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.

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.

How can technology and efficiency alleviate the dilemma of economic growth and carbon emissions in China's industrial economy? A metafrontier decoupling decomposition analysis

This paper attempts to explore the decoupling relationship and its drivers between industrial economic increase and energy-related CO_(2) emissions(ICE). Firstly, the decoupling relationship was evaluated by Tapio index. Then, based on the DEA meta-frontier theory framework which taking into account the regional and industrial heterogeneity and index decomposition method, the driving factors of decoupling process were explored mainly from the view of technology and efficiency. The results show that during2000-2019, weak decoupling was the primary state. Investment scale expansion was the largest reason hindering decoupling process of industrial increase from ICE. Both energy saving and production technology achieved significant progress, which facilitated the decoupling process. Simultaneously, the energy technology gap and production technology gap among regions have been narrowed, and played a role in promoting decoupling process. On the contrary, both scale economy efficiency and pure technical efficiency have inhibiting effects on decoupling process. The former indicates that the scale economy of China's industry was not conducive to improve energy efficiency and production efficiency, while the latter indicates that resource misallocation problem may exist in both energy market and product market.

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.

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.

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.

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.

Tackling the proton limit under industrial electrochemical CO_(2)reduction by a local proton shuttle

Industrial CO_(2)electroreduction has received tremendous attentions for resolution of the current energy and environmental crisis,but its performance is greatly limited by mass transport at high current density.In this work,an ion‐polymer‐modified gas‐diffusion electrode is used to tackle this proton limit.It is found that gas diffusion electrode‐Nafion shows an impressive performance of 75.2%Faradaic efficiency in multicarbon products at an industrial current density of 1.16 A/cm^(2).Significantly,in‐depth electrochemical characterizations combined with in situ Raman have been used to determine the full workflow of protons,and it is found that HCO_(3)^(−)acts as a proton pool near the reaction environment,and HCO_(3)^(−)and H_(3)O^(+)are local proton donors that interact with the proton shuttle−SO_(3)^(−)from Nafion.With rich proton hopping sites that decrease the activation energy,a“Grotthuss”mechanism for proton transport in the above system has been identified rather than the“Vehicle”mechanism with a higher energy barrier.Therefore,this work could be very useful in terms of the achievement of industrial CO_(2)reduction fundamentally and practically.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

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.

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.

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.

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.

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.

The effects of population aging on industrial structure upgrading:Empirical analysis of provincial and threshold characteristics in China

Global population aging trends are intensifying,presenting multifaceted economic and social challenges for countries worldwide.As the world’s largest developing country,China has entered a phase of extreme demographic aging,posing significant questions about its impact on the ongoing upgrading of industrial structures.How does this demographic shift influence the upgrading of industrial structures,and does technological innovation mitigate or exacerbate this impact?The empirical results indicate that population aging impedes upgrading the industrial structure,while technological innovation positively affects the relationship between the two.Moreover,using technological innovation as a threshold variable,the impact of population aging on industrial structure upgrading evolves in a“gradient”manner from“impediment”to“insignificant”to“promotion”as the technological innovation levels increase.These findings offer practical guidance for tailoring industrial policies to different stages of technological advancement.

Impact of an official accountability audit on industrial structure adjustment:a case study of environmental regulation in China

As a unique environmental regulation in China,the official accountability audit was piloted in 2014.With a focus on prioritizing the ecological environment,officials in pilot districts have implemented economic construction,adjusted industrial structures,and promoted coordinated development between the economy and environment.The effects of implementation have garnered widespread attention from society.However,there is limited research on the impact of an accountability audit on industrial structure adjustments.Using the“Accountability Audit of Officials for Natural Resource Assets(Trial)”released in 2015 as a quasi-natural experiment,this study collected panel data from 279 cities between 2013 and 2017.It then empirically analyzed the impact mechanism and effects of the accountability audit on industrial structure adjustment using the Propensity Score Matching and Difference-in-Differences model.The research findings indicate that the accountability audit directly impacted industrial structure adjustment,promoting the upgrading of the primary industry to the secondary industry and restricting the development of the tertiary industry.In addition,the audit is beneficial for enterprise entry,but not conducive to technological innovation,and has no significant impact on foreign direct investment.This conclusion fills a gap in the existing research and provides valuable insights for policymakers.

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.

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.

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”.

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.