Human Intervention Research Articles

LigandDiff: de Novo Ligand Design for 3D Transition Metal Complexes with Diffusion Models.

Transition metal complexes are a class of compounds with varied and versatile properties, making them of great technological importance. Their applications cover a wide range of fields, either as metallodrugs in medicine or as materials, catalysts, batteries, solar cells, etc. The demand for the novel design of transition metal complexes with new properties remains of great interest. However, the traditional high-throughput screening approach is inherently expensive and laborious since it depends on human expertise. Here, we present LigandDiff, a generative model for the de novo design of novel transition metal complexes. Unlike the existing methods that simply extract and combine ligands with the metal to get new complexes, LigandDiff aims at designing configurationally novel ligands from scratch, which opens new pathways for the discovery of organometallic complexes. Moreover, it overcomes the limitations of current methods, where the diversity of new complexes highly relies on the diversity of available ligands, while LigandDiff can design numerous novel ligands without human intervention. Our results indicate that LigandDiff designs unique and novel ligands under different contexts, and these generated ligands are synthetically accessible. Moreover, LigandDiff shows good transferability by generating successful ligands for any transition metal complex.

Tritium management in ITER test blanket systems port cell for maintenance operations

Four Test Blanket Systems (TBS) will be tested in the International Thermonuclear Experimental Reactor equatorial ports #16 and #18 to verify tritium breeding and heat extraction technology. A significant quantity of tritium would be produced in TBM, and partly released into the port cell from the pipework of TBS or other high-temperature components due to its strong mobility and high permeation. The port cell should be accessible during equipment maintenance and human intervention. This work built a multi-dimensional geometric model to characterize HTO transport in the port cell, absorption/desorption, and diffusion in walls and discussed the effect of paint thickness, ventilation rate, source term, and epoxy properties on detritiation efficiency. The results suggest that a 0.1–0.16 mm paint with the lowest HTO solubility is optimal from the compromise between quick cleanup and tritiated waste decommission. A higher ventilation rate could accelerate detritiation while minimizing the radioactive source by a tritium-resisting layer is the most direct method. The optimized design options for reducing the time required to reach 1 DAC in 12 h still need further discussion because of the delayed HTO source from epoxy paint and dead zone of the flow field.

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

Caregiver impact on cattle health, performance and well-being

Animal well-being is founded on positive interactions and building trusting relationships between caregivers and cattle. Trusting relationships between animals and caregivers result in positive performance levels providing the foundation for a holistic system which benefits the whole internal supply chain i.e., from conception to harvest. The positive effect of handling livestock with methods based on handler understanding of cattle behavior inclusive of accli­mation and exercise techniques is seen predominately in quali­tative measures resulting in quantitative results in health and performance. Every human intervention such as calving, processing, wean­ing, pasture rotation, transportation and address change is an opportunity to build cattle confidence, health, performance and well-being.

Mechanisms of microbiota modulation: Implications for health, disease, and therapeutic interventions.

Microbiota modulation, the intentional change in the structure and function of the microbial community, is an emerging trajectory that holds the promise to mitigate an infinite number of health issues. The present review illustrates the underlying principles of microbiota modulation and the various applications of this fundamental process to human health, healthcare management, and pharmacologic interventions. Different strategies, directing on dietary interventions, fecal microbiota transplantation, treatment with antibiotics, bacteriophages, microbiome engineering, and modulation of the immune system, are described in detail. This therapeutic implication is reflected in clinical applications to gastrointestinal disorders and immune-mediated diseases for microbiota-modulating agents. In addition to this, the review outlines the challenges of translating researched outcomes into clinical practice to consider safety and provides insights into future research directions of this rapidly developing area.

