Key Technologies Research Articles

Key technology for digital twins in the architecture, engineering, and construction industry: new advances in point cloud semantic segmentation algorithms for buildings

Key technology for digital twins in the architecture, engineering, and construction industry: new advances in point cloud semantic segmentation algorithms for buildings

E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

Discrete modulation continuous variable quantum key distribution under fast fading channel

Abstract After decades of development, continuous variable quantum key distribution technology still cannot meet people's needs for secure communication over long distances. Free-space quantum communication provides a new way for secure communication over long distances. However, because the communication connected in free space is inevitably affected by atmospheric turbulence, the transmission rate of the channel changes rapidly according to the probability distribution, making it difficult for the communication personnel to determine the transmission rate of the channel. At the same time, discrete modulated continuously variable quantum key Distribution protocol (DM-CVQKD) has certain advantages over Gauss modulated continuously variable quantum key Distribution protocol (GM-CVQKD) in the communication distance. Therefore, this paper analyzes the key rate of DM-CVQKD in this case (that is, in the fast attenuation channel), proves that it can resist the collective attack, and observes the relationship between the parameters and the key rate in the fast attenuation channel by numerical simulation and control variables. Finally, through the analysis of numerical simulation results, it is found that DM-CVQKD can still maintain a high key rate under certain conditions, which proves that long-distance free space quantum communication is feasible under certain conditions. At the same time, it was found that the performance of DM-CVQKD was affected by the extreme case where Eve completely controlled the instantaneous transmittance.

Empirical Research on the Impact of Technological Innovation on New Energy Vehicle Sales

In the context of global carbon peak and carbon neutrality goals, researching the driving forces and influencing factors behind the growth in sales of new energy vehicles (NEVs) is particularly urgent and crucial. Although the academic community has extensively explored various factors affecting NEV sales, technological innovation, as the core engine driving industry progress, is yet to receive sufficient and in-depth exploration regarding its potential profound impact on sales. Therefore, this study thoroughly analyzes the mechanism by which technological innovation influences sales, supplementing the existing literature, exploring sustainable industry development, and guiding the optimization of business strategies and precise government policies. This research employs the quality of NEV technology patents as a proxy variable for technological innovation, and conducts an empirical analysis using China’s NEV sales data from 2015 to 2022. The results of the study show the following: (1) the quality of technological innovation significantly contributes to the growth of new energy vehicle sales. This conclusion still holds when the administrative protection of intellectual property rights and the government’s public innovation environment indicators are used as instrumental variables of technological innovation quality. (2) Technological innovations in different links of the new energy vehicle industry chain have a positive impact on sales, especially those in the midstream battery, motor, and downstream charging services. This shows that in the critical period of new energy vehicle development, not only is the breakthrough of key technologies crucial, but also, non-critical technologies can promote sales growth by improving user experience and product performance. (3) Compared with plug-in hybrid electric vehicles (PHEVs), the overall technological innovation of new energy vehicles (NEVs) has a more significant effect on the sales of battery electric vehicles (BEVs). (4) In addition, the study finds that there is no significant difference between high-quality and low-quality technological innovations on the promotion of new energy vehicle sales, which indicates that the market values the overall effect of technological innovations more than the mere level of quality. The study of the impact of technological innovation on sales in different branches of the new energy industry chain can help to clarify the direction of technological innovation, optimize resource allocation, promote the synergistic development of the industry chain, enhance market competitiveness, and provide a scientific basis for policy formulation.

The Role of the Working Fluid and Non-Ideal Thermodynamic Effects on Performance of Gas Lubricated Bearings

