Application Of Technology Research Articles

Is fair carbon mitigation practicable in China? Insights from digital technology innovation and carbon inequality

The uneven matching of distribution and targets in practicing carbon emission reduction has led to emission reduction inequality, while digital technology innovation (DTI) is expected to reduce these gaps. Therefore, this study casts new insights into modeling the nexus between DTI and carbon inequality using China's 286 prefecture-level cities from 2000 to 2021. Remarkably, city-level DTI performance is innovatively proxied by the related patent application volume, while the carbon inequality index within the region is measured using the environmental Gini coefficient. The main findings of this study include: (1) DTI robustly mitigates China's carbon inequality. (2) The effect of DTI is asymmetrically more significant in regions with a higher carbon inequality index. (3) DTI heterogeneously reduces carbon inequality more obviously in the eastern and central regions, and technology innovation in digital product manufacturing and digital technology application industries reduces carbon inequality more significantly. (4) More precisely, DTI strengthens carbon justice through income equalization, spatial agglomeration, and digital inclusion effects. Specifically, DTI significantly reduces income inequality and the digital economy's spatial agglomeration level and enhances digital financial inclusion, thereby reducing carbon inequality. This study also provides the policy implications for China.

Synthesized Polymeric Nanocomposites with Enhanced Optical and Electrical Characteristics Based on SiO2 Nanoparticles for Multifunctional Technological Applications

Synthesized Polymeric Nanocomposites with Enhanced Optical and Electrical Characteristics Based on SiO2 Nanoparticles for Multifunctional Technological Applications

Exploring the Path and Practice of Integrating Information Technology into Classroom Teaching in Vocational Colleges

With the rapid development of information technology, its application in the field of education is becoming increasingly widespread, bringing profound changes to classroom teaching in vocational colleges. The integration of information technology not only innovates teaching methods and enriches teaching resources, but also promotes the updating of educational concepts and the innovation of teaching models. This article aims to explore the path and practical methods of integrating information technology into classroom teaching in vocational colleges, comprehensively analyze the current application status of information technology in teaching, including its specific performance in improving teaching effectiveness, promoting student self-directed learning, and optimizing teacher-student interaction. Meanwhile, this article will propose specific implementation strategies based on the actual needs of vocational colleges, such as strengthening the construction of information technology infrastructure, cultivating teachers' information technology application abilities, and developing information technology teaching resources suitable for vocational education. Finally, this article will also look forward to the future development trend of information technology in classroom teaching in vocational colleges, in order to provide useful reference and inspiration for improving the quality of education and teaching in vocational colleges.

Breaking through the "Tower of Babel": Exploratory Research on the International Communication Practice of Artificial Intelligence Generating News

With the deepening influence of artificial intelligence on the production mode of news and the increasing application of technological innovation in cross-border communication, this study found that artificial intelligence can solve the communication dilemma of language in cross-cultural communication revealed in the myth of the Tower of Babel through language translation and empathetic communication strategies. The literature review section introduces the basic concepts of cross-cultural empathy communication and the practical application of artificial intelligence-generated news in international communication. Multiple case studies were used in the research method, based on the specific practices of major media outlets including Xinhua News Agency, People's Daily, China Central Television, Associated Press, New York Times, and CBS in the application of intelligent technology. The research results indicate that the application of intelligent technology in the news production process has significantly improved work efficiency and promoted innovation in news production models at three levels: technical logic, media logic, and capital logic. The conclusion drawn from this article is that the deep involvement of generative artificial intelligence technology in the news production process can help build a new production mechanism that balances efficiency and responsibility and promotes the comprehensive improvement of efficiency and quality in cross-cultural communication. Future research will continue to focus on the empirical application of intelligent technology in news production, further optimizing news production models, and providing new perspectives and methods for developing the media industry.

Dynamic Ion Correlations and Ion-Pair Lifetimes in Aqueous Alkali Metal Chloride Electrolytes.

Electrolytes are central to many technological applications, as well as life itself. The behavior and properties of electrolytes are often described in terms of ion pairs, whereby ions associate as either contact ion pairs (in which ions are "touching") solvent-separated ion pairs (in which ions' solvent shells overlap) or solvent-solvent-separated ion pairs (in which ions' solvent shells are distinct). However, this paradigm is generally restricted to statistically averaged descriptions of solution structure and ignores temporal behavior. Here we elucidate the time-resolved dynamics of these ion-ion interactions in aqueous metal chloride electrolytes using the partial van Hove correlation function, based on polarizable molecular dynamics simulations. Our results show that the existence and persistence of ion pairs in aqueous metal chloride electrolytes should not be assumed a priori, but in fact are ion specific features of the solution with lifetimes on subpicosecond time scales.