Improved fiber orientation measurement in nonwovens with corner removal

The orientation of fibers or filaments in nonwovens is critical in determining their mechanical characteristics. Image processing techniques, prized for their minimal human intervention and rapid processing speed, are widely utilized in nonwoven fiber orientation measurement. However, these techniques often face substantial challenges, such as low accuracy in corner detection, errors in fiber segmentation, and inefficiencies in fiber orientation calculation. Addressing these concerns, this study introduces a novel, enhanced method accompanied by two innovative optimization algorithms to enhance accuracy. The first innovation involves the development of a newly developed fiber corner detection algorithm, dubbed the T-detector, specifically tailored for the unique characteristics of fiber images, enabling efficient corner point detection and removal. Subsequently, we introduce and employ a fiber length restriction algorithm to further segment the processed longer fibers into the remaining fiber fragments and utilize a skeleton projection algorithm to calculate the fiber orientation. These algorithms overcome the existing technology’s inherent shortcomings, thereby heightening measurement accuracy. The results illustrate an improvement in measurement precision over other orientation distribution measurement algorithms, with the fiber information retention (covering ratio) reaching an impressive 95%. Our proposed method not only calculates fiber orientation distribution in nonwovens with remarkable accuracy and efficiency, but its innovative approach also stands to provide a theoretical foundation for the design of three-dimensional filtering models with specific fiber orientation.

Affordable Sensors for Speditive and Accurate Documentation of Built Heritage: First Tests and Preliminary Results

Abstract. When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

BackgroundIn recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications.ResultsTo mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index.ConclusionThe results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA. Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/.

Prognostics and Health Management of Unmanned Surface Vessels: Past, Present, and Future

Abstract With the increasing popularity and deployment of unmanned surface vessels (USVs) all over the world, prognostics and health management (PHM) has become an indispensable tool for health monitoring, fault diagnosis, health prognosis, and maintenance of marine equipment on USVs. USVs are designed to undertake critical and extended missions, often in extreme conditions, without human intervention. This makes the USVs susceptible to equipment malfunction, which increases the probability of system failure during mission execution. In fact, in the absence of any crew onboard, system failure during a mission can create a great inconvenience for the concerned stakeholders, which compels them to design highly reliable USVs that must have integrated intelligent PHM systems onboard. To improve mission reliability and health management of USVs, researchers have been investigating and proposing PHM-based tools or frameworks that are claimed to operate in real time. This paper presents a comprehensive review of the existing literature on recent developments in PHM-related studies in the context of USVs. It covers a broad perspective of PHM on USVs, including system simulation, sensor data, data assimilation, data fusion, advancements in diagnosis and prognosis studies, and health management. After reviewing the literature, this study summarizes the lessons learned, identifies current gaps, and proposes a new system-level framework for developing a hybrid (offline-online) optimization-based PHM system for USVs in order to overcome some of the existing challenges.

The shape of fringing tidal flats in engineered estuaries

For the management of estuaries and the preservation of tidal flats it is crucial to understand the tidal flat shape and development. Previous work focused predominantly on the quasi-equilibrium shape of tidal flats along open coasts with a dominant cross-shore flow and wave exposure. This paper evaluates the shape of fringing tidal flats in engineered estuaries, where longshore velocities generally dominate. Using a long-term (20 years) topographic data set of an anthropogenically modified estuary in the Netherlands (the Western Scheldt estuary), we relate key profile shape parameters and changes over time to natural and anthropogenic processes. In an engineered estuary, the tidal flat shape depends on the estuary geometry, hydrodynamic forcings and human interventions. In contrast to open coast tidal flats, the presence of the channel and dominant longshore flow determines the available cross-shore length (accommodation space) of the tidal flat and the shape of the tidal flat. This accommodation space defines the maximum tidal flat height and opportunity for marsh development. We propose the use of the Index of Development, indicating to what extend tidal flats have space to develop. This index is not only influenced by longshore and cross-shore flow, but also (or even more) by hydraulic structures, dike realignments and channel migration. Especially the latter two strongly influence the accommodation space and thereby the maximum tidal flat height and the opportunity for marsh development. For large stretches of the Western Scheldt, the accommodation space is too small, and the majority of the tidal flats do not vertically extent to mean high water. The success of tidal flat and marsh restoration projects depends on the accommodation space.