Abstract Small-scale turbomachinery operating at high rotational speed is a key technology for increasing the power density of energy and propulsion systems. A notable example is the turbine of an organic Rankine cycle turbogenerator for thermal recuperation from prime engines and industrial processes. Such systems typically operate with organic compounds characterized by complex molecular structures, to allow the design of efficient fluid machinery and flexibility in matching the heat source and sink temperature profiles. Gas bearings are considered advantageous compared to traditional oil-lubricated rolling element bearings for supporting the turbine rotor, enabling greater machine compactness and reduced complexity, and to avoid contamination of the working fluid. In certain operating conditions, however, the lubricant of the bearing is in thermodynamic states near the saturated vapor line or in the vicinity of the fluid critical point whereby non-ideal effects are relevant and may affect bearing performance. This work investigates the physics of thin film flows in gas bearings operating with fluids made by complex molecules. The influence of non-ideal thermodynamic effects on gas bearing performance is discussed by analysis of the fluid bulk modulus. Reduced values of the non-dimensional bulk modulus near the critical point or saturated vapor line decrease bearing performance. The main parameter characterizing the influence of molecular complexity on bearing performance is shown to be the acentric factor. For complex fluids with large acentric factors, the impact of non-ideal thermodynamic effects on non-dimensional bearing load capacity and rotor-dynamic characteristics is less pronounced.

Preparation of glass fiber-reinforced polyphenylene sulfide/magnesium hybrid based on micro-arc oxidation by hot pressing molding

ABSTRACT The construction of surface structures and joining technology has become a key technology for the preparation of polymer-metal hybrid(PMH) materials in recent years. The magnesium alloy surface was subjected to micro-arc oxidation(MAO) to create a micro-nanopore structure, and the Glass fiber-reinforced polyphenylene sulfide/magnesium alloy(GFRPPS/Mg) hybrid joints were prepared by hot pressing molding. The joining mechanism of the GFRPPS/Mg hybrid joints was explored, and the interfacial disruption forms, crystalline properties, embedding depths, and interfacial chemistry of the GFRPPS/Mg hybrids were investigated at different hot pressing temperatures. The results demonstrate that the mechanical strength of the GFRPPS/Mg hybrid joints is a consequence of the interplay between micromechanical interlocking and chemical bonding, and the optimal tensile shear strength is 18.51 MPa. The tensile shear failure of the hybrids can be attributed to a combination of cohesive and interfacial failure. The crystalline properties of the failed surface of GFRPPS were also analyzed by X-ray diffraction (XRD), which revealed that the crystallinity also affects the mechanical properties of the GFRPPS/Mg hybrid joints.

Online Self-Learning-Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process coupled with the rotary hearth furnace system’s reliance on human labor results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for proportioning raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the local outlier factor (LOF) method to detect any abnormal values, using Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience demonstrates good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and significantly enhances batching efficiency and product quality.

PQSF: post-quantum secure privacy-preserving federated learning

In federated learning, secret sharing is a key technology to maintain the privacy of participants’ local models. Moreover, with the rapid development of quantum computers, existing federated learning privacy protection schemes based on secret sharing will no longer be able to guarantee the data security of participants in the post-quantum era. In addition, existing privacy protection methods have the problem of high communication and computational overhead. Although the multi-stage secret sharing scheme proposed by Pilaram et al. is one of the effective solutions to the above problems, existing studies have proven the privacy leakage risk of this scheme. This paper firstly designs a new lattice-based multi-stage secret sharing scheme Improved-Pilaram to solve the security problem, which allows participants to use public vectors to reconstruct different secret values without changing the secret sharing. Based on Improved-Pilaram, this article proposes a post-quantum secure federated learning scheme PQSF. PQSF uses double masking technology to encrypt model parameters and achieves mask reconstruction through secret sharing. Since Improved-Pilaram is multi-stage, participants do not need to update their local secret shares frequently during training. Analysis and experimental results show that the PQSF proposed in this paper reduces the communication complexity between participants and reduces the computational overhead by about 20% compared with existing solutions.

Realizing Tunable Long Persistent Luminescent in Novel Cu2+‐Doped NaGaO2 for Multi‐Level Information Storage and Encryption

AbstractAnti‐counterfeiting and encryption are key technologies for information transmission in modern society. However, most optical materials offer only a single fixed response mode, limiting their security level in advanced anti‐counterfeiting applications. Exploring efficient and tunable long persistent luminescent (LPL) phosphors is urgently demanded and highly meaningful. In this work, a dual‐site occupancy strategy is innovatively reported via Li+ doped NaGaO2: Cu2+ (NGO: Cu2+/Li+) phosphors for enhancing LPL properties. To be specific, Cu2+ initially occupies both Na and Ga sites in NaGaO2, producing orange–yellow LPL at 585 nm and near‐infrared (NIR) emission at 712 nm, respectively. Furthermore, the introduction of Li+ will occupy the Na+ sites, attenuating the NIR emission and increasing the defect density of the oxygen‐deficient states, which results in enhanced LPL intensity and prolonged afterglow time. Significantly, the obtained NGO:Cu2+/Li+ exhibits multi‐emission modes with dynamic change of LPL time (10–20 min). More importantly, the NGO: Cu2+/Li+ has great potential applications in multi‐level information storage and encryption.

