Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus on why and what is Industry 5.0 yet.In this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development.We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies.Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

Research on the Industrial Transfer and Restructuring Path of the Energy-Intensive Manufacturing Industry in Western China

The orderly transfer of the manufacturing industry is a major action in China’s industrial restructuring.From the perspective of industrial transfer,we used the concentration ratio to depict the trend of the industrial transfer of energy-intensive manufacturing in the eastern,central,and western regions since the policy of large-scale development of western China was implemented.We measured the total factor productivity(TFP)of western China using the DEAMalmquist index method.We conducted a regression analysis to measure the effect of western China’s undertaking of the transfer of the energy-intensive manufacturing industry.The findings of this study show that during 2000–2019,eleven provinces(as well as autonomous regions and municipalities)in western China undertook the transfer of the energy-intensive manufacturing industry from the eastern and central regions to varying degrees,exhibiting significant phase features regarding the rate and scale of transfers.Further investigation also demonstrated that the transfer of energy-intensive manufacturing industries has a U-shaped enabling effect on TFP in western China with the scale effect greater than the technology effect.Therefore,it is necessary to transition from“extensive industrial transfer”at the cost of the labor force,land,and resources to“modern industrial transfer”featured by technology and efficiency improvements to contribute to industrial restructuring in western China effectively.

Can Industrial Structure Upgrading Restrain Industrial Land Expansion?Evidence from China

China has made great achievements in industrial development and is transforming into a powerful manufacturing country.Meanwhile,the industrial land scale is also expanding.However,whether industrial structure upgrading achieves the purpose of restraining industrial land expansion remains unanswered.By calculating the industrial land structure index(ILSI)and industrial land expansion scale(ILES),this study analyzed their temporal and spatial distribution characteristics at both regional and city levels from 2007to 2020 in China.Results show that industrial land expansion presents a different trend in the four regions,the ILES in the eastern region is the largest,and the speed of industrial land expansion has declined since 2013,but it has gradually increased since 2016.The ILSI of the eastern and central regions is higher than that of the western and northeastern regions.Furthermore,a spatial Durbin model(SDM)has been established to estimate the spatial effect of industrial structure upgrading on industrial land expansion from 2007 to2020.Notably,industrial structure upgrading has not slowed industrial land expansion.The eastern and western regions require a greater amount of industrial land while upgrading the industrial structure.The improvement of the infrastructure level and international trade level has promoted industrial land expansion.

Requirements ranking based on crowd-sourcing high-end product USs

Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.

Potential industrial applications of photo/electrocatalysis: Recent progress and future challenges

Nowadays,the rapid development of the social economy inevitably leads to global energy and environmental crisis.For this reason,more and more scholars focus on the development of photocatalysis and/or electrocatalysis technology for the advantage in the sustainable production of high-value-added products,and the high efficiency in pollutants remediation.Although there is plenty of outstanding research has been put forward continuously,most of them focuses on catalysis performance and reaction mechanisms in laboratory conditions.Realizing industrial application of photo/electrocatalytic processes is still a challenge that needs to be overcome by social demand.In this regard,this review comprehensively summarized several explorations in thefield of photo/electrocatalytic reduction towards potential industrial applications in recent years.Special attention is paid to the successful attempts and the current status of photo/electrocatalytic water splitting,carbon dioxide conversion,resource utilization from waste,etc.,by using advanced reactors.The key problems and challenges of photo/electrocatalysis in future industrial practice are also discussed,and the possible development directions are also pointed out from the industry view.

Research on Risk Identification and Industrial Governance of Digital Education Products Based on Data Annotation Technology

The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.

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.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Attributes of Specialized Households'Resilience and Its Impact on Rural Industrial Advancement:A Case Study of National Musical Instrument Production Specialized Households in Lankao County,Henan,China

Specialized households serve as the primary units within specialized villages in China,and their capacity to withstand risks and external influences significantly shapes the future trajectory of specialized villages and the overall vitality of the rural economy.In this study,we established a measurement indicator system based on the definition of specialized households’resilience,elucidating the logical connection between specialized households’resilience and rural industrial development in China.The musical instrument industry in Lankao County,Henan Province of China,was employed as a case;survey data,the entropy method,and an obstacle diagnosis model were used to examine how instrument production specialized households responded to the challenges posed by Corona Virus Disease 2019(COVID-19)and the tightening of national environmental protection policies,yielding the following key findings:1)there exists substantial variation in the comprehensive resilience levels among different specialized households;2)the ability to learn and adapt is the most significant contributor to the overall resilience level of specialized households;3)technological proficiency and access to skilled talent emerge as pivotal factors influencing specialized households’resilience;4)the positioning of specialized households within the industrial supply chain and the stability of their income have a direct bearing on their resilience level.The influence of specialized households’resilience on industrial development primarily manifests in the following ways:stronger resilience correlates with increased stability in production and sales,fostering a more proactive approach to future actions.However,heightened exposure to the external macroeconomic environment can lead to a higher rate of export reduction.To enhance the development resilience of entities like specialized households and family farms,and to invigorate rural economic development,escalating investments in rural science and technology and prioritizing the training of technical talent become imperative.

