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.

Analysis on Technical Structure and Competitiveness of ICT Manufacturing Industries

In order to analyze the technical structure and international comparative advantage of the information and communication technology(ICT)manufacturing industry,a complete set of ICT manufacturing product categories has been constructed by matching National Economical Industry Classification(GB/T4754-2017)with Harmonized System(HS)Codes,based on the relevant definitions in International Standard Industrial Classification(ISIC).The proposed definition overcomes inherent defects such as inaccurate scopes,lagging data and rough categories,which are characterized by commonly utilized product-level based classification approaches.Within the given framework,this paper has designed the technology content related indicators from the perspective of production distribution,and divided ICT product categories into high-end,medium-end and lowend manufacturing classifications according to respective global shares.Then,we have calculated international market shares(IMS),revealed comparative advantages(RCA),and market penetration rates(MPR)of ICT manufacturing exports for major economies from 2010 to 2021.Finally,development characterizations of ICT manufacturing industries for China’s Mainland are analyzed,and several practical suggestions are provided.

Government Role in Fostering Emerging Technological Industries:A New Perspective for Industrial Policy Research

Mainstream industrial policy research cannot fully explain how government interventions have helped China’s mobile communications industry catch up with and overtake those of advanced nations.China’s mobile communications industry made breakthroughs in 3G,caught up with advanced nations in 4G,and gained a leadership position in 5G due to the implementation and improvement of a strategy of industrial competition that accommodates mainstream standards and prioritizes the mid-band spectrum based on the integrated“technology,standard and industry”deployment system and swift decision-making.The introduction of a perspective of a strategy of industrial competition may supplement industrial policy research in the following ways:First,when the concerted actions of numerous innovators are important for industrial competition performance,an effective strategy of industrial competition can be devised and overall coordinated by the government provided that is compatible with the catch-up development of emerging technological industries.Second,an industrial policy becomes effective when it is complementary with the strategic factors for long-term industrial performance such as the strategy of industrial competition and avoids serious disruptions to market-based mechanisms.

Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

Customer retention is one of the challenging issues in different business sectors,and variousfirms utilize customer churn prediction(CCP)process to retain existing customers.Because of the direct impact on the company revenues,particularly in the telecommunication sector,firms are needed to design effective CCP models.The recent advances in machine learning(ML)and deep learning(DL)models enable researchers to introduce accurate CCP models in the telecom-munication sector.CCP can be considered as a classification problem,which aims to classify the customer into churners and non-churners.With this motivation,this article focuses on designing an arithmetic optimization algorithm(AOA)with stacked bidirectional long short-term memory(SBLSTM)model for CCP.The proposed AOA-SBLSTM model intends to proficiently forecast the occurrence of CC in the telecommunication industry.Initially,the AOA-SBLSTM model per-forms pre-processing to transform the original data into a useful format.Besides,the SBLSTM model is employed to categorize data into churners and non-chur-ners.To improve the CCP outcomes of the SBLSTM model,an optimal hyper-parameter tuning process using AOA is developed.A widespread simulation analysis of the AOA-SBLSTM model is tested using a benchmark dataset with 3333 samples and 21 features.The experimental outcomes reported the promising performance of the AOA-SBLSTM model over the recent approaches.

Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently.The pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient costs.Blockchain(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake circulation.The designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DSCM)method.With this motivation,the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme(BCODL-DSCMRS)for Pharmaceutical Industries.Firstly,Hyperledger fabric is used for DSC management,enabling effective tracking processes in the smart pharmaceutical industry.In addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and consumers.The spotted hyena optimizer(SHO)algorithm is used to optimize the performance of the HDBN model.The design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the work.The presented model is tested on the UCI repository’s open-access drug reviews database.

