility and JA-mediated stress response in maize,shedding light on the crosstalk of stress response and male sterility mediated by phytohormone homeostasis and signaling.

Effect of variations in the polar and azimuthal angles of coarse particles on the structure of drainage channels in thickened beds

The 3D reconstruction and quantitative characterization of drainage channels and coarse tailings particles in a bed were conducted in this study.The influence of variations in the azimuthal angle(θ)and polar angle(φ)of coarse particles on drainage channel structure was analyzed,and the drainage mechanism of the bed was studied.Results showed that water discharge in the bed reduced the size of pores and throat channels,increasing slurry concentration.The throat channel structure was a key component of the drainage process.Theφandθof particles changed predominantly along the length direction.The changes inφhad a cumulative plugging effect on the drainage channel and increased the difficulty of water discharge.The rake and rod formed a shear ring in the tailings bed with shear,and theθdistribution of particles changed from disorderly to orderly during the rotation process.The drainage channel was squeezed during the shearing process with the change inθ,which broke the channel structure,encouraged water discharge in the bed,and facilitated a further increase in slurry concentration.The findings of this work are expected to offer theoretical guidance for preparing high-concentration underflow in the tailings thickening process.

Effective short text classification via the fusion of hybrid features for IoT social data

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

Determining the Boundary of Molten Electrolyte and Aluminum in Electrolytic Cells by Detecting Their Vertical Distribution of Potential

Aluminum production is a high energy consumption process so that maintaining fundamental compositions in balance and optimal conditions are essential.The molten electrolyte and melted aluminum are primary materials and their boundary needs to be monitored from time to time.An automatic measurement technic is presented in the paper to substitute for the traditional manual measurement work that is dull,poor efficiency and dangerous for operators.The boundary forming mechanism is analyzed,the vertical profile of electric potential is simulated,an automatic instrument is developed to sense the potential distribution,and a strategy is provided to identify the boundary according to the potential curves.Finally,some practical results are compared with manual measurements,which shows good consistency.

Potential application of a novel building-integrated solar facade water heating system in a subtropical climate:A case study for school canteen

The design and potential application analysis of the novel solar-absorbing integrated facade module and its corresponding building-integrated solar facade water heating system are presented in this study.Compared with the conventional building envelope,the main novities of the proposed facade module lie in its contributions towards the supplied water preheating to loads and the internal heat gain reduction.Besides,the proposed building-integrated solar facade water heating system broadens the combination modes of the solar thermal system and the building envelope.A dynamic model is introduced first for system design and performance prediction.To evaluate the energy-saving potential and feasibility of the implementation of the proposed facade module,this paper carried out a suitable case study by replacing the conventional facade module in the ongoing retrofitting project of a kitchen,part of the canteen of a graduate school.The detailed thermal performances of three system design options are compared in the typical winter and summer weeks and throughout the year,and then,with the preferred system design,the economic,energy,and environmental effects of the proposed system are evaluated.It was found that the system with a high flow rate of the circulating water is suggested.The annual electricity saved reaches 4175.3 kWh with yearly average thermal efficiency at 46.9%,and its corresponding cost payback time,energy payback time,and greenhouse gas payback time are 3.8,1.7,1.7 years,respectively.This study confirms the feasibility and long-term benefits of the proposed building-integrated solar facade water heating system in buildings.

Generation and Applications of a Broad Atomic Oxygen Beam with a High Flux-Density via Collision-lnduced Dissociation of O_(2)

We detail the generation of a pulsed atomic oxygen(AO)broad beam with a high flux-density via collision-induced dissociation of O_(2) to support practical industrial exploitation of AOs,particularly for facilitating 2-dimenstional oxidation/etching at a fast rate of one-monolayer per second in an area≥1000 cm².This innovation fuses the following interdisciplinary concepts:(a)a high density of O^(*) can be produced in an electron-cyclotron-resonance(ECR)O2 plasma;(b)o^(*) can be extracted and accelerated with an aperture-electrode in the plasma.

Energy-efficient computation offloading assisted by RIS-based UAV

The new applications surge with the rapid evolution of the mobile communications.The explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local side.It is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading efficiency.Mobile edge computing(MEC)assisted by reflecting intelligent surfaces(RIS)-based unmanned aerial vehicle(UAV)is a promising method to assist the users in executing the computation tasks in proximity at low cost.In this paper,we propose an energy-efficient MEC system assisted by RIS-based UAV,where the UAV with RIS mounted relays the computation tasks to the MEC server.The energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory,the transmission power of all users,and the phase shifts of the reflecting elements placed on the UAV.Considering that the optimization problem is non-convex,we propose a deep deterministic policy gradient(DDPG)-based algorithm.By combining the DDPG algorithm with the energy efficiency maximization problem,the optimization problem can be resolved.Finally,the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.

Valuable metals recovery from spent ternary lithium-ion battery:A review

Ternary lithium-ion batteries(LIBs),widely used in new energy vehicles and electronic products,are known for their high en-ergy density,wide operating temperature range,and excellent cycling performance.With the rapid development of the battery industry,the recycling of spent ternary LIBs has become a hot topic because of their economic value and environmental concerns.To date,a con-siderable amount of literature has reported on the recycling of spent ternary LIBs designed to provide an efficient,economical,and envir-onmentally friendly method for battery recycling.This article examines the latest developments in various technologies for recycling spent ternary LIBs in both research and practical production,including pretreatment,pyrometallurgy,hydrometallurgy,pyro-hydrometallurgy,and direct regeneration.Suggestions for addressing challenges based on the benefits and disadvantages of each method are made.Finally,through a comparison of the feasibility and economic benefits of various technologies,the challenges faced during battery recycling are summarized,and future development directions are proposed.