Tailoring the surface topography and crystallinity of EuMnO3 films: A study on sintering effects

We obtained EuMnO3 films through a sol-gel process at varying temperatures, aiming to investigate spatial layout variations influenced by different sintering temperatures. Our results indicate that films adopt an orthorhombic structure when sintered between 800 °C and 850 °C, accompanied by a reduction in nanocrystal size during annealing. Surface analysis reveals that films processed at higher temperatures exhibit increased roughness, negative asymmetry, heightened kurtosis, elevated peak densities, and more distinct peak forms. Furthermore, these films display surfaces with greater anisotropy and lower spatial frequencies compared to those sintered at lower temperatures. Nevertheless, fractal analysis uncovers that both the EuMnO800 and EuMnO850 samples showcase heightened spatial complexity, a more uniform nanotexture, optimal surface porosity, and improved topographic consistency. The findings suggest that films sintered at 800 °C display a spatial arrangement with superior topographic qualities, rendering them potentially valuable for the advancement of semiconductor films derived from perovskite rare-earth oxides for various technological applications.

Research on wireless monitoring system and algorithm for preload force utilizing machine learning and electromechanical impedance

Abstract The root mean square deviation (RMSD) is a common way to measure the electromechanical impedance (EMI) of piezoceramic transducers that are used for traditional preload monitoring. However, due to the impedance signal's high sensitivity to temperature, most current research is carried out under the same temperature conditions to avoid its effects. Even so, it is impossible to ignore the temperature factor in practical engineering, which hinders the application of impedance technology. Therefore, this paper proposes a novel approach for preload monitoring in actual engineering: using a wireless impedance signal acquisition platform to collect long-distance impedance signals and then establishing a predicted model based on machine learning (ML) considering the temperature. The wireless impedance signal acquisition platform includes a smart washer, a piezoelectric impedance wireless sensing device, and the host computer software. This test established a dataset under various operating conditions and developed a machine-learning-based predictive model. After comparing five typical ML algorithms, it was discovered that XGBoost performed better in prediction accuracy and generalization ability. Moreover, the Shapley additive explanation (SHAP) method is utilized to further analyze and interpret the XGBoost model. It indicates that machine learning primarily relies on numerical features (such as the area of each subinterval) to identify the impedance signal and predict the prestress, whereas information features (such as temperature value, peak, etc.) have little influence on the model's output. Finally, the results above demonstrate that the ML-based models can predict the preload at different temperatures, effectively reducing temperature interference.

A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

Research on remote reference denoising method based on non-coaxial and non-coplanar tunnel NMR detection

Abstract The limited space within the tunnel constrains the size of the antenna for NMR detection, thereby significantly impacting the signal-to-noise ratio of NMR signals. Insufficient signal-to-noise ratio data poses substantial challenges to obtaining reliable NMR signals. The paper presents a novel approach to address the challenge of strong background noise in tunnel environments and low signal-to-noise ratio (SNR) data by incorporating the ground multi-channel remote reference denoising method into tunnel NMR advance detection. Specifically designed for narrow tunnels, a multichannel non-coaxial and non-coplanar remote reference denoising method is proposed. Firstly, the effectiveness of the non-coaxial, non-coplanar remote reference denoising method is verified in the laboratory environment. Secondly, the correlation between the detector antenna and the reference antenna is calculated theoretically to ensure the significant correlation between the detector antenna and the reference antenna. Finally, two processing methods of reference denoising and non-reference denoising are carried out respectively by combining the tunnel detection data. By comparing the inversion results and the engineering construction results, the effectiveness of non-coaxial and non-coplanar remote reference denoising methods in tunnel NMR detection is proved, which provides relevant research support for expanding the application of tunnel NMR detection technology.