Advancements in Optical Resonator Stability: Principles, Technologies, and Applications

This paper provides an overview of the study of optical resonant cavity stability, focusing on the relevant principles, key technological advances, and applications of optical resonant cavities in a variety of high-precision measurement techniques and modern science and technology. Firstly, the vibration characteristics, thermal noise, and temperature characteristics of the reference cavity are presented. Subsequently, the report extensively discusses the advances in key technologies such as mechanical vibration isolation, thermal noise control, and resistance to temperature fluctuations. These advances not only contribute to the development of theory but also provide innovative solutions for practical applications. Typical applications of optical cavities in areas such as laser gyroscopes, high-precision measurements, and gravitational wave detection are also discussed. Future research directions are envisioned, emphasising the importance of novel material applications, advanced vibration isolation technologies, intelligent temperature control systems, multifunctional integrated optical resonator design, and deepening theoretical models and numerical simulations.

Visual Navigation Systems for Maritime Smart Ships: A Survey

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships.

Steel Plate Cutting Path Optimisation Problem Based on Euler's Theorem and Greedy Algorithm

Steel plate cutting as one of the key technologies in the field of machining, planning the optimal path and reducing the air distance can improve the cutting efficiency of the steel plate, and improving the cutting efficiency is the most critical step in the machining process. In this paper, we analyze the main reasons for the generation of empty paths and make the assumption of cutting the whole part at once while cutting the part. On this basis, Euler's theorem and greedy algorithm are used to find the proximal point of the cutting path from the outer contour to the inner contour, and to plan the optimal path and the resulting shortest air distance for a given cutting of the outer contour of the serrated steel plate as well as the cutting of different shapes of parts in the inner part of the serrated steel plate. The resulting shortest air travel distance is 31.5536 by matlab.

Two-stage optimal PMU placement for distribution systems

The rapid growth of distributed energy resources has highlighted the importance of monitoring the system state of distribution networks. One key technology that enhances observability in distribution systems is the Phasor Measurement Unit (PMU). However, there are still challenges in effectively deploying PMUs in practice. One challenge is the solution multiplicity in optimal PMU placement (OPP). Another challenge arises from the limited deployment space available at distribution nodes, leading to incomplete observability and the need for optimization to ensure node observability. To address the challenge of solution multiplicity, this work proposes a two-stage OPP approach that considers critical node observability. The first stage optimization model determines the minimum number of PMUs required to meet observability requirements. The second stage model identifies the solution with the greatest observability redundancy while satisfying practical constraints. To validate the effectiveness of our approach, this work conducts simulations using both IEEE testing systems and a real-world distribution feeder. These simulations provide empirical evidence of the benefits of the proposed OPP model and demonstrate its applicability in practical scenarios.

CyCadas: Accelerating interactive annotation and analysis of clustered cytometry data.

Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map (SOM) clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. CyCadas is available as open source at: Https://github.com/DII-LIH-Luxembourg/cycadas. Supplementary data are available at Bioinformatics online.

Photocatalysis with Covalent Organic Frameworks.

