Data Processing Methods Research Articles

A LithoScanner logging data processing method and application applicable to shale oil formation

A LithoScanner logging data processing method and application applicable to shale oil formation

6th German Oral Health Study (DMS • 6): data processing and statistical methods.

The 6th German Oral Health Study (DMS • 6) is a combined cross-sectional and cohort study with the main objective of reporting oral diseases in Germany. Based on cross-sectional data, current prevalence estimates and trend analyses on the development of oral health and care status in Germany were conducted using representative data. Associations between oral health and further participant characteristics were examined. The aim of this article is to provide details on data handling and statistical analysis of the cross-sectional data. Sample weighting: Weighting factors were used as part of the statistical analysis to correct for deviations between the analysis set and the population structure in Germany. The objective was to make nationwide representative statements for the age groups examined in the cross-sectional component of the DMS • 6. Different types of weights were calculated: design, non-response, and calibration weights. Processing of quantitative variables: The indices and transformed variables required for data analysis were defined based on variables collected in clinical examinations and social science interviews. Dental characteristics were aggregated at the participant level. For epidemiologic description, prevalence rates and means with associated 95% confidence intervals were calculated. Regression models were adjusted to estimate the strength of associations between participant characteristics of interest and oral health-related outcomes. To describe trends in the temporal development of oral health and dental care status in Germany, epidemiologic descriptions from DMS • 6 and previous studies were compared.

Impact of Backpack LiDAR Scan Routes on Diameter at Breast Height Estimation in Forests

Forest resource surveys are of vital importance for grasping the current status of forest resources, formulating management strategies, and evaluating ecosystem functions. Traditional manual measurement methods have numerous limitations in complex forest environments. The emergence of LiDAR technology has provided a new approach. Backpack LiDAR has been increasingly applied due to its portability and flexibility. However, there is a lack of comprehensive research on the influence of different scanning routes on data quality and analysis results. In this study, forest plots of four tree species, namely Carya cathayensis, Cinnamomum camphora, Koelreuteria bipinnata, and Quercus acutissima in Chuzhou City, Anhui Province, were selected as the research objects. Six scanning routes were designed to collect point cloud data using backpack LiDAR. After preprocessing, including denoising and ground point classification, diameter at breast height (DBH) fitting and accuracy evaluation were carried out. The results indicated that the individual tree recognition rates of C. cathayensis, C. camphora, and K. bipinnata reached 100%, while that of Q. acutissima was between 64.71% and 78.07% and was significantly affected by the scanning route. The DBH fitting accuracy of each tree species varied among different routes. For example, C. cathayensis had high accuracy in routes 1 and 6, and C. camphora had high accuracy in routes 1 and 3. Tree species characteristics, scanning routes, and data processing methods jointly affected the DBH fitting accuracy. This study provides a basis for the application of backpack LiDAR in forest resource surveys. Although backpack LiDAR has advantages, it is still necessary to optimize data acquisition schemes targeting tree species characteristics and improve point cloud data processing algorithms to promote its in-depth application in the forestry field.

Factors that influence behavioral intention in m-payment users in Surabaya

Many aspects of human life have shifted as a result of the rapid advancement of digital technology. Almost every aspect of human life that has been handled traditionally has begun to transition to the digital realm. In the economic field, digital payments using smartphones, also known as m-payments, are one proof of technological advances in the use of digital payments or m-payments. This study aims to analyze the factors that influence the behavioral intentions of m-payment users in Surabaya. The theory used in this study is UTAUT (Unified Theory of Acceptance and Use of Technology) with 6 independent variables (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit), 1 mediating variable (behavioral intention), 1 dependent variable (use behavior). Respondents in this study were 150 people who were analyzed using the SEM (Structural Equation Modeling) method of data processing and testing using SmartPLS software. Based on the results of data processing, shows that performance expectations, effort expectations, social influence, facilitating conditions, hedonic motivation, and habits have a positive effect on behavioral intentions. As well as behavioral intentions have a positive effect on usage behavior.

