Does digitalization mitigate regional inequalities?Evidence from China

Regional inequality significantly influences sustainable development and human well-being.In China,there exists pronounced regional disparities in economic and digital advancements;however,scant research delves into the interplay between them.By analyzing the economic development and digitalization gaps at regional and city levels in China,extending the original Cobb-Douglas production function,this study aims to evaluate the impact of digitalization on China's regional inequality using seemingly unrelated regression.The results indicate a greater emphasis on digital inequality compared to economic disparity,with variable coefficients of 0.59 for GDP per capita and 0.92 for the digitalization index over the past four years.However,GDP per capita demonstrates higher spatial concentration than digitalization.Notably,both disparities have shown a gradual reduction in recent years.The southeastern region of the Hu Huanyong Line exhibits superior levels and rates of economic and digital advancement in contrast to the northwestern region.While digitalization propels economic growth,it yields a nuanced impact on achieving balanced regional development,encompassing both positive and negative facets.Our study highlights that the marginal utility of advancing digitalization is more pronounced in less developed regions,but only if the government invests in the digital infrastructure and education in these areas.This study's methodology can be utilized for subsequent research,and our findings hold the potential to the government's regional investment and policy-making.

Method for evaluation of geological strength index of carbonate cliff rocks:Coupled hyperspectral-digital borehole image technique

The deterioration of unstable rock mass raised interest in evaluating rock mass quality.However,the traditional evaluation method for the geological strength index(GSI)primarily emphasizes the rock structure and characteristics of discontinuities.It ignores the influence of mineral composition and shows a deficiency in assessing the integrity coefficient.In this context,hyperspectral imaging and digital panoramic borehole camera technologies are applied to analyze the mineral content and integrity of rock mass.Based on the carbonate mineral content and fissure area ratio,the strength reduction factor and integrity coefficient are calculated to improve the GSI evaluation method.According to the results of mineral classification and fissure identification,the strength reduction factor and integrity coefficient increase with the depth of rock mass.The rock mass GSI calculated by the improved method is mainly concentrated between 40 and 60,which is close to the calculation results of the traditional method.The GSI error rates obtained by the two methods are mostly less than 10%,indicating the rationality of the hyperspectral-digital borehole image coupled evaluation method.Moreover,the sensitivity of the fissure area ratio(Sr)to GSI is greater than that of the strength reduction factor(a),which means the proposed GSI is suitable for rocks with significant fissure development.The improved method reduces the influence of subjective factors and provides a reliable index for the deterioration evaluation of rock mass.

Lateral earth pressure of granular backfills on retaining walls with expanded polystyrene geofoam inclusions under limited surcharge loading

Existing studies have focused on the behavior of the retaining wall equipped with expanded polystyrene(EPS)geofoam inclusions under semi-infinite surcharge loading rather than limited surcharge loading.In this paper,the failure mode and the earth pressure acting on the rigid retaining wall with EPS geofoam inclusions and granular backfills(henceforth referred to as EPS-wall),under limited surcharge loading are investigated through two-and three-dimensional model tests.The testing results show that different from the sliding of almost all the backfill in the EPS-wall under semi-infinite surcharge loading,only an approximately triangular backfill slides in the wall under limited surcharge loading.The distribution of the lateral earth pressure on the EPS-wall under limited surcharge loading is non-linear,and the distribution changes from the increase of the wall depth to the decrease with the increase of the limited surcharge loading.An approach based on the force equilibrium of a differential element is developed to predict the lateral earth pressure behind the EPS-wall subjected to limited surcharge loading,and its performance was fully validated by the three-dimensional model tests.

From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

Triple digital divide and depressive symptoms among middle-aged and older Chinese adults:a disparity analysis

Background The triple digital divide refers to the lack of internet access,use and knowledge among specific populations.In China,middle-aged and older adults and those living in rural areas or various regions of the country are more likely to have limited internet access and skills and,thus,have less accessibility to internet services.Few longitudinal studies have explored the association between the digital divide and the progression of depressive symptoms among middle-aged and older Chinese adults.Significantly,none of the existing studies have estimated this long-term relationship from a disparity perspective.Aims This study investigates the association between the triple digital divide and depressive symptom trajectories among middle-aged and older adults in China during a 10-year follow-up period from 2011 to 2020.Methods The sample for this secondary analysis comprises 3019 urban and 10427 rural respondents selected from the China Health and Retirement Longitudinal Study baseline survey in 2011.Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale.Employing longitudinal mixed-effects models,this study explored the association between the triple digital divide and depressive symptom trajectories among middle-aged and older Chinese adults by examining gender,rural-urban and regional disparities in this relationship.Results Our findings revealed a significant association between the triple digital divide and increasing trajectories of depressive symptoms,showing significant disparities based on gender,rural-urban dwelling and regional location.Notably,for both male and female participants who resided in urban areas or the central region of the country,their ability to use the internet,coupled with enhanced internet skills and greater access to internet services,was found to have a mitigating effect on the increasing trajectories of depressive symptoms.Conclusions To alleviate some of the confounding influences on the trajectory of depression in middle-aged and older adults,policymakers in China should continue to prioritise the development of internet technology,foster easy access to the internet to ensure it is'elder-friendly',provide internet skill training platforms for this population and broaden access to various internet services appropriate for them.Additionally,the implementation of tailored interventions to address depression,especially targeting the more vulnerable cohorts,such as middle-aged and older women,those residing in rural areas and the western regions,is crucial.Such tailored approaches are essential for addressing the disparities and challenges associated with the triple digital divide.