Impacts of agri-food e-commerce on traditional wholesale industry:Evidence from China

Rapidly expanding studies investigate the effects of e-commerce on company operations in the retail market.However,the interaction between agri-food e-commerce(AEC)and the traditional agri-food wholesale industry(AWI)has not received enough attention in the existing literature.Based on the provincial panel data from 2013 to 2020 in China,this paper examines the effect of AEC on AWI,comprising three dimensions:digitalization(DIGITAL),agrifood e-commerce infrastructure and supporting services(AECI),and agri-food e-commerce economy(AECE).First,AWI and AEC are measured using an entropy-based combination of indicators.The results indicate that for China as a whole,AWI has remained practically unchanged,whereas AEC exhibits a significant rising trend.Second,the findings of the fixed-effect regression reveal that DIGITAL and AECE tend to raise AWI,whereas AECI negatively affects AWI.Third,threshold regression results indicate that AECI tends to diminish AWI with three-stage inhibitory intensity,which manifests as a first increase and then a drop in the inhibition degree.These results suggest that with the introduction of e-commerce for agricultural product circulation,digital development will have catfish effects that tend to stimulate the vitality of the conventional wholesale industry and promote technical progress.Furthermore,the traditional wholesale industry benefits financially from e-commerce even while it diverts part of the traditional wholesale circulation for agricultural products.

New Industrialization: Characteristics, System Development and Implementation Pathway

New industrialization in China, different from its past economic development pattern or patterns in developed nations, is the country’s theoretical innovation based on the positive and negative experiences of industrialization at home and worldwide. New industrialization has various novel characteristics, including new sources of efficiency, new factors of production, new organizational forms, and new constraints. In addition, it has certain particularities arising from modernization with Chinese characteristics. This article summarizes the characteristics of new industrialization from the perspectives of people-centered approach, quality-first concept, independent innovation, green low-carbon economics, digital-real integration, and open circulation. There are four systems for promoting new industrialization: A self-sustained scientific and technological system, a high-end advanced manufacturing system, a green low-carbon circular system, and a division of labor system with domestic and international circulation. The Chinese new industrialization proposes the pathway and policy measures considering the new global situation and the requirements of new goals of strengthening organization and leadership, reducing factor cost, accelerating independent technological innovation, smoothing domestic and international circulation, and optimizing competition environment.

Digital Twin-Assisted Semi-Federated Learning Framework for Industrial Edge Intelligence

The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data generated by the IIo T,coupled with heterogeneous computation capacity across IIo T devices,and users’data privacy concerns,have posed challenges towards achieving industrial edge intelligence(IEI).To achieve IEI,in this paper,we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server.In addition,we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIo T devices through the mapping of physical entities.We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded nonprivate data.As the joint problem is NP-hard and combinatorial and taking into account the reality of largescale device training,we develop a multi-agent hybrid action deep reinforcement learning(DRL)algorithm to find the optimal solution.Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

Offshore high-grade limemud resources of west coast of India:Economic potential and industrial applications

Limestone is one of the essential raw materials in the cement,paint,steel,ceramic,glass,chemical,pharmaceutical,paper,and fertilizer industries.In India,only 8%of the limestone resources are placed under the reserve category,of which 97%is of cement grade.Thus,India depends on imports to bridge the demand‐supply gap of steel,blast furnace,and chemical‐grade limestone.Efforts of Geological Survey of India(GSI)to locate alternate sources for limestone led to the discovery of enormous quantities of carbonate minerals called limemud from the continental shelf margin of the west coast of India.GSI carried out systematic studies to explore the nature of the disposition,quality,quantity,and suitability of the offshore limemud for various industrial applications.A preliminary estimate of resources using high‐resolution subbottom profiling and sediment core sample studies established the occurrence of more than 172 billion tonnes of high‐grade(The content of CaCO3 is greater than 80 wt%)limemud in 0.4–28.0m thick stratified sediment layers spread over an area of 18000 km2.Chemical,physical,mineralogical,beneficiation,and agglomeration studies found the offshore limemud as a potential replacement for limestone in the cement,filler,blast furnace,steel melting shop,lime production,paint,and Grade‐I steel industries.An assessment of mining and transportation costs indicates that the offshore limemud(USD 5–6/ton)is more cost‐effective than that imported from other countries(USD16‐18/ton).With several advantageous factors like low impurity,mode of occurrence in overburden‐free stratified form,fine‐grained slurry nature,and shallow water depth,sustainable mining of offshore limemud could be a future reality with controllable technological,economic,and environmental challenges.