Perancangan Dan Pembuatan Cetakan Ornamen Teralis Dari Aluminium

The metal casting process is the process of pouring molten metal produced from melting into a mold at the melting temperature of the metal. Aluminum casting is generally done by the Sand Casting method, using a sand mold. Aluminum itself is a metal that is often used for various purposes, ranging from household appliances, construction, to automotive components and various other applications. This study focuses on the design and manufacture of aluminum trellis ornament molds, which are intended to improve production efficiency and the quality of the final product. The data processing method used in this study is the FEA (Finite Element Analysis) method. From the results of the analysis carried out through the FEA (Finite Element Analysis) analysis method that has been applied, it can be concluded that the stress distribution in the trellis ornament mold increases significantly with an increase in load from 50kg to 150kg. The results of the study also show that the mold is able to withstand loads of up to 150kg with tension and exceeding that which is within the safe limit. From the results of this study, it is recommended to make recommendations for improvements including the use of alternative materials with higher strength, redesigning the geometry of the mold to reduce stress concentration, and optimizing the process. Therefore, the results of this research are expected to be used in the future as a reference or literature to increase insight into the development of stronger and more efficient aluminum trellis ornament molds.

Optimizing Supply Chain Efficiency Using AI-Driven Predictive Analytics in Logistics

In modern supply chain management, shipping de- lays remain a significant issue, impacting customer satisfaction, operational effectiveness, and overall profitability. Traditional data processing methods don’t provide real-time information due to the latency in extracting, transforming, and loading (ETL) data from disparate sources. To alleviate this challenge, automated ETL processing combined with real-time data analytics offers an effective and scalable approach to minimizing shipping delays. This research explores the ways in which automated ETL workflows streamline shipping operations through the integration of real-time information from various sources, such as order management systems, GPS tracking, warehouse databases, and customer feedback platforms. The study recognizes the benefits of cloud-based ETL tools like Apache NiFi, Talend, and AWS Glue in automating data pipelines, reducing manual intervention, and improving data accuracy. Through real-time analytics with the help of tools such as Power BI, Apache Kafka, and Snowflake, businesses can monitor KPIs such as transit time, warehouse process efficiency, and last-mile delayed deliveries. The findings demonstrate that automated ETL processing reduces data la- tency, enhances supply chain visibility, and enables proactive decision-making. Real-time alerts generated through AI-based anomaly detection models also help logistics teams reduce poten- tial delays proactively before they become critical. Case studies conducted across e-commerce and third-party logistics providers (3PLs) demonstrate a 30Despite its advantages, challenges such as data integration complexity, security, and infrastructure costs must be addressed for seamless deployment. Hybrid ETL archi- tectures, including edge computing and blockchain, must be the subject of future research to further enhance real-time supply chain visibility. By embracing automated ETL and real-time analytics, businesses can significantly reduce shipping delays, improve logistics performance, and improve overall supply chain resilience in a world dominated by data.

Impact of the EU Energy Markets Transformations on the Ukrainian Economy

As a result of Russia’s full-scale war against Ukraine, Ukraine is experiencing destruction of its energy infrastructure and is forced to move away from the clean energy model in production and consumption. Moreover, the whole Europe has become one of the most affected regions, having suffered serious energy, economic, and political consequences. Therefore, the analysis of the mutual influence of energy changes in the EU on the economy of Ukraine is important for the establishment of an effective economic and energy policy that will facilitate the adaptation of the Ukrainian economy to global and regional energy transformations. In this regard, the purpose of this research is to establish the features, patterns, and tendencies of changes occurring on the EU energy markets and assess their influence on the economic development of Ukraine. This study explores the profound impact of the EU energy market transformations on Ukraine’s economic landscape, highlighting opportunities and risks in light of geopolitical tensions and global energy changes. Key areas include Ukraine’s integration into the EU’s energy systems, prospects for renewable energy and challenges in policy adaptation amid the ongoing conflict and infrastructure damages. The analysis highlights the urgency of modernizing Ukraine’s energy infrastructure to align with EU standards and implement green energy strategies. The research is carried out applying the comparative analysis, structural-logical, dialectical, generalization, and statistical data processing methods. As a result, the main trends of energy markets transformations in Europe are determined, which are related to a radical change in the European gas market structure, acceleration of the transition to renewable energy sources, reduction of final energy consumption, changes in price regulations on energy markets, filling of gas storage facilities, aggregate demand, and joint purchase of energy resources. In accordance with each established direction of transformation of the EU energy markets, the consequences of such changes for the Ukrainian energy system are determined and the risks associated with the reorientation of global trends in the energy market are identified. Accordingly, the consequences of such changes for the Ukrainian energy system are determined and the risks associated with the reorientation of global trends on the energy market are identified.

