Digitalization Enables Cultural,Commercial and Tourism Industries to Explore the Global Market

The recent 2024 National Tour ism Development Conference has clearly pointed out that China’s tourism industry has grown into the world’s largest domestic tourism market and has become an important destination in the international tourism sector,marking the rise of the tourism industry as a strategic pillar industry for the country.

Development Level and Dynamic Analysis of the Integration of Three Rural Industries in the Context of Rural Revitalization

As a key measure to comprehensively promote the strategy of rural revitalization,especially in the field of industrial prosperity,the integration of rural three industries is of great strategic significance.In this study,entropy method and TOPSIS method were employed to calculate the comprehensive evaluation index of integration of three industries and the ideal development value respectively.The development status of integration of three rural industries was systematically evaluated,and the development trend of different regions was compared and analyzed.The results indicate that the development index of integration of three industries showed a steady growth trend,among which the development level of Jiangsu Province ranked first,followed by Hubei Province,while the development level of Shaanxi Province was relatively low.When analyzing the Level I indicators of integration of three rural industries,the contribution of industrial integration behavior exceeded the performance of industrial integration.Among the behavioral indicators of industrial integration,the weight of agricultural multi-functionality and service integration was relatively large,which plays a significant role in promoting the development of integration of three rural industries.The scores and growth rates of Jiangsu Province in increasing farmers income,increasing agricultural production and rural economic development were higher than those of other regions,while Shaanxi Province still had a certain gap in rural industrial integration indicators compared with Jiangsu Province and Hubei Province.In view of this,we came up with some strategic recommendations for further promoting the development of integration of three rural industries.

Science and Technology Promotion Policies of Strategic Emerging Industries in Anhui Province

In recent years,based on advantages of industry,market,science and technology and other development environment,strategic emerging industries in Anhui Province are developing rapidly,and emerging industries such as new energy,new materials,new generation of information technology occupy an important market share in China and even the world.However,there are still a number of problems in the process of development,and the policy support has a greater impact.In this paper,the development status of strategic emerging industries in Anhui Province was discussed firstly,and then the challenges and problems of the development was discussed.Finally,some science and technology promotion policies of strategic emerging industries in Anhui Province were proposed.

Is“Intelligence”More Capable of Promoting the Development of the Tertiary Industry?-Analysis of the Impact of Artificial Intelligence on Different Categories of Industries

In the new scientific and technological revolution round,artificial intelligence(AI)technology has become a key leading force for industrial change.Research shows that AI not only promoted technical transformation and industry upgrades but also played a significant role in the rapid development of emerging industries.Based on the installed number of industrial robots and the industrial data by the National Bureau of Statistics,this study establishes a theoretical framework with the econometric model and compares the impact of AI on different categories of industries through empirical analysis.Our results show that AI not only promotes economic growth but also plays a key role in promoting the tertiary industry.Hence,optimization of industrial structure and economic upgrade can be induced.

Evolution of Handicraft Industries in the Textile Sector in Saudi Arabia: A Comprehensive Analysis of Trends and Innovations

This review explores the evolution of the textile handicraft industry in Saudi Arabia, emphasizing its cultural and economic significance. The study highlights the transition from traditional practices to modern innovations and examines the impact of globalization and technological advancements on the industry. Key innovations are discussed, demonstrating their role in enhancing textile production while preserving cultural heritage. Major challenges, such as competition from industrial textiles and the need for sustainable practices, are identified. Opportunities for growth are explored, including leveraging tourism and international markets to promote Saudi handicrafts. The social and cultural impacts of the sector are underscored, particularly in sustaining community traditions and providing economic opportunities for artisans. Strategic recommendations for supporting and advancing the industry are offered, ensuring its continued relevance and sustainability in a rapidly changing global market. This analysis provides a robust framework for understanding the current state and future potential of Saudi Arabia’s textile handicraft industry.

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.

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.

Spatial Evolution and Locational Determinants of High-tech Industries in Beijing

Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a more efficient urban spatial structure.

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.

Re-measuring Status of China＇s High-Tech Industries in International Division of Labor： Applying a Non-Competitive Input-Output Model

Based on a refined ＂non-competitive input-output model,＂ this paper proposes a new framework for analyzing the status of a country＇s high-tech industries in the international division of labor, i.e. calculates the index of＂ weighted value-added productivity ＂ by compiling non-competitive input-output tables which distinguish high-tech industries from traditional industries. The new method effectively avoids ＂statistical illusion＂ which stems from a biased focus on gross exports under intra-product specialization. The empirical study shows that since 1995, the status of China＇s high-tech industries has grown quickly as a result of enhanced labor productivity, but still lags behind those of major developed countries. In addition, the study also suggests that the status of China＇s high-tech industries has been over-estimated using the traditional gross export statistical method.

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.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

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.

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.

Spatio-temporal evolution of the coordinated development of high-tech industry agglomeration and resource-environmental carrying capacity in the Yellow River Basin

Under the background of new infrastructure,the Yellow River Basin’s superior growth cannot be separated originating with the synergistic effect of scientific and technological inventiveness and ecological civilization construction.In light of the coupling coordination analysis of the coordination effect of provincial high-tech industry agglomeration and resource carrying capacity in the Yellow River Basin from 2009 to 2021,The evolution of the geographical and temporal pattern of development was investigated using the Moran index and kernel density estimation.The results show that the agglomeration of high-tech industries in the Yellow River Basin presents a development trend of seek improvement in stability,and there is a good coupling and coordination throughout the progression of scientific and technological innovation and the loading capacity of the resource,from the viewpoint of a time series.From the perspective of spatial pattern distribution,the whole basin aims at the lower reaches,accelerates the optimization of digital industry and promotes Yellow River Basin development of superior quality through innovation support and increase of input,and based on policy guidance.

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.

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.

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.

AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

This comparative review explores the dynamic and evolving landscape of artificial intelligence(AI)-powered innovations within high-tech research and development(R&D).It delves into both theoreticalmodels and practical applications across a broad range of industries,including biotechnology,automotive,aerospace,and telecom-munications.By examining critical advancements in AI algorithms,machine learning,deep learning models,simulations,and predictive analytics,the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies.The review integrates both qualitative and quantitative data derived from academic studies,industry reports,and real-world case studies to showcase the tangible impacts of AI on product innovation,process optimization,and strategic decision-making.Notably,it discusses the challenges of integrating AI within complex industrial systems,such as ethical concerns,technical limitations,and the need for regulatory oversight.The findings reveal a mixed landscape where AI has significantly accelerated R&D processes,reduced costs,and enabled more precise simulations and predictions,but also highlighted gaps in knowledge transfer,skills adaptation,and cross-industry standardization.By bridging the gap between AI theory and practice,the review offers insights into the effectiveness,successes,and obstacles faced by organizations as they implement AI-driven solutions.Concluding with a forward-looking perspective,the review identifies emerging trends,future challenges,and promising opportunities inAI-poweredR&D,such as the rise of autonomous systems,AI-driven drug discovery,and sustainable energy solutions.It offers a holistic understanding of how AI is shaping the future of technological innovation and provides actionable insights for researchers,engineers,and policymakers involved in high-tech Research and Development(R&D).