Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Filter-based iterative learning control for linear large-scale industrial processes

In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information,a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy,

Industrial Arrangement of Large-scale,Non-grid-connected Wind Power Industrial Zones in Coastal Areas of China

Constructing an industrial system for a large-scale,non-grid-connected wind power industry is a key step towards the diverse utilization of wind power.However,wind power exploitation is not only a technical challenge but an industrial problem as well.The objective of this study is to introduce a concept of large-scale,non-grid-connected wind power(LSNGCWP) industrial zones and establish an evaluation model to assess their industrial arrangement.The data of wind energy,industry,nature resources and socio-economy were collected in this study.Using spatial overlay analysis of geographic information system,this study proposes a spatial arrangement of the LSNGCWP indus-trial zones in the coastal areas of China,which could be summarized as the 'one line and three circles' structure,which will contribute to the optimization of the industrial structure,advance the wind power technology,coordinate the multi-industrial cooperation,and upgrade the industrial transformation of China's coastal areas.

Building 3D CityGML models of mining industrial structures using integrated UAV and TLS point clouds

Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.

The research on the operating models of public service platform of the Hubei province key-developing industrial clusters

Public service platform of industrial cluster includes Industry Association, Guarantee Agency, Productivity Center, R＆D Center, Inspection and Test Center, Training Institutions, Product Center and Professional Market, etc. By case study in Hubei province, local government and leading enterprises, they are the major part of public service platform constructing of industrial cluster.

Construction of Risk Assessment Application Model of Epidemic Disease in Large-scale Pig Farms

The application model of epidemic disease assessment technology for Web-based large-scale pig farm was expounded from the identification of epidemic disease risk factors, construction of risk assessment model and development of risk assessment system. The assessed pig farm uploaded the epidemic disease risk data information through on-line answering evaluating questionnaire to get the immediate evaluation report. The model could enhance the risk communication between pig farm veterinarian, manager and veterinary experts to help farm system understand and find disease risk factors, assess and report the potential high risk items of the pig farm in the three systems of engineering epidemic disease prevention technology, biological safety and immune monitoring, and promote the improvement and perfection of epidemic disease prevention and control measures.

Analysis on the Competitiveness of Industrial Real Estate Based on Diamond Model ——Taking Jiangxi Province as an Example

Based on the current development of industrial real estate of Jiangxi Province, comprehensive analysis was conducted to 6 aspects using the idea of diamond model, namely, factor conditions, demand conditions, related and supporting industries, firm strategy, structure and rivalry, government and chance through the comparison with the other industrial real estates and analysis on horizontal competition in China. Development countermeasures were investigated to improve the competitiveness of the industrial real estate in Guangxi, putting forward the strategies of developing the role of government and business, seizing the opportunities of the times, city-industry integration development, implementation of industrial integration and integration of city development, the implementation of industrial integration and investment planning.

NEW MODEL OF THEODOLITE INDUSTRIAL MEASURING SYSTEM

Comparing with the coordinates measuring machine (CMM),the theodolite industrial measuring system (TIMS) can be easily moved and it can measure large sized industrial targets contactlessly.But up to now the precision of the TIMS has been considered so low that the TIMS isnt applied to some precise measurements.The error in self locating TIMS is a main factor which affects the precision of the TIMS.A new model of the TIMS is given out in this paper,and it can eliminate the error in self locating the TIMS.The new model is not only investigated and analyzed theoretically but also verified by the real measured data.

Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus

A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.

Validating the Runoff from the PRECIS Model Using a Large-Scale Routing Model

The streamflow over the Yellow River basin is simulated using the PRECIS （Providing REgional Climates for Impacts Studies） regional climate model driven by 15-year （1979-1993） ECMWF reanalysis data as the initial and lateral boundary conditions and an off-line large-scale routing model （LRM）. The LRM uses physical catchment and river channel information and allows streamflow to be predicted for large continental rivers with a 1°×1° spatial resolution. The results show that the PRECIS model can reproduce the general southeast to northwest gradient distribution of the precipitation over the Yellow River basin, The PRECIS- LRM model combination has the capability to simulate the seasonal and annual streamflow over the Yellow River basin. The simulated streamflow is generally coincident with the naturalized streamflow both in timing and in magnitude.

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

In the present industrial revolution era,the industrial mechanical system becomes incessantly highly intelligent and composite.So,it is necessary to develop data-driven and monitoring approaches for achieving quick,trustable,and high-quality analysis in an automated way.Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery.The advent of deep learning(DL)methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals.This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network(IIFD-SOIR)Model.The proposed model operates on three major processes namely signal representation,feature extraction,and classification.The proposed model uses a Continuous Wavelet Transform(CWT)is for preprocessed representation of the original vibration signal.In addition,Inception with ResNet v2 based feature extraction model is applied to generate high-level features.Besides,the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer.Finally,a multilayer perceptron(MLP)is applied as a classification technique to diagnose the faults proficiently.Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset.The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6%and 99.64%on the applied gearbox dataset and bearing dataset.The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

Evolution History and Development Trend of Industrialized Live Pig Breeding Model in China

Tremendous achievements of live pig industry in China are closely related to the industrialization of the industry,and development trend of the latter is essential for maintaining sustained and stable development of animal husbandry.The paper,on the basis of defining the evolution of industrialized live pig breeding model,elaborated the industrialized operation models of live pig industry in China since 1978,i.e.household operation,large-scale operation,and industrialized operation.The external environment for the development of live pig industry was analyzed,such as global economic competition,development of experience economy,and stronger green consciousness of consumers.Then development trend of industrialized live pig breeding was analyzed as"expanding international market,consolidating domestic market,integrating resources of live pig industry for the integrated operation,promoting the industrialization model and breeding technology driven by live pig processing,applying animal welfare and the internet of things in live pig breeding industry".