Portable and Self-Powered Sensing AI-Enabled Mask for Emotional Recognition in Virtual Reality.

With the increasing development of metaverse and human-computer interaction (HMI) technologies, artificial intelligence (AI) applications in virtual reality (VR) environments are receiving significant attention. This study presents a self-sensing facial recognition mask (FRM) utilizing triboelectric nanogenerators (TENG) and machine learning algorithms to enhance user immersion and interaction. Various TENG negative electrode materials are evaluated to improve sensor performance, and the efficacy of a single sensor is confirmed. For accurate facial movement and emotion detection, different machine learning algorithms are assessed, leading to the selection of an advanced data processing method with a two-layer long short-term memory model, which achieves 99.87% accuracy. The practical applications of the FRM system in virtual reality, including psychotherapy and HMI scenarios, are validated through mathematical models. Additionally, a digital twin-based monitoring platform is developed using 5G, database, and visualization technologies to oversee the user status. Overall, these innovative approaches overcome the limitations of existing face recognition technologies, including environmental interference and high cost, compared with other facial recognition technologies.

Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand

This study aims to construct a prediction model for the demand for medical and daily care services of the elderly and to explore the factors that affect the demand for medical and daily care services of the elderly. In this study, a questionnaire survey on the demand for medical and daily care services of 1291 elderly was conducted using multi-stage stratified whole cluster random sampling. SPSS21.0 statistical analysis software was used to describe the basic data of the elderly statistically, and univariate analysis was used to screen variables for model construction and binary logistic regression analysis. The acquired dataset has class imbalance, and to handle this issue, Synthetic Minority Over Sampling Technique with TomekLink (SMOTE-TomekLink) was adopted to resample the dataset for class-balancing. To improve computational efficiency, we used three algorithms to develop prediction models, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms. The performance of each model was measured, and the performance of the prediction model was obtained using the following performance metrics: accuracy (ACC), recall (R), precision (P), F1-score, and area under the receiver operating characteristic (AUC). The prediction models for the medical and daily care services demand of the elderly were developed and validated using 12 and 13 key features, respectively. The LightGBM algorithm emerged as the superior prediction model for estimating the service needs of the elderly. For the medical service demand prediction model, LightGBM achieved an AUC of 0.910 and F1-score of 0.841. In the daily care services demand prediction model, LightGBM demonstrated an AUC of 0.906 and an F1-score of 0.819. In the LightGBM model, the analysis of feature importance indicates that the number of chronic diseases, education level, and financial sources emerge as the most significant predictors for the demand of healthcare services, encompassing both medical and daily care services. Based on questionnaire information combined with feature selection, unbalanced data processing and machine learning methods, this study constructed a machine learning model for predicting the demand for medical and daily care services for the elderly, and analyzed the influencing factors of the demand for medical and daily care services for the elderly, providing a reference for the construction and verification of future prediction models for the demand for medical and daily care services for the elderly.

ОСОБЕННОСТИ впервые выявленных ЛОКАЛЬНЫХ ФОРМ ТУБЕРКУЛЕЗА У ПОДРОСТКОВ КИРОВСКОЙ ОБЛАСТИ

Tuberculosis among adolescents is an urgent public health problem, manifested by a variety of clinical forms and an ever-changing epidemiological landscape. The study showed the features of the newly identified local forms of tuberculosis in adolescents of the Kirov region for the period 2009-2023, focusing on the epidemiological and clinical aspects of the disease. Materials and methods. The study was based on an analysis of 53 case histories of adolescents with newly diagnosed local forms of tuberculosis who were treated at the Regional Clinical Tuberculosis Dispensary. In accordance with federal clinical guidelines, patients underwent comprehensive examination and treatment in a hospital setting. The effectiveness of therapy was assessed by clinical improvement, X-ray dynamics, and cessation of bacterial excretion. Statistical methods of data processing were used, where qualitative indicators were described as a percentage, and quantitative ones using the arithmetic mean and standard deviation. Results. More girls (68%) than boys (32%) were among the newly infected adolescents, and urban residents (60%) prevailed over rural residents (40%). The main risk factor was contact with a tuberculosis patient (87%). Pulmonary localization occurred in 66% of cases, with a predominance of the infiltrative form (32%). Of the detected concomitant diseases, the most common were diseases of the respiratory and cardiovascular systems. In 60% of cases, the disease was asymptomatic. Symptoms of intoxication prevailed among the complaints made by adolescents, and micropolyadenopathy was detected in one third of the patients during the examination. Conclusions. Tuberculosis in adolescents is characterized by a predominance of pulmonary localization and often occurs as an infiltrative form. Effective therapy makes it possible to eliminate bacterial excretion, positive radiological dynamics and improve the condition of patients. The identified risk factors emphasize the importance of prevention in the family and social circles of adolescents.