Classification model for chlorophyll content using CNN and aerial images

Chlorophyll content is usually used as a quantitative measurement of plant health. The chlorophyll content is also a continuous number of data type, leading to a regression approach when developing the deep learning model. The regression model will predict the chlorophyll content in number format, which requires experts to analyse the outcome. Nevertheless, the analysis of the outcome could possibly lead to human error in diagnosing the plant’s health condition. Therefore, this study proposed a classification approach in developing a deep learning model to analyse the plant’s health condition without human intervention. The classification approach requires a discrete group for dependent variables instead of continuous numbers. When forming the chlorophyll content index groups in this study, which are low, optimum and high levels, two research studies were combined to form the groups, which were (1) the product of the standard range of nitrogen value in mango plant and (2) the correlation analysis between nitrogen value and chlorophyll content index. The classification model in this study used transfer learning algorithms, which were InceptionV3, DenseNet121 and ResNet50, with the canopy-scale level of mango plant RGB images with complex leaf structures in an uncontrolled and open area. Based on the findings, the classification model could classify the chlorophyll content index levels on both mango plant images, which were infected and not infected with black sooty mould. The finding also shows that a clearer distribution pattern of spectral information extracted from the mango plant images can influence the performance result of the classification model. Besides that, the starting point of the Digitization Footprint for this study site across the development stages of the classification model was 308.5756 MB/ha. Finally, the overall accuracy performances for the classification models that used the transfer learning algorithms, which were InceptionV3, DenseNet121, and ResNet50, and trained using the images of the mango plant infected with pest were 96.49 %, 92.98 %, and 89.47 %, respectively, and for using the images of the mango plant not infected with pest were 88.10 %, 78.57 %, and 69.05 %, respectively.

Ecology and behaviour of the invasive lionfish Pterois miles colonizing coastal areas of the central Mediterranean

Biodiversity redistribution is occurring at an unprecedented pace, with one prominent example being the invasion of the lionfish (Pterois miles) in the Mediterranean Sea, altering the fish community composition by creating novel interspecific interactions and potentially threatening local biodiversity. Our study attempts to identify patterns in the lionfish distribution and behaviour in the coastal zone of the eastern Ionian Sea (central Mediterranean). Over a 14-month period, visual censuses were conducted along coastal strip transects at six stations, situated in three areas (Mani, Katakolo, Astakos) across a latitudinal gradient. Lionfish density was highest in Mani, the southernmost area. Higher densities were found at two stations experiencing direct anthropogenic pressures: one with structural modifications by port infrastructure, and the other in the vicinity of a fish farm, indicating a potential role of human interventions in facilitating the establishment of invasive populations. Lionfish group size was influenced by overall density, exhibiting variation between the two natural stations. Lionfish activity levels by microhabitat were site-specific, with more inactive individuals in crevices. Temporal activity patterns were similar in the natural stations, with increased activity during spring and summer months; however, temporal activity patterns were not identical in the two anthropogenic sites, suggesting that the specific nature of anthropogenic intervention might possibly influence lionfish activity. Finally, lionfish densities were not significantly different between early morning and late afternoon times of day, in agreement with the crepuscular behaviour already documented for this species.

A data-driven conceptual framework for understanding the nature of hazards in railway accidents

Hazards threaten railway safety by their potential to trigger railway accidents, resulting in significant costs and impacting the public's willingness to use railways. Whilst many prior works investigate railway hazards, few offer a holistic view of hazards across jurisdictions and time because the large number of primary sources make synthesising such learnings time consuming and potentially incomplete. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic modelling for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. The results allow the different aspects of each hazard to be listed along with the potential combinations of hazards that could trigger railway accidents. Better understanding of the aspects of individual hazards and the relationships between hazards and previous accidents can inform more effective hazard mitigation policies including technical or regulatory interventions. A case study of the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability to better inform policymaking for managing risks. The primary contributions of the framework proposed are to enable a large amount of knowledge accumulated to be summarised for an intuitive policymaking process, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could apply the technique to road, aviation or maritime accidents.

Home Automation Using Image Processing

Today, many of the automation systems deployed are mostly controlled by humans. This control by humans restricts the automation of home appliances to an extent. Also, most of the deployed home automation systems use the Internet of Things technology to control the appliances. In this we propose a system developed using action recognition to fully automate the home appliances. We recognize the three actions of a person (sitting, standing and lying) along with the recognition of an empty room. With this system, we remove the human intervention in home automation systems for controlling the home appliances and at the same time we ensure the data privacy and reduce the energy consumption by efficiently and optimally using home appliances and also it has a security mode. In the security mode if any human is detected it captures the image of the person detected and shares the picture to responsible person,the picture shared will be refreshes with every one minute and also it uploads the captured image in local IP address provided to it. through the local IP no. of persons can see the image captured.