Retraction Note: Application and functional simulation of data mining technology in Hadoop Cloud platform based on improved algorithm

Retraction Note: Application and functional simulation of data mining technology in Hadoop Cloud platform based on improved algorithm

Hotspot analysis for integrated multi-infrastructure asset management

ABSTRACT Urban infrastructure, important for societal functioning, faces challenges from aging assets and increasing service demands. Traditional asset management practices, often conducted in silos, fail to address the interconnected nature of these systems, leading to inefficiencies and heightened system failure risks. This article combines the spatial and temporal aspects of sewer, water, and road networks to facilitate integrated interventions and enable informed decision-making among diverse stakeholders. The outcome of this research is the creation of interactive hotspot maps on a unified platform, highlighting potential areas for integrated intervention across different infrastructures. To enhance the potential for collaboration in integrated interventions, flexibility in intervention planning was incorporated. With increased flexibility in intervention decisions, the potential for collaboration also increased. For the case study, introducing a 5-year intervention flexibility increased the number of collaborative projects between sewer, water, and roads from 0 to 18. The maps can also indicate areas where the application of trenchless technologies are justifiable. Other important information on asset characteristics for the decision-makers, including age, inspection, deterioration, and other relevant spatial and temporal details can also be obtained from the maps. The presented methodology and findings provide practical solution for utilities to manage urban infrastructure networks more efficiently.

Digital Competency Model Development for Public High School Teachers in Nanning

Background and Aims: With the rapid development and popularization of technology, the application of digital technology in education is increasingly widespread, and the digital competency of teachers has become the key to promoting education modernization and innovation. The objectives of this study include: 1) To study the situation of digital competency of public high school teachers in Nanning. 2) To develop a model for improving the digital competency of public high school teachers in Nanning. 3) To evaluate the model for improving the digital competency of public high school teachers in Nanning. Methodology: This study is based on the latest research results and widely adopts the opinions of experts in the field of digital education by employing the Delphi method and the focus group method. Results: The digital competency model for teachers in public high schools in Nanning was successfully developed. Important measures and suggestions have been provided to promote the improvement of the digital competency of teachers in public high schools in Nanning. Conclusion: An important development in educational practices is indicated by the development of a digital competency model for teachers in public high schools in Nanning, which was completed with success. The strategies and recommendations offered are meant to improve educators' digital literacy, promoting efficient instruction in the digital era and improving the educational experiences of students.

Evolution of Industrial Robots from the Perspective of the Metaverse: Integration of Virtual and Physical Realities and Human–Robot Collaboration

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making.

Retraction Note: Application and effect simulation of image recognition technology based on machine vision feature parameters in art teaching

Retraction Note: Application and effect simulation of image recognition technology based on machine vision feature parameters in art teaching

Personal Information Protection in the Application of Facial Recognition Technology in Public Places - Taking the "First Facial Recognition Case" as an Example

In recent years, the widespread use of facial recognition technology in public places has raised challenges for personal information protection. By analyzing the "First Facial Recognition Case," this article reveals practical challenges under the current legal framework. Significant risks exist in the collection, use, and storage of information, particularly regarding the principles of legality, necessity, and informed consent. The article explores the ambiguity of these principles in legal applications and proposes improvements, including better evaluation of legality and necessity, introducing a tiered consent mechanism, and constructing a dynamic consent mechanism to enhance compliance and protect personal information security.

Tunable charge transport properties in non-stoichiometric SrIrO3 thin films

Delving into the intricate interplay between spin-orbit coupling and Coulomb correlations in strongly correlated oxides, particularly perovskite compounds, has unveiled a rich landscape of exotic phenomena ranging from unconventional superconductivity to the emergence of topological phases. In this study, we have employed pulsed laser deposition technique to grow SrIrO3 (SIO) thin films on SrTiO3 substrates, systematically varying the oxygen content during the post-deposition annealing. X-ray photoelectron spectroscopy (XPS) provided insights into the stoichiometry and spin-orbit splitting energy of Iridium within the SIO film, while high-resolution x-ray studies meticulously examined the structural integrity of the thin films. Remarkably, our findings indicate a decrease in the metallicity of SIO thin films with reduced annealing O2 partial pressure. Furthermore, we carried out magneto-transport studies on the SIO thin films, the results revealed intriguing insights into spin transport as a function of oxygen content. The tunability of the electronic band structure of SIO films with varying oxygen vacancy is correlated with the density functional theory calculations. Our findings elucidate the intricate mechanisms dictating spin transport properties in SIO thin films, offering invaluable guidance for the design and optimization of spintronic devices based on complex oxide materials. Notably, the ability to tune bandwidth by varying post-annealing oxygen partial pressure in iridate-based spintronic materials holds significant promise for advancing technological applications in the spintronics domain.