Study of high-resolution analysis of Rayleigh waves on the basis of the Choi–Williams distribution

Rayleigh wave exploration is widely used in the field of engineering investigation, which can analyze the near surface velocity structure. However, the traditional Rayleigh wave exploration data processing method can only provide the average change of Rayleigh wave phase velocity with depth under the seismic line, but cannot obtain the horizontal change of Rayleigh wave phase velocity along the line direction, which brings great challenges to the detection of underground wave velocity anomalies. To solve these problems, the Choi‒Williams distribution is introduced to process Rayleigh waves, and the relationships among the Rayleigh wave time, frequency and amplitude are obtained. The frequency dispersion curve is subsequently calculated via similarity analysis. In this method, first, the Rayleigh wave is intercepted in the time domain. Second, the Choi‒Williams distribution of Rayleigh waves is calculated, and the time domain waveform of each frequency component of each Rayleigh wave is obtained. The time difference is subsequently calculated via similarity analysis, and the dispersion curve between two adjacent channels is obtained. Finally, the phase velocity image below the seismic arrangement is obtained via combination with the multichannel Rayleigh wave dispersion curve. When this method is applied to the detection of geological anomalies in engineering investigations, it can accurately reflect the spatial distribution position of the detection target and has a relatively high lateral resolution.

A microwave tool for studying dielectric metasurfaces

Abstract Our contribution focuses on methods relevant to teaching/learning in optics, electromagnetic wave theory, and materials science at post-secondary, tertiary, or higher education levels, as well as researching and applying new findings to teaching. In particular, this paper describes a technique for performing a quasi-optical (microwave) experiment to characterize artificial structures such as dielectric metasurfaces. Dielectric particles and their arrays bear different types of resonant modes, which can be the subject of study in experimental lessons in university teaching tasks and PhD students' research. We describe a simple and cheap technique for sample preparation, experimental measurement setup based on standard microwave equipment only, and methods for data acquisition and processing. The introduced routine from the sample fabrication to result analysis could make undergraduate students see the electromagnetic waves in their essence toward a better understanding of how electromagnetic waves interact with optical nanostructures.

Особенности ценностных ориентаций студентов разных направлений подготовки

The purpose of the study is to determine the features of the value orientations of students from different fields of study. Research methods and organization. The total sample was made up of students in the field of training "Psychological and Pedagogical Education" (SFU), "Management" (RANEPA) and "State and Municipal Administration". Diagnostics of the value sphere was carried out using the method of diagnosing the real structure of value orientations of the personality by S.S. Bubnova, the methods of mathematical and statistical data processing were also used: descriptive statistics, Friedman's NPar-criterion; nonparametric Kruskal-Wallis test, nonparametric U-Mann-Whitney test. Research results and conclusions. Various perspectives of foreign and domestic scholars on personal value systems have been examined. The predominant value orientations of students in various academic disciplines have been identified.

Conjugated Polyelectrolyte-Based Sensor Arrays: from Sensing Mechanisms to Artificial Sensory System Conceptualization.

In the past decades, conjugated polyelectrolytes (CPEs) have become prominent in sensing applications due to their unique properties, including strong and tunable light absorption, high sensitivity, water solubility, and biocompatibility. Inspired by mammalian olfactory and gustatory systems, CPE-based sensor arrays have made significant strides in discriminating structurally similar analytes and complex mixtures for various applications. This review consolidates recent advancements in CPE-based sensor arrays, highlighting rational design, controllable fabrication, and effective data processing methods. It covers the fundamentals of CPE fluorescence sensing, emphasizing design strategies for sensor array units and data processing techniques. The broad applicability of CPE-based sensor arrays is demonstrated across diverse domains, including environmental monitoring (e.g., detecting metal ions and explosives), medical diagnostics (e.g., sensing disease markers and analyzing biological samples), and food safety (e.g., assessing the freshness, quality, and source of food products). Further, challenges and future directions in the field are discussed to inspire further research and development in this area.

