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 Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation

Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.

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%.

Current status and prospects of research on safety situation awareness of high speed railway operation environment

Purpose–The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail.The operating environment of the high-speed rail is complex,and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters,perimeter intrusion and external environmental hazards.The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.Design/methodology/approach–In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments,the research status is elaborated,and the latest research progress and achievements of the team are introduced.This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological,perimeter and external environmental situation perception methods for high-speed rail operation.Findings–Based on the technical route of“situational awareness evaluation warning active control,”a technical system for monitoring the safety of high-speed train operation environments has been formed.Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters,perimeter intrusion and the external environment on high-speed rail safety.These works strongly support the improvement of China’s railway environmental safety guarantee technology.Originality/value–With the operation of CR450 high-speed trains with a speed of 400 kmper hour and the application of high-speed train autonomous driving technology in the future,new and higher requirements have been put forward for the safety of high-speed rail operation environments.The following five aspects of work are urgently needed:(1)Research the single factor disaster mechanism of wind,rain,snow,lightning,etc.for high-speed railways with a speed of 400 kms per hour,and based on this,study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment,revealing the coupling disastermechanism ofmultiple influencing factors;(2)Research covers multi-source data fusion methods and associated features such as disaster monitoring data,meteorological information,route characteristics and terrain and landforms,studying the spatio-temporal evolution laws of meteorological disasters,perimeter intrusions and external environmental hazards;(3)In terms of meteorological disaster situation awareness,research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines;(4)In terms of perimeter intrusion,research amulti-modal fusion perception method for typical scenarios of high-speed rail operation in all time,all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and(5)In terms of external environment,based on the existing general network framework for change detection,we will carry out research on change detection and algorithms in the surrounding environment of highspeed rail.

Current Situation and Countermeasures of Household Waste Classification in Feixi County, Hefei City

Feixi County has made significant progress in promoting waste classification,such as establishing a comprehensive classification system,and effectively reducing environmental pollution and waste volume.However,with the increase in waste generation,the county faces multiple challenges especially in the disposal of kitchen waste and improvement of residents environmental awareness.To address these issues,Feixi County has implemented various measures,such as strengthening the construction of infrastructure for waste classification,improving regulations and clearly defining responsibilities,enhancing residents environmental awareness to encourage their participation in waste classification through education and promotion,and increasing supervision to ensure effective implementation of the work.It emphasizes community governance,encourage all parties to participate in it,and strengthen publicity,education and training to enhance residents participation.Feixi County has achieved positive results,but efforts are needed to further improve facilities,raise awareness,enhance supervision,and ensure the continuous effectiveness of waste classification work to promote urban green sustainable development.

Ways to Cultivate and Develop Human Resources in the New Situation

In the trend of globalization,digitization,and economic integration,human resources are the core competitiveness of enterprises,and cultivating and developing them is crucial for enhancing competitive advantages.This article delves into how human resource development drives enterprise innovation,promotes team collaboration,and optimizes performance management.Currently,although many enterprises are beginning to attach importance to human resource development,they still face problems such as insufficient investment,weak targeted training,and insufficient exploration of employee potential.Therefore,it is proposed that in the new situation,enterprises need to strengthen diversified training strategies,use digital tools to improve training efficiency,build a fair performance evaluation system,stimulate employee potential,and promote internal talent mobility.The implementation of these strategies will effectively enhance the efficiency of enterprise human resource management and consolidate market position.

Architectural Design and Urban Planning in the New Situation

Under the influence of the world economy,China’s economic development is very rapid,the people’s living standards have been greatly improved,and the urbanization process has been accelerated.As a part of urban planning,architecture plays an important role in urban planning and construction,so urban planning must be combined with the future development goals of the city,develop in an all-round way,strengthen architectural design,ensure the consistency of urban planning and architectural design,and create a good environment for urban development.Therefore,this paper analyzes the relationship between architectural design and urban planning under the new situation,clarifies the relationship between architectural design and urban planning,and strengthens urban construction.

Exploration of the Current Situation and Mechanism Innovation of University Education Management under the New Situation

In the new era,university education management should be oriented toward the cultivation of innovative talents and the construction of a comprehensive education system.It should develop capable and qualified higher-level talents in response to the social demand for talent application,thereby enhancing the overall educational competitiveness of universities and driving deep reforms in educational activities.However,in current educational practice,efficient educational management often focuses on curriculum development and cultural education,with insufficient attention paid to the development of students’comprehensive literacy.The talent cultivation and education programs are not yet perfect.This article explores the current situation of university education management activities under the new situation and considers practical methods for innovating university education management mechanisms.

