Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

尺度适应性起沙机制在CMA-CUACE/Dust中的应用

沙尘暴是影响我国重要的灾害性天气之一,针对中国气象局亚洲沙尘暴数值预报系统CMA-CUACE/Dust(China Meteorological Administration Unified Atmospheric Environment for Dust)的沙尘质量浓度在中亚高估、蒙古北部低估、在我国消散过快以及极端沙尘暴预报峰值偏低等问题,应用与模式格距匹配的尺度适应性起沙机制并更新风蚀资料库对模式进行改进。对2021年3月13—17日东亚最强沙尘暴个例和2023年3—5月与业务运行环境一致的连续预报试验表明,改进后的模式(CMA-CUACE/Dust V1.5)有效改善了上述不足,极端沙尘暴过程传输至我国后的沙尘质量浓度峰值与观测接近。连续预报试验TS(threat score)评分显示:CMA-CUACE/Dust V1.5预报一致性和连续性较好,1~5 d不同时效预报TS评分明显高于改进前和韩国模式ADAM(the Asian Dust Aerosol Model),漏报率明显降低,对2023年5次沙尘过程的4次预报TS评分高于0.3,部分过程超过0.5。

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Deflagration characteristics of freely propagating flames in magnesium hydride dust clouds

The flame propagation processes of MgH_(2)dust clouds with four different particle sizes were recorded by a high-speed camera.The dynamic flame temperature distributions of MgH_(2)dust clouds were reconstructed by the two-color pyrometer technique,and the chemical composition of solid combustion residues were analyzed.The experimental results showed that the average flame propagation velocities of 23μm,40μm,60μm and 103μm MgH_(2)dust clouds in the stable propagation stage were 3.7 m/s,2.8 m/s,2.1 m/s and 0.9 m/s,respectively.The dust clouds with smaller particle sizes had faster flame propagation velocity and stronger oscillation intensity,and their flame temperature distributions were more even and the temperature gradients were smaller.The flame structures of MgH_(2)dust clouds were significantly affected by the particle sinking velocity,and the combustion processes were accompanied by micro-explosion of particles.The falling velocities of 23μm and 40μm MgH_(2)particles were 2.24 cm/s and 6.71 cm/s,respectively.While the falling velocities of 60μm and 103μm MgH_(2)particles were as high as 15.07 cm/s and 44.42 cm/s,respectively,leading to a more rapid downward development and irregular shape of the flame.Furthermore,the dehydrogenation reaction had a significant effect on the combustion performance of MgH_(2)dust.The combustion of H_(2)enhanced the ignition and combustion characteristics of MgH_(2)dust,resulting in a much higher explosion power than the pure Mg dust.The micro-structure characteristics and combustion residues composition analysis of MgH_(2)dust indicated that the combustion control mechanism of MgH_(2)dust flame was mainly the heterogeneous reaction,which was affected by the dehydrogenation reaction.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Risk Analysis Using Multi-Source Data for Distribution Networks Facing Extreme Natural Disasters

Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.

Evaluation and Improvement Strategies for Slow Traffic Systems Based on Multi-source Big Data:A Case Study of Shijingshan District of Beijing City

The slow traffic system is an important component of urban transportation,and the prerequisite and necessary condition for Beijing to continue promoting“green priority”are establishing a good urban slow traffic system.Shijingshan District of Beijing City is taken as a research object.By analyzing and processing population distribution data,POI data,and shared bicycle data,the shortcomings and deficiencies of the current slow traffic system in Shijingshan District are explored,and corresponding solutions are proposed,in order to provide new ideas and methods for future urban planning from the perspective of data.

Classification of Beijing Line 10 Subway Living Circle Based on Multi-source Big Data

In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.

Effects of dust controls on respirable coal mine dust composition and particle sizes:case studies on auxiliary scrubbers and canopy air curtain

Control of dust in underground coal mines is critical for mitigating both safety and health hazards.For decades,the National Institute of Occupational Safety and Health(NIOSH)has led research to evaluate the effectiveness of various dust control technologies in coal mines.Recent studies have included the evaluation of auxiliary scrubbers to reduce respirable dust downstream of active mining and the use of canopy air curtains(CACs)to reduce respirable dust in key operator positions.While detailed dust characterization was not a focus of such studies,this is a growing area of interest.Using preserved filter samples from three previous NIOSH studies,the current work aims to explore the effect of two different scrubbers(one wet and one dry)and a roof bolter CAC on respirable dust composition and particle size distribution.For this,the preserved filter samples were analyzed by thermogravimetric analysis and/or scanning electron microscopy with energy dispersive X-ray.Results indicate that dust composition was not appreciably affected by either scrubber or the CAC.However,the wet scrubber and CAC appeared to decrease the overall particle size distribution.Such an effect of the dry scrubber was not consistently observed,but this is probably related to the particular sampling location downstream of the scrubber which allowed for significant mixing of the scrubber exhaust and other return air.Aside from the insights gained with respect to the three specific dust control case studies revisited here,this work demonstrates the value of preserved dust samples for follow-up investigation more broadly.