Multivariate Air Quality Forecasting with Residual Nested LSTM Neural Network Based on DSWT

With the continuous deterioration of air quality and the increasingly serious environmental problem of air pollution, accurate air quality prediction is of great significance for environmental governance. Air quality index (AQI) prediction based on deep learning is currently a hot research topic. The neural network model method currently used for prediction has difficulty effectively coping with the high volatility of AQI data and capturing the complex nonlinear relationships and long-term dependencies in the data. To address these issues, this paper proposes multivariate air quality forecasting with a residual nested LSTM neural network based on the discrete stationary wavelet transform (DSWT) model. Firstly, the DSWT data-decomposition technique decomposes each AQI data point into multiple sub-signals. Then, each sub-signal is sent to the NLSTM layer for processing to capture the temporal relationships between different pollutants. The processed results are then combined, using residual connections to mitigate issues of gradient vanishing and explosion during the model training process. The inverse mean squared error method is combined with the simple weighted average method, to serve as the weight-update approach. Back propagation is then applied, to dynamically adjust the weights based on the prediction accuracy of each sample, further enhancing the model’s prediction accuracy. The experiment was conducted on the air quality index dataset of 12 observation stations in and around Beijing. The results show that the proposed model outperforms several existing models and data-processing methods in multi-task AQI prediction. There were significant improvements in mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R square (R2).

Методический подход к формированию системы сбора «больших данных» при открытой разработке месторождений угля

The article examines various approaches to designing a Big Data collection systems for mining systems based on the analysis of the differences between mining coal and ore deposits. Specific features of handling Big Data are provided openpit mining of different types of solid minerals. Special aspects of data collection are highlighted for effective management of the coal-mining companies. A schematic diagram of the coal material flows is given, on the basis of which principles of data handling and digitalization of technological processes are described. Coal mining enterprises have their own specific features of the organization of technological processes, which drives the formation of requirements for the systems and methods of data collection and processing. The typical specific tasks include management of coal material flow, including its quality, starting from the mine face and up to dispatch of the finished product, creation of the material flow system, availability of different types and payload classes of open-pit transportation. In addition to the type of mineral deposits, it is necessary to consider the technological infrastructure (wireless data transmission systems, distributed server capacity, etc.), as well as the requirements of the related systems, including environmental and social demands, which vary depending on the geography and type of the solid mineral deposit.

Multifrequency seafloor acoustic backscatter as a tool for improved biological and geological assessments – updating knowledge, prospects, and challenges

Multibeam echosounders (MBES) have emerged as a primary tool for seafloor mapping over the past three decades. Technological advancements and improved data processing methods have increased the accuracy and spatial resolution of bathymetric measurements, and have also led to the increasing use of MBES backscatter data for seafloor geological and benthic habitat mapping applications. MBES backscatter is now frequently used to characterize habitat for marine flora and fauna, contribute to the development of effective marine spatial planning and management strategies, and generally better classify the seabed. Recently, further technological advances have enabled the acquisition and analysis of backscatter at multiple sonar operating frequencies (multifrequency backscatter), with follow-on potential benefits for improved seafloor characterization and classification. This review focuses on the currently available peer-reviewed papers related to multifrequency seafloor acoustic backscatter, providing a comprehensive summary of the contributions across different benthic environments, setting the stage for related applications and outlining challenges and research directions.

A new trauma severity scoring system adapted to wearable monitoring: A pilot study.