Research on English Language and Literature Teaching Strategies Based on Situational Teaching Method

The teaching of English language and literature not only focuses on developing students’English language proficiency but also emphasizes their understanding and cognition of the cultural connotations behind the English language.This dual focus aims to cultivate students’cross-cultural communication skills and practical English skills.The application of the situational teaching method can create corresponding learning scenarios based on English language knowledge,thereby enriching students’learning experiences and understanding within these scenarios.This method has significant advantages in enhancing the effectiveness of English language and literature teaching.Consequently,this paper first analyzes the significance of applying the situational teaching method in university-level English language and literature instruction.It then proposes several strategies for implementing this method,with the goal of enhancing students’language awareness,cultural literacy,and comprehensive English skills.

Discussion on Cultural Integration and Educational Services for International Students from the Perspective of Situational Communication

From the perspective of situational communication,this paper aims to explore how to promote cultural integration and provide better educational services for international students in China.This paper first analyzes the basic assumptions and key factors of situational communication based on international students in China.Secondly,the relationship between situational communication and cultural integration and educational services for international students in China is discussed in depth.Additionally,the role of situational communication in educational services for international students in China,such as promoting cross-cultural communication,solving language barriers,and enhancing mutual understanding,is proposed.Lastly,from the perspective of situational communication,an optimization path of educational services and cultural integration for international students in China is proposed,including the provision of diversified educational resources,the enhancement of cross-cultural communication,and the establishment of an effective communication mechanism.This study expands the perspective of educational services for international students in China,provides a new theoretical framework and practical path,and offers a reference for promoting cultural integration and improving educational services for international students in China.

Current Situation and Countermeasures of the Construction of “Double-Qualified” Teachers in Higher Vocational Colleges

The construction of“double-qualified”teachers is the basic premise for the high-quality development of higher vocational education,and it is also the key force for higher vocational colleges to cultivate high-quality skilled talents.This paper probes into the positioning of the construction of“double-qualified”teachers in higher vocational colleges,deeply analyzes the current situation of the construction of“double-qualified”teachers in higher vocational colleges,and provides the countermeasures of the construction of“double-qualified”teachers.

Research on the Current Situation and Countermeasures of Integrating Artificial Intelligence into Ideological and Political Teaching in Universities

With the rapid development of modern science and technology,artificial intelligence(AI)technology is becoming more and more mature,which has brought a profound impact on every field of human society.The new era background provides a new teaching concept for ideological and political teaching in colleges and universities.Teachers in colleges and universities should adopt a positive attitude to face the teaching change brought by new technology,update the teaching ideas in time,and adjust the teaching strategy.This paper will analyze the current situation of the integrated development of AI and ideological and political teaching and put forward corresponding teaching strategies to help train well-developed socialist successors.

Analysis of the Current Situation of Drug Clinical Trial Institutions in Shaanxi Province

To understand the current situation of institutional registration in Shaanxi Province after the implementation ofregistration system management in drug clinical trial institutions.Relevant information was collected on the“Announcement on the Accreditation of Drug Clinical Trial Institutions”issued by the National Medical Products Administration from 2005 to August 2022,the record management information system of drug and medical device clinical trial institutions,and the drug clinical trial registration and information publicity platform.A retrospective analysis was carried out in terms of institutional development,regional distribution,registered majors,principal investigators,and the number of drug clinical trials.After the implementation of institution registration,the number of drug clinical trial institutions in Shaanxi Province increased by 47.4%,884 principal investigators were registered,the number of registered majors expanded from 58 qualified to 117,and the professional scope increased by 50.4%.The policy of institution registration is conducive to promoting the rational use of medical resources and the development of drug clinical trial institutions and improving the healthy development of the pharmaceutical industry in Shaanxi Province.

Research on the Current Situation of China’s Higher Education Students Studying Abroad in the Context of Higher Education Internationalization

In the context of education internationalization,the internationalization of higher education has become a major trend in the field of education in most countries,with international student education as the core.This phenomenon promotes cultural exchanges and integration among countries as well as higher education reform,which is of great significance in enhancing the international influence of higher education in each country.However,there are also some challenges faced by higher education students when studying abroad.The article digs into the origin and history of studying abroad,analyzes the current situation and reasons for higher education students in China to study abroad,discusses the opportunities and challenges faced by higher education students in China when studying abroad,and analyzes the trend of studying abroad in China.

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.