Note on:“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”

A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”(DOI:https://doi.org/10.1016/j.dt.2018.07.017).Reply to the Note from Li Piani et al is linked to this article.

Digital Twin Modeling Enabled Machine Tool Intelligence:A Review

Machine tools,often referred to as the“mother machines”of the manufacturing industry,are crucial in developing smart manufacturing and are increasingly becoming more intelligent.Digital twin technology can promote machine tool intelligence and has attracted considerable research interest.However,there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence.Herein,digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin.The review then delves into state-of-the-art digital twin modelingenabled machine tool intelligence,examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling.Additionally,it highlights three bottleneck issues facing the field.Considering these problems,the architecture of a digital twin machine tool(DTMT)is proposed,and three key technologies are expounded in detail:Data perception and fusion technology,mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology,and dynamic optimization and collaborative control technology for multilevel parameters.Finally,future research directions for the DTMT are discussed.This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence,making it significant for developing intelligent machine tools.

Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

The rapid development of 5G/6G and AI enables an environment of Internet of Everything(IoE)which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay.However,these massive devices will lead to explosive traffic growth,which in turn cause great burden for the data transmission and content delivery.This challenge can be eased by sinking some critical content from cloud to edge.In this case,how to determine the critical content,where to sink and how to access the content correctly and efficiently become new challenges.This work focuses on establishing a highly efficient content delivery framework in the IoE environment.In particular,the IoE environment is re-constructed as an end-edge-cloud collaborative system,in which the concept of digital twin is applied to promote the collaboration.Based on the digital asset obtained by digital twin from end users,a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention(TPA)enabled Long Short-Term Memory(LSTM)model.Then,the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning(RL)technology.Finally,a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead.The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate,the average throughput,the successful content delivery rate and the average routing overhead.

Digitalization’s Effects on Income Growth and Distribution-Evidence from the CHDES Database

Using the CHDES database,we created the CIDI to comprehensively quantify digital adoption at the individual level and examine digitalization’s impact on individual income.Empirical analysis results indicate that increasing the CIDI by one standard deviation(0.13)improves individual income by 5.93 percentage points,which remains true after a series of endogeneity and robustness tests.In the heterogeneity analysis,we discovered that when digital adoption grows,residents in the countryside and county seats can earn more money through internet business operations and other channels.This helps to decrease the urban-rural income divide,but it has also pushed those digitally skilled into high-paying sectors,widening the income gaps between sectors.In the mechanism analysis,we developed an income function that takes into account the individual level of digitalization to show that digitalization can boost income by increasing working hours or labor participation and adjusting the portfolio of individual material,human,and social capital.In this paper,we have expanded the system for measuring the individual levels of digitalization by offering basic data,research methodology,and policy suggestions for the digital economy’s inclusive development.

A survey on blockchain-enabled federated learning and its prospects with digital twin

Digital Twin(DT)supports real time analysis and provides a reliable simulation platform in the Internet of Things(IoT).The creation and application of DT hinges on amounts of data,which poses pressure on the application of Artificial Intelligence(AI)for DT descriptions and intelligent decision-making.Federated Learning(FL)is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model training.Therefore,DT can benefit by combining with FL,successfully solving the"data island"problem in traditional AI.However,FL still faces serious challenges,such as enduring single-point failures,suffering from poison attacks,lacking effective incentive mechanisms.Before the successful deployment of DT,we should tackle the issues caused by FL.Researchers from industry and academia have recognized the potential of introducing Blockchain Technology(BT)into FL to overcome the challenges faced by FL,where BT acting as a distributed and immutable ledger,can store data in a secure,traceable,and trusted manner.However,to the best of our knowledge,a comprehensive literature review on this topic is still missing.In this paper,we review existing works about blockchain-enabled FL and visualize their prospects with DT.To this end,we first propose evaluation requirements with respect to security,faulttolerance,fairness,efficiency,cost-saving,profitability,and support for heterogeneity.Then,we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation requirements.Finally,we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL,based on which we further propose some directions for future research.