Electrostatic Force-Assisted Transfer of Flexible Silicon Photodetector Focal Plane Arrays for Image Sensors.

Flexible photodetectors are pivotal in contemporary optoelectronic technology applications, such as data reception and image sensing, yet their performance and yield are often hindered by the challenge of heterogeneous integration between photoactive materials and flexible substrates. Here, we showcase the potential of an electrostatic force-assisted transfer printing technique for integrating Si PIN photodiodes onto flexible substrates. This clean and dry process eliminates the need for chemical etchants, making it a highly desirable method for manufacturing high-performance flexible photodetector arrays, expanding their widespread applications in electronic eyes, robotics, and human-machine interaction. As a demonstration, a 5 × 5 flexible Si photodetector focal plane array is constructed for imaging sensors and shaped into a convex semicylindrical form to achieve a π field of view with long-term mechanical and thermal stability. Such an approach provides a high yield rate and consistent performance, with the single photodetector demonstrating exceptional characteristics, including a responsivity of 0.61 A/W, a response speed of 39.77 MHz, a linear dynamic range of 108.53 dB, and a specific detectivity of 2.75 × 1012 Jones at an applied voltage of -3 V at 940 nm.

Transforming User Experience (UX) through Artificial Intelligence (AI) in interactive media design

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance. Keywords: Artificial Intelligence Technology, Digital Media Interaction Design, Generate Adversarial Network, User Experience.

Self-Driven Gas Spreading on Mesh Surfaces for Regeneration of Underwater Superhydrophobicity.

Underwater superhydrophobic surfaces stand as a promising frontier in technological applications such as drag reduction, antifouling, and anticorrosion. Unfortunately, the air film, known as the plastron, on these surfaces tends to be unstable. To address this problem, active approaches have been designed to preserve or restore plastrons. In this work, a self-driven gas spreading superhydrophobic mesh (SHM) surface is designed to facilitate recovery of the plastron. The immersed SHM can be "wetted" by gas, even when the plastron is removed. We demonstrate that the injected gas can spread spontaneously along the SHM over a large area, which greatly simplifies the plastron replenishment process. By incorporating a locally coated gas-producing layer, we achieve rapid in situ plastron recovery and long-term immersion stability, extending the plastron lifespan by at least 48 times. We also provide a framework for designing an SHM with suitable structural dimensions for gas spreading. Furthermore, an SHM with asymmetric structural dimensions enables unidirectional gas transport by the capillary pressure difference. This SHM surface shows excellent drag reduction properties (37.2%) and has a high slip recovery coefficient (73.4%) after plastron loss. This facile and scalable method is expected to broaden the range of potential applications involving nonwetting-related fields.

Advances in Plasmonic Photonic Crystal Fiber Biosensors: Exploring Innovative Materials for Bioimaging and Environmental Monitoring

AbstractThis review paper comprehensively analyzes recent advancements in optical fiber‐based biosensors, focusing on conventional fiber and photonic crystal structures. This paper overviews the significant applications of optical fiber biosensors, including bioimaging, quality analysis, food safety, and field environment monitoring, setting the stage for subsequent discussions. The primary objective of the review is to systematically evaluate recent literature concerning optical fiber‐based biosensors, emphasizing their sensitivities and resolutions. The second section explores integrating plasmonic materials such as graphene, TDMC, germanium, black phosphorus, and silicon within optical fiber biosensors, elucidating their roles in enhancing sensitivity and resolution in biosensing applications. A detailed examination of photonic crystal fibers (PCF) follows, categorizing them into internally and externally metal film‐coated biosensors, highlighting their distinct advantages and limitations. Comparative analyses in two tables delineate the performance and sensitivity of optical fiber‐based biosensors, mainly focusing on different coating strategies. The final section of the review discusses emerging trends and applications in optical fiber biosensing technologies, underscoring their potential to transform biomedical and environmental monitoring fields. By synthesizing recent developments and challenges, this review aims to offer researchers and practitioners a comprehensive understanding of optical fiber‐based biosensors, facilitating informed decision‐making and driving further advancements in the field.