ConspectusUtilizing light to enable chemical conversions presents a green and sustainable approach to produce fuels and chemicals, and photocatalysis is one of the key chemical technologies that needs to be well developed in this century. Despite continuous progress in the advancement of various photocatalysts based on small inorganic and organic compounds, polymers, and networks, designing and constructing photocatalysts that combine activity, selectivity, and reusability remains a challenging goal. For catalytic activity, the difficulty originates from the complexity of photochemical reactions, where the light-harvesting system, multielectron and multihole-involving processes, and pinpoint mass delivery simultaneously need to be established in the system. For selectivity, the difficulty stems from the elaborate design of catalytic sites and space, especially their orbital energy levels, spatial arrangement, and environment; developing a molecular strategy that enables an overall design and control of these factors of different aspects is necessary yet arduous. For reusability, the difficulty arises from the stability and recyclability of the photocatalysts upon continuous operation under photoredox reaction conditions. How to recover photocatalysts in an energy-saving way to enable their cyclic use while retaining activity and selectivity is at the core of this problem. These bottleneck issues reflect that molecular design of a photocatalyst is not a simple summation of the above requirements, but a systematic scheme that can organically interlock various aspects is needed.To enable such an elaborate design and precise control, a basic requirement of the scaffold for constructing a promising photocatalyst is that its primary and high-order structures should be molecularly predesignable and synthetically controllable. Such a molecular regime has successfully evolved in natural photosynthesis, where light-harvesting chlorophyll antennae and photocatalytic centers are spatially well-organized and energetically well-defined to build ways for exciton migration, photoinduced electron transfer and charge separation, electron and hole flows, and oxidation of water and reduction of carbon dioxide, thereby converting water into oxygen to release ATP and NADPH via the light reaction and carbon dioxide into glucose with ATP and NADPH through the dark reaction. Similarly, a predesignable polymeric scaffold would be promising for integrating these complex photochemical processes to construct photocatalysts.Covalent organic frameworks (COFs) are a class of extended yet polymeric materials that enable the organization of organic units or metallo-organic moieties into well-defined architectures. In principle, COFs are molecularly designable with topology diagrams and synthetically controllable through polymerization reactions, offering an irreplaceable platform for designing and synthesizing photocatalysts. This feature enticed researchers to develop various photocatalysts based on COFs and drove the rapid progress in this field over the past decade. In this Account, we summarize the recent advances in the molecular design and synthetic control of COF photocatalysts, by highlighting the key achievements in developing ways to enable light harvesting, trigger photoinduced electron transfer and charge separation, allow charge carrier transport and mass delivery, control energy level, catalytic space, and environmental engineering, and develop stability and recyclability with an aim to reveal a full picture of this field. By scrutinizing typical photocatalytic reactions, we show the key problems to be addressed for COFs and predict future directions.

WHAT ARE BEST TOOLS FOR DIGITAL TRANSFORMATION IN TODAY WORLD HELPING PEOPLE FROM HEALTH AND BANKING SECTORS

In recent days we looks into the best digital transformation strategies for modernizing the banking and healthcare sectors in the rapidly evolving technological environment of today. It examines key technologies for the banking sector, including big data analytics, mobile banking applications, robotic process automation (RPA), and electronic health records (EHR) and artificial intelligence in healthcare diagnostics. The analysis examines how these solutions solve issues unique to the industry, boost productivity, and enhance customer satisfaction. By examining how both industries are approaching digital transformation, this report sheds light on emerging trends and best practices from across industries.[17] The results highlight the need for further innovation and cybersecurity in order to fully exploit the potential of digital transformation in improving public health outcomes. Key words: Digital transformation , Health Care, Cybersecurity, Technology.

A Brief Review of Key Technologies for Cloud-Based Battery Management Systems

A Brief Review of Key Technologies for Cloud-Based Battery Management Systems

Tracing the technological trajectories of smart logistics domain based on citation analysis and topic modelling

ABSTRACT The emergence and development of intelligent technology are triggering major changes in the logistics industry, and relevant scientific and technological research on smart logistics (SL) is emerging endlessly. However, there is no research that systematically explores SL from the technological perspective. Therefore, this article selects 47,253 documents retrieved from the Derwent Innovation Index for systematic analysis and provides a comprehensive understanding of the SL domain. We combine main path analysis (MPA) and LDA topic modelling approaches to identify technological evolution trajectories. Through three main paths, the key technologies and technological evolution directions in the SL domain are revealed. Through topic identification and topic correlation calculation, global topic popularity trends and local topic evolution paths are identified. Finally, this study summarises our findings, contributions, and limitations. This is the first attempt to detect key technologies in the SL domain through MPA and topic analysis, thereby guiding relevant researchers and enterprises.

Research and Application of CO2 Fire Prevention Mechanism and Key Technologies in Mines: A Review

Research and Application of CO2 Fire Prevention Mechanism and Key Technologies in Mines: A Review

Maturity of knowledge inputs and the breakthrough of key core technology

Maturity of knowledge inputs and the breakthrough of key core technology