Effects of Desalination Processes on the Water Circulation and Earth System

Desalination is emerging as a promising alternative among various technologies to resolve water shortage. However, desalination requires a sufficient energy and cooling device and therefore poses limitations for its installation and application. In particular, many countries suffering water deficits are economically underdeveloped and cannot afford the technology. As this technology, which changes seawater into freshwater, has little environmental impact, developed countries will need to assist less developed countries to introduce this technology as a humanitarian effort. This will help reduce the number of countries that have experienced difficulty with development.

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”.

Analyzing topics in social media for improving digital twinning based product development

Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product development.Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital spaces.This work mines real-world consumer feedbacks through social media topics,which is significant to product development.We specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a product.The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution.Therefore,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics.To this end,this work combines deep learning and survival analysis to predict the prevalent time of topics.We propose a specialized deep survival model which consists of two modules.The first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network structure.Moreover,a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction.Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

The Impact of Digital Technology on Healthcare Delivery and Patient Outcomes

Digital technology has fundamentally transformed healthcare delivery, exerting profound influence on patient outcomes. This paper delves into the roles played by telemedicine, electronic health records (EHRs), and mobile health applications in augmenting healthcare services. The objective is to scrutinize the ways in which these digital innovations enhance healthcare delivery and patient outcomes, while also identifying the attendant challenges in their adoption. To achieve this, a rigorous literature review encompassing peer-reviewed articles, reports, and case studies that examine the impact of digital technology in healthcare settings was conducted. The findings underscore that digital technology significantly bolsters patient care by enhancing access, operational efficiency, and diagnostic accuracy. Nonetheless, persistent challenges such as safeguarding data privacy, ensuring interoperability across systems, and managing implementation costs continue to pose significant hurdles.

High-Quality Development in China-Arab Digital Economy Cooperation:Opportunities,Challenges and Prospect

As the world undergoes a digital transformation,China-Arab cooperation in the digital economy has become a pivotal area of international engagement.This article explores the opportunities,challenges,and prospects of this cooperation,with a particular focus on the Digital Silk Road initiative under the Belt and Road framework.The study highlights the synergies between China’s technological advancements and the digital strategies of Arab countries,examining progress in key areas such as cooperation mechanisms,digital infrastructure,and cross-border e-commerce.Moreover,it identifies significant challenges,including the digital divide and talent shortages,and proposes strategies to enhance China-Arab digital economy cooperation.These strategies include improving digital infrastructure,growing digital talent cultivation,and developing smart city initiatives alongside expanding cross-border e-commerce,all aimed at fostering sustainable and mutually beneficial outcomes.

The Mediating Effect of Digital Transformation on the Link Between Executive Overconfidence and Corporate Green Innovation

Green technological innovation is crucial for the manufacturing industry’s green transformation and sustainable development.This study examines the impact of executive overconfidence on corporate green innovation,focusing on the internal drivers of corporate innovation and using a sample of Shanghai and Shenzhen A-share listed manufacturing companies from 2013 to 2020.We further examine the mediating role of digital transformation and the moderating role of external attention.The findings indicate that executive overconfidence promotes corporate green technological innovation.Overconfident executives enhance green innovation by accelerating digital transformation.Moreover,external attention from analysts and media positively moderates the relationship between executive overconfidence and corporate green innovation.Heterogeneity analysis reveals that the positive impact of executive overconfidence on green innovation is more significant in non-state-owned enterprises,high-tech firms,and enterprises with lower pollution levels.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

A digital twin model-based approach to cost optimization of residential community microgrids

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency.This study was conducted along two main axes.The first axis focuses on designing a digital twin for a residential community microgrid platform.This phase involves data collection,cleaning,exploration,and interpretation.Moreover,it includes replicating the functionality of the real platform and validating the results.The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid,while maintaining the concept of peer-to-peer energy trading.Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid.Meanwhile,a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit.This is achieved by optimizing battery usage using a cost optimization solution.The proposed solution is validated using the developed digital twin.

Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

Digital Text Document Watermarking Based Tampering Attack Detection via Internet

Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies.The authors’difficulties were tampering detection,authentication,and integrity verification of the digital contents.This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the Internet.The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark information.The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely.Once the watermark is embedded with no damage to the original document,it is then shared with the destination.The watermark extraction process is performed to get the original document securely.The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different measures.The simulation values indicated that the ADMDTW-TAD technique improved performance over other models.