Efficacy of the QuitSure App for Smoking Cessation in Adult Smokers: Cross-Sectional Web Survey.

Cigarette smoking remains one of the leading causes of preventable death worldwide. A worldwide study by the World Health Organization concluded that more than 8 million people die every year from smoking, tobacco consumption, and secondhand smoke. The most effective tobacco cessation programs require personalized human intervention combined with costly pharmaceutical supplementation, making them unaffordable or inaccessible to most tobacco users. Thus, digital interventions offer a promising alternative to these traditional methods. However, the leading smartphone apps available in the market today have either not been studied in a clinical setting or are unable to match the smoking cessation success rates of their expensive offline counterparts. We would like to understand whether QuitSure, a novel smoking cessation app built by Rapidkart Online Private Limited, is able to bridge this efficacy gap and deliver affordable and effective smoking cessation at scale. Our objective was to do an initial exploration into the engagement, efficacy, and safety of QuitSure based on the self-reported experiences of its users. Outcomes measured were program completion, the effect of program completion on smoking behavior, including self-reported cessation outcomes, and negative health events from using the app. All QuitSure registered users who created their accounts on the QuitSure app between April 1, 2021, and February 28, 2022, were sent an anonymized web-based survey. The survey results were added to their engagement data on the app to evaluate the feasibility and efficacy of the app as a smoking cessation intervention. The data were analyzed using descriptive statistics (frequencies and percentages) and the χ2 test of independence. In total, 1299 users who had completed the QuitSure program submitted the survey and satisfied the inclusion criteria of the study. Of these, 1286 participants had completed the program more than 30 days before filling out the survey, and 1040 (80.1%, 95% CI 79.1%-82.6%) of them had maintained prolonged abstinence for at least 30 days after program completion. A majority of participants (770/891, 86.4%) who were still maintaining abstinence at the time of submitting the survey did not experience any severe nicotine withdrawal symptoms, while 41.9% (373/891) experienced no mild withdrawal symptoms either. Smoking quantity prior to completing the program significantly affected quit rates (P<.001), with heavy smokers (>20 cigarettes per day) having a lower 30-day prolonged abstinence rate (relative risk=0.91; 95% CI 90.0%-96.2%) compared to lighter smokers. No additional adverse events outside of known nicotine withdrawal symptoms were reported. The nature of web-based surveys and cohort selection allows for extensive unknown biases. However, the efficacy rates of survey respondents who completed the program were high and provide a case for further investigation in the form of randomized controlled trials on the QuitSure tobacco cessation program.

Technological innovations and optimized work methods in subsea maintenance and production

Subsea maintenance and production represent critical aspects of offshore operations, vital for sustaining energy production and ensuring operational efficiency. However, these endeavors face numerous challenges, including the complexities of deepwater environments, harsh weather conditions, and the high costs associated with traditional maintenance methods. To address these challenges, this paper explores the integration of technological innovations and optimized work methods in subsea operations. Technological innovations play a pivotal role in revolutionizing subsea maintenance and production. Remote monitoring and control systems enable real-time data collection and decision-making, enhancing operational visibility and efficiency. Autonomous underwater vehicles (AUVs) offer the capability to conduct inspections and repairs in remote and hazardous environments, reducing human intervention and associated risks. Robotics and automation further streamline maintenance tasks, improving accuracy and reducing downtime. Advanced materials and coatings enhance equipment durability and corrosion resistance, prolonging asset lifecycles and reducing maintenance requirements. In parallel, optimized work methods offer strategic approaches to enhance subsea operations' effectiveness. Predictive maintenance strategies leverage data analytics and machine learning to anticipate equipment failures, enabling proactive interventions and minimizing downtime. Condition-based monitoring facilitates real-time assessment of asset health, enabling timely maintenance interventions and cost savings. Integrated asset management systems provide holistic insights into asset performance and facilitate informed decision-making. Lean operations and continuous improvement methodologies further optimize workflows, driving operational excellence and cost efficiency. Through case studies and industry examples, this paper highlights the successful implementation of technological innovations and optimized work methods in subsea maintenance and production. Furthermore, it explores future trends, regulatory considerations, and the importance of industry collaborations in shaping the future of subsea operations. Ultimately, the integration of these approaches offers a pathway towards enhanced operational efficiency, reduced costs, and sustainable subsea operations. Keywords: Technological Innovations, Optimized Work Methods, Subsea Maintenance and Production.