Wearable technologies represent a strong development axis for various medical applications and these devices are increasingly used in daily life as illustrated by smart watches' popularisation. Combined with new data processing methods, it constitutes a promising opportunity for telemonitoring, triage in mass casualty situations, or early diagnosis after a traffic or sport accident. An approach to processing the physiological data is to develop severity scoring systems to quantify the critical level of an individual's health status. However, the existing severity scores require a human evaluation. A first version of a severity scoring system adapted to continuous and real-time wearable monitoring is proposed in this article. The focus is made on three physiological parameters straightforwardly measurable with wrist-wearables: heart rate, respiratory rate, and SpO2, which may be enough to characterise continuously hemodynamic and respiratory status. Intermediate score functions corresponding to each physiological parameter have been established using a sigmoid model. The boundary conditions have been defined based on a survey conducted among 54 health professionals. An adapted function has also been developed to merge the three intermediate scores into a global score. The scores are associated with a triage tricolour code: green for a low-priority casualty, orange for a delayable one, red for an urgent one. Preliminary confrontation of the new severity scoring system with real data has been carried out using a database of 84 subjects admitted to the intensive care unit. Colour classification by the new scoring system was compared with independent physicians' direct evaluation as a reference. The prediction success rate values 74% over the entire database. Two examples of continuous monitoring over time are also given. The new score has turned out to be consistent, and may be easily upgraded with the integration of additional vital signs monitoring or medical information.

Wind regime changes on the shipping routes of the Russian seas at the end of the 20th – beginning of the 21st centuries

This article aims to assess wind regime variations in the Russian seas and identify shipping and fishing areas where the frequency of dangerous winds showed statistically significant upward trends. The assessment is based on ERA-5 global atmospheric reanalysis data on hourly mean wind velocity from 1991–2020. The methodology employed standard statistical methods for data processing. The results of the assessment of increased storm risks across all Russian seas are summarized in a table. The study confirms that the wind regime in the Russian seas is non-stationary and responds to global climate variations. The most dynamic changes in wind conditions occur in the open seas (Bering, Barents), which have free exchange with external seas and oceans. The greatest risks to shipping and fishing, associated with an increased frequency of dangerous winds given medium or high climatic norms for this indicator, were identified in the following areas: the northeastern part of the Black Sea, including approaches to the ports of Novorossiysk, Tuapse, and the Kerch Strait area (winter season); the Baltic Sea, including shipping lanes in its central part and the Gulf of Finland (winter season); the Barents Sea (winterspring season); the southwestern part of the Bering Sea, including sections of transoceanic routes (winter season); and the northwestern sector of the Pacific Ocean, including sections of shipping lanes and fishing areas off the eastern coasts of Kamchatka and the Kuril Islands (winter-spring season). It is noted that ship crews and shipping company personnel should account for the increasing storm risks when planning navigation in these areas (especially those with harsh climates) and take measures to minimize them, including considering alternative routes. Emphasis is placed on the critical importance of the quality of training of ship crews for navigation in storm conditions, icing, steering failure, and assisting vessels in distress.

One-dimensional convolutional neural network-based analysis of dielectric spectral abundance in liquid-phase media

Aiming at the difficult problem of component information analysis of mixed dielectric spectra, the component information characteristics of mixed dielectric spectra are investigated by one-dimensional convolutional neural network, and the component analysis of mixed media is realised. First, the mixed dielectric spectra of water, ethanol and isopropoxyethano (iso) with different volume ratios were obtained by experimental measurements, and the singular spectrum analysis (SSA) method was applied to denoise the raw data, which provides a new data processing method for the effective analysis of dielectric spectra. Then, Utilizing the linear mixing model, we systematically obtained the dielectric spectra of binary mixtures of pure water and isopropoxyethanol with diverse ratios, along with those of multi-component mixtures integrating water, ethanol, and isopropoxyethanol at various proportion settings. The generated data were used as a training set for a one-dimensional convolutional neural network to model the correlation between the mixed dielectric spectra and the mixing ratios. The coefficient of determination (R2) values of the model for the two-component and three-component mixed solution test sets were both found to be 0.999. In the validation sets, the corresponding R² values were determined to be 0.9887 and 0.9786, indicating that the accuracy and reliability of the unmixing algorithm based on the one-dimensional convolutional neural network in predicting the dielectric spectra of different proportions of the mixed media are good. This study provides an effective method to analyse and predict the dielectric properties of mixed media integrated apparent dielectric spectra, which provides a scientific basis for the research and analysis of the dielectric properties of mixed media, in addition to this study, this study promotes the integration of dielectric spectroscopy analysis and machine learning, providing new ideas and tools for the research of water environment governance and hydrochemical analysis.