Exploring Paths for the High-Quality Development of Forest-Based Wel ness Industry:A Case Study of Sichuan Province

The forest-based wellness industry,as a rapidly growing sector that integrates various business forms with extensive coverage and an extended industrial chain,is undergoing rapid development due to the increasing aging population and people's suboptimal health conditions.As a forerunner in developing the forest-based wellness industry,Sichuan province is known for its early development,proactive efforts,diverse models,and significant impact in this industry.It has achieved certain milestones in terms of top-level design,pilot demonstration,standardized guidance,and public awareness campaigns to promote the development of this industry.Therefore,this paper utilizes Sichuan as a case study to systematically summarize and analyze the key practices made by the province in promoting the rapid development of the industry by investigating the development trajectory of the forest-based wellness industry.Additionally,it examines the development trends of this industry from the perspectives of supply,demand,and consumption.Finally,this paper proposes several measures to facilitate the high-quality development of the forest-based wellness industry.These measures encompass nurturing specialized talent in forest-based wellness,enhancing market players'capabilities in this domain,conducting extensive research on technologies that promote this industry,actively seeking support from relevant policies,and promoting integrated development across diverse sectors.

A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface:Starlight High-Temperature Industrial Endoscope

Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.

Application of Large Language Models in Data Governance in the Petroleum and Petrochemical Industry

This paper delves into the current application, challenges, and demands of data governance in the oil and gas industry, as well as methods and implementation strategies to enhance data governance efficacy through the latest technologies such as large language models and algorithm optimization. Through the analysis of the current application status of large language models in intelligent data classification, data quality improvement, knowledge extraction and management, and intelligent search and information retrieval, the main challenges and demands faced by the petroleum and petrochemical industry in data governance have been revealed. A set of data governance optimization solutions tailored for the oil and gas industry has been proposed. These solutions aim to address the current data management challenges in the industry through advanced data processing technologies and algorithmic optimization, enhance the efficiency and quality of data governance, and thereby support the digital transformation and development of the oil and gas industry.

Effects of acid-rock reaction on physical properties during CO_(2)-rich industrial waste gas(CO_(2)-rich IWG)injection in shale reservoirs

"Carbon peaking and carbon neutrality"is an essential national strategy,and the geological storage and utilization of CO_(2)is a hot issue today.However,due to the scarcity of pure CO_(2)gas sources in China and the high cost of CO_(2)capture,CO_(2)-rich industrial waste gas(CO_(2)-rich IWG)is gradually emerging into the public's gaze.CO_(2)has good adsorption properties on shale surfaces,but acidic gases can react with shale,so the mechanism of the CO_(2)-rich IWG-water-shale reaction and the change in reservoir properties will determine the stability of geological storage.Therefore,based on the mineral composition of the Longmaxi Formation shale,this study constructs a thermodynamic equilibrium model of water-rock reactions and simulates the regularity of reactions between CO_(2)-rich IWG and shale minerals.The results indicate that CO_(2)consumed 12%after reaction,and impurity gases in the CO_(2)-rich IWG can be dissolved entirely,thus demonstrating the feasibility of treating IWG through water-rock reactions.Since IWG inhibits the dissolution of CO_(2),the optimal composition of CO_(2)-rich IWG is 95%CO_(2)and 5%IWG when CO_(2)geological storage is the main goal.In contrast,when the main goal is the geological storage of total CO_(2)-rich IWG or impurity gas,the optimal CO_(2)-rich IWG composition is 50%CO_(2)and 50%IWG.In the CO_(2)-rich IWG-water-shale reaction,temperature has less influence on the water-rock reaction,while pressure is the most important parameter.SO2 has the greatest impact on water-rock reaction in gas.For minerals,clay minerals such as illite and montmorillonite had a significant effect on water-rock reaction.The overall reaction is dominated by precipitation and the volume of the rock skeleton has increased by 0.74 cm3,resulting in a decrease in shale porosity,which enhances the stability of CO_(2)geological storage to some extent.During the reaction between CO_(2)-rich IWG-water-shale at simulated temperatures and pressures,precipitation is the main reaction,and shale porosity decreases.However,as the reservoir water content increases,the reaction will first dissolve and then precipitate before dissolving again.When the water content is less than 0.0005 kg or greater than 0.4 kg,it will lead to an increase in reservoir porosity,which ultimately reduces the long-term geological storage stability of CO_(2)-rich IWG.

An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.