IOT based Smart Plant Monitoring System and Control System

The world is now fully dependent on the internet and the Internet of Things (IoT), which is the inter-networking of physical devices embedded with electronics, sensors, software and network connectivity. IoT enables physical devices to exchange data with the manufacturer and achieve greater value and services. Agriculture is the backbone of many countries, but water scarcity is a major issue in this field. To overcome this problem, the irrigation process can be automated using IoT. By using sensors, the temperature, humidity, and light can be measured, and appropriate processing can be performed based on the outcome. This helps in reducing water wastage and improving accuracy, economic benefits, and efficiency while reducing human intervention. This paper highlights the importance of IoT in day-to-day life, its scope in the future, and its potential applications in farming methods. To demonstrate the use of IoT in agriculture, we present the Automatic Watering System, which monitors and maintains the moisture content in the soil using temperature, humidity, and moisture sensors. The Arduino UNO microcontroller is used as the control unit, regulating the appropriate amount of water to avoid over/under irrigation. Keywords – sensors, temperature, humidity, soil moisture, Arduino uno

A domain knowledge-informed design space exploration methodology for mechanical layout design

Layout designs have a large number of design variables and various physical constraints. Conventional design exploration approaches are time-consuming and may require human intervention. A unique feature about physical layout designs is the availability of domain knowledge, which can be utilised to speed up the design process. In this paper, we propose a generative deep learning-based design space exploration (DSE) methodology that is capable of learning design constraints in the layout design without explicit supervision. Moreover, it can incorporate domain knowledge in the generated layouts, thereby speeding up the design process. This is realised by constructing a layout generation variational autoencoder (LGVAE) model, which uses a latent space as an interface to generate the layouts. By training the LGVAE model, significantly lower-dimensional representations can be learned compared to the original dimensionality of the design space. Therefore, the number of design variables is greatly reduced. We showcase the performance of the proposed DSE approach by solving the heat source layout design problem encountered in thermal management of chips. Experiments demonstrate that the LGVAE model is capable of generating compressed latent representations that capture the characteristics of the input samples, which makes the DSE cost-effective.

The Impact of Blockchain Technology on Financial Markets and Its Future Trends: An Economic Perspective Based on Data Analysis

Based on the economic perspective of data analysis, this paper deeply discusses the impact of blockchain technology on the financial market and its future trend. By using econometrics, we systematically analyze how blockchain technology can change the operation mechanism of financial markets, improve transaction efficiency, reduce transaction costs, and enhance the security and transparency of financial markets. The study has found that the decentralized nature of blockchain technology can help to break down the barriers of the traditional financial market and promote the popularization and inclusiveness of financial services. At the same time, the automatic execution function of smart contracts simplifies the process of financial transactions and reduces the risk of human intervention and fraud. In addition, the blockchain technology has also enhanced the trust mechanism of the financial markets by improving the security and traceability of the data. In terms of econometric analysis, we use a large amount of financial data to empirically test the impact of blockchain technology on the performance of the financial market by building regression models, time series analysis and other methods. The results show that there is a significant positive correlation between the application of blockchain technology and financial market efficiency, liquidity and stability. Looking into the future, with the continuous innovation and improvement of blockchain technology, its application in the financial market will be more extensive and deeper. On the one hand, blockchain technology is expected to promote the further digitalization and intelligence of the financial market, and improve the convenience and personalization of financial services. On the other hand, blockchain technology will also promote the cross-border integration and innovation of the financial market, and inject new vitality into the global economic development.