Carbon emission efficiency of forest processing industries in China

This paper uses an input-output table of China's provinces(2007-2016) to measure carbon emissions of these industries.It employs a Malmquist-Luenberger(ML) index with expected and undesired outputs,and an absolute β convergence and a conditional β convergence model,to conduct an in-depth analysis of dynamic changes and spatial convergence.Carbon emission efficiency of forest processing industries in 25 regions,including Shanghai,Chongqing,Zhejiang,and Jiangsu are increasing,whereas those of Tianjin,Liaoning,Heilongjiang,and Tibet are decreasing.The main contributing factors of carbon emission efficiency in three major regions vary over time.Further,carbon emission efficiency in the eastern,central,and western regions all have absolute β convergence and conditional β convergence,indicating that different regions are developing toward their own goals and industry,yet regions with lower efficiency are catching up with those where with more efficient strategies in place.Finally,this paper proposes according recommendations.

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.

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.

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction:A Case Study of Cities in the Pearl River Basin,China

China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.

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”.

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.

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.

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.

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.

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.

Use of Component Integration Services in Multidatabase Systems: A Feasible Solution for Integrating Academic Institutions or Commercial Industries

The book chapter is an extended version of the research paper entitled “Use of Component Integration Services in Multidatabase Systems”, which is presented and published by the 13th ISITA, the National Conference of Recent Trends in Mathematical and Computer Sciences, T.M.B. University, Bhagalpur, India, January 3-4, 2015. Information is widely distributed across many remote, distributed, and autonomous databases (local component databases) in heterogeneous formats. The integration of heterogeneous remote databases is a difficult task, and it has already been addressed by several projects to certain extents. In this chapter, we have discussed how to integrate heterogeneous distributed local relational databases because of their simplicity, excellent security, performance, power, flexibility, data independence, support for new hardware technologies, and spread across the globe. We have also discussed how to constitute a global conceptual schema in the multidatabase system using Sybase Adaptive Server Enterprise’s Component Integration Services (CIS) and OmniConnect. This is feasible for higher education institutions and commercial industries as well. Considering the higher educational institutions, the CIS will improve IT integration for educational institutions with their subsidiaries or with other institutions within the country and abroad in terms of educational management, teaching, learning, and research, including promoting international students’ academic integration, collaboration, and governance. This will prove an innovative strategy to support the modernization and large expansion of academic institutions. This will be considered IT-institutional alignment within a higher education context. This will also support achieving one of the sustainable development goals set by the United Nations: “Goal 4: ensure inclusive and quality education for all and promote lifelong learning”. However, the process of IT integration into higher educational institutions must be thoroughly evaluated, identifying the vital data access points. In this chapter, Section 1 provides an introduction, including the evolution of various database systems, data models, and the emergence of multidatabase systems and their importance. Section 2 discusses component integration services (CIS), OmniConnect and considering heterogeneous relational distributed local databases from the perspective of academics, Section 3 discusses the Sybase Adaptive Server Enterprise (ASE), Section 4 discusses the role of component integration services and OmniConnect of Sybase ASE under the Multidatabase System, Section 5 shows the database architectural framework, Section 6 provides an implementation overview of the global conceptual schema in the multidatabase system, Section 7 discusses query processing in the CIS, and finally, Section 8 concludes the chapter. The chapter will help our students a lot, as we have discussed well the evolution of databases and data models and the emergence of multidatabases. Since some additional useful information is cited, the source of information for each citation is properly mentioned in the references column.

Optimization of Agricultural Industrial Structure in Changping District of Beijing Based on Grey Relational Analysis

In the economic development of Beijing,although the share of the total amount of agricultural industry in the overall economy is relatively low,it has an important impact on the daily life of residents,social stability and the development of other industries.Changping District,as an important agricultural production base of Beijing,its agricultural development has an indispensable strategic significance for the stability and growth of the entire regional economy.Therefore,it is very important to study the structure of agricultural industry in Changping District.Based on the detailed analysis of the agricultural industrial structure of Changping District,this paper uses the grey relation theory to analyze the different industries in the agricultural industrial structure of Changping District,including planting,forestry,animal husbandry,fishery and agricultural,forestry,service industries,in order to reveal the impact of these industries on the agricultural industrial structure of Changping District.Through this study,it comes up with specific and feasible suggestions for the optimization of agricultural industrial structure in Changping District,and provides valuable reference for the agricultural development of other areas in Beijing.