Filtration performance and modeling of granular bed for dust removal from coal pyrolytic vapors

Dust removal from pyrolytic vapors at high temperatures is an obstacle to the industrialization of the coal pyrolysis process.In this work,a granular bed with expanded perlites as filtration media was designed and integrated into a 10 t·d^(–1)coal pyrolysis facility.The testing results showed that around 97.56%dust collection efficiency was achieved.As a result,dust content in tar was significantly lowered.The pressure drop of the granular bed maintained in the range of 356 Pa to 489 Pa.The dust size in the effluent after filtration exhibited a bimodal distribution,which was attributed to the heterogeneity of the dust components.The effects of filtration bed on pyrolytic product yields were also discussed.A modified filtration model based on the macroscopic phenomenological theory was proposed to describe the performance of the granular bed.The computation results were well agreed with the experimental data.

Experimental and numerical simulation study on forced ventilation and dust removal of coal mine heading surface

In order to study the problems of unreasonable airflow distribution and serious dust pollution in a heading surface,an experimental platform for forced ventilation and dust removal was built based on the similar principles.Through the similar experiment and numerical simulation,the distribution of airflow field in the roadway and the spatial and temporal evolution of dust pollution under the conditions of forced ventilation were determined.The airflow field in the roadway can be divided into three zones:jet zone,vortex zone and reflux zone.The dust concentration gradually decreases from the head to the rear of the roadway.Under the forced ventilation conditions,there is a unilateral accumulation of dust,with higher dust concentrations away from the ducts.The position of the equipment has an interception effect on the dust.The maximum error between the test value and the simulation result is 12.9%,which verifies the accuracy of the experimental results.The research results can provide theoretical guidance for the application of dust removal technology in coal mine.

Ion acoustic solitary waves in an adiabatic dusty plasma:Roles of superthermal electrons,ion loss and ionization

We investigate propagation of dust ion acoustic solitary wave(DIASW)in a multicomponent dusty plasma with adiabatic ions,superthermal electrons,and stationary dust.The reductive perturbation method is employed to derive the damped Korteweg-de Vries(DKdV)equation which describes DIASW.The result reveals that the adiabaticity of ions significantly modifies the basic features of the DIASW.The ionization effect makes the solitary wave grow,while collisions reduce the growth rate and even lead to the damping.With the increases in ionization cross sectionΔσ/σ_(0),ion-to-electron density ratioδ_(ie)and superthermal electrons parameterκ,the effect of ionization on DIASW enhances.

Oxygen-assisted zinc recovery from electric arc furnace dust using magnesium chloride

Electric arc furnace(EAF)dust is an important secondary resource containing metals,such as zinc(Zn)and iron(Fe).Recover-ing Zn from EAF dust can contribute to resource recycling and reduce environmental impacts.However,the high chemical stability of ZnFe_(2)O_(4)in EAF dust poses challenges to Zn recovery.To address this issue,a facile approach that involves oxygen-assisted chlorination using molten MgCl_(2)is proposed.This work focused on elucidating the role of O2 in the reaction between ZnFe_(2)O_(4)and molten MgCl_(2).The results demonstrate that MgCl_(2)effectively broke down the ZnFe_(2)O_(4)structure,and the high O2 atmosphere considerably promoted the sep-aration of Zn from other components in the form of ZnCl_(2).The presence of O2 facilitated the formation of MgFe_(2)O_(4),which stabilized Fe and prevented its chlorination.Furthermore,the excessive use of MgCl_(2)resulted in increased evaporation loss,and high temperatures pro-moted the rapid separation of Zn.Building on these findings,we successfully extracted ZnCl_(2)-enriched volatiles from practical EAF dust through oxygen-assisted chlorination.Under optimized conditions,this method achieved exceptional Zn chlorination percentage of over 97%within a short period,while Fe chlorination remained below 1%.The resulting volatiles contained 85wt%of ZnCl_(2),which can be further processed to produce metallic Zn.The findings offer guidance for the selective recovery of valuable metals,particularly from solid wastes such as EAF dust.

Global dust density in two-dimensional complex plasma

The driven-dissipative Langevin dynamics simulation is used to produce a two-dimensional(2D) dense cloud, which is composed of charged dust particles trapped in a quadratic potential. A 2D mesh grid is built to analyze the center-to-wall dust density. It is found that the local dust density in the outer region relative to that of the inner region is more nonuniform,being consistent with the feature of quadratic potential. The dependences of the global dust density on equilibrium temperature, particle size, confinement strength, and confinement shape are investigated. It is found that the particle size, the confinement strength, and the confinement shape strongly affect the global dust density, while the equilibrium temperature plays a minor effect on it. In the direction where there is a stronger confinement, the dust density gradient is bigger.