Spatial Patterns and Drivers of Intelligent Manufacturing from a‘Glob-allocal'Perspective:A Study of China's Industrial Robotics

The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency.In this study,we took the industrial robot industry(IRI)as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective,and the modified diamond model(DM)was used to analyze the influencing factors.Results show that:1)the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar,centered in the southeast.Key investment areas are in the east and south.The spatial distribution of China's IRI covers a multitude of provinces and obtains differ-ent scales of investment in different countries(regions).2)The spatial correlation between foreign investors and China's provincial-level administrative regions(PARs)forms a network,and the network of foreign-invested enterprises is more stable.Different countries(regions)have distinct location preferences in China,with significant spatial differences in correlation degrees.3)Overall,the interac-tion of these factors shapes the location decisions and correlation patterns of industrial robot enterprises.This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and sugges-tions for national IMI planning and relevant industry investors.

The impact of industrial transformation on green economic efficiency:New evidence based on energy use

Developing a green economy is key to achieving the 2030 Sustainable Development Goals. This paper uses the SBM-GML index, which includes non-desired outputs, to measure the trend of regional green economic efficiency changes and analyze the impact mechanism and realization path of industrial transformation on green economic efficiency. The research results show that advanced industrial structure has a positive influence on green economic efficiency nationwide, while energy utilization structure and energy utilization efficiency have positive partial intermediary effects in the influence path;industrial structure rationalization is also significantly positively related to green economic efficiency nationwide, and the mediating effect of energy utilization is positive. The impact of industrial transformation on green economic efficiency has regional heterogeneity, and the mediating effect of energy use also differs. Among them, the impact effect in the eastern region is basically consistent with the national sample, but is negative in the central and western regions. This paper proposes countermeasures in terms of adjusting the industrial structure, improving energy efficiency, and perfecting industrial and energy policies, which can provide theoretical and practical references for promoting the transformation and upgrading of regional industrial structure, optimizing energy utilization, and advancing the efficiency of the national and regional green economy.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.

Industrial Poverty Alleviation Model in Southwestern High-altitude Mountainous Areas of China——A Case Study of Industrial Poverty Alleviation of Xueshan Township in Luquan County of Yunnan Province through Planting Codonopsis pilosula

Due to the severe restrictions of natural conditions and ecological environment,high-altitude mountainous areas usually become the " hard bones" in the battle against poverty. Xueshan Township,Luquan Yi and Miao Autonomous County of Yunnan Province,located in the alpine valley of Jinsha River,is a major township with wide and deep poverty,and the incidence of poverty is up to 45. 00%. In recent years,Xueshan Township has insisted on the battle against poverty,made effort to develop the Codonopsis pilosula industry,and successfully developed a road to poverty alleviation through C. pilosula industry,and formed a unique industrial poverty alleviation model by the end of 2018,the incidence of poverty dropped to 0. 74%. Based on field survey and interview,this paper analyzes and summarizes the specific practices,main results,practical experience and promotion and application measures of the poverty alleviation model of C. pilosula planting industry in Xueshan Township,in the hope of providing certain reference for the targeted poverty alleviation in similar areas in Yunnan Province and other provinces of China.

Technological anxiety:Analysis of the impact of industrial intelligence on employment in China

Employment is the greatest livelihood.Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an“icing on the cake”or“adding fuel to the fire”needs further study.This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy.First,from the theoretical logic and the economic development law,this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs.Then,based on the panel data of 269 cities in China from 2006 to 2021,we use the two-way fixed effect model,dynamic threshold model,and two-stage intermediary effect model.The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism.Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the“creation effect”.In other words,advanced digital technology has created additional jobs,and the overall supply of employment in the labor market has increased.The conclusion is still valid after the endogeneity identification and robustness test.In addition,the positive effect has a nonlinear effect on the network scale.When the installation density of industrial robots exceeds a particular threshold value,the division of labor continues to deepen under the combined action of the production efficiency and compensation effects,which will cause enterprises to increase labor demand further.Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions.This study concludes that in the digital China era,the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market.This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.

The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

Research on the Impact of Chinese Digital Inclusive Finance on Industrial Structure Upgrade—Based on Spatial Dubin Model

Based on the panel data of 30 provinces, municipalities, and autonomous regions in China from 2011 to 2018, this paper uses the digital inclusive financial index and industrial structure upgrading coefficient of the Internet Research Center of Peking University as the core explanatory and explained variables to construct a spatial panel. Bin model performs regression analysis on the effect of digital inclusive finance in the upgrading of industrial structure. The results prove that the development of digital inclusive finance in this province and city has significantly promoted the upgrading of the regional industrial structure, and it has a positive overall effect on the upgrading of industrial structure.