Precise Data Analysis Research Articles

ENHANCING NATURAL LANGUAGE PROCESSING USING WALRUS OPTIMIZER WITH SELF-ATTENTION DEEP LEARNING MODEL IN APPLIED LINGUISTICS

Sentiment analysis (SA) is the popular natural language processing (NLP) problem utilized for analyzing texts, including uploaded reviews and user posts on e-commerce portals, forums, and social media platforms, regarding their opinions about the event or person, product, and service. The SA task comprises analyzing text to determine whether the sentiment expressed is negative, positive, or neutral, aiming for precise subjective data analysis. The deep learning (DL) technique enables various architectures to model SA tasks and has surpassed other machine learning (ML) techniques as the first method to perform SA tasks. Recent advancements in the DL model depart from the growing dominance of transformer language algorithms and convolutional neural network (CNN) and recurrent neural network (RNN) models. Utilizing pre-trained transformer language models to transfer knowledge to downstream tasks has emerged as a cutting-edge model in NLP. Therefore, the study designs a fractal walrus optimizer with self-attention DL-based SA in applied linguistics (WOSADL-SAAL) method. The WOSADL-SAAL method aims to recognize and classify the presence of sentiments in social media content. In the WOSADL-SAAL technique, data preprocessing is initially performed to transform the input dataset into a meaningful format. In addition, the WOSADL-SAAL technique uses the bag of words (BoW) model for extracting features. The self-attention bidirectional long short-term memory (SA-BiLSTM) network is applied to classify sentiment. The walrus optimizer (WO) model performs the hyperparameter tuning model to boost the detection outcomes of the SA-BiLSTM network. The performance evaluation of the WOSADL-SAAL method takes place under the benchmark SA dataset. The experimental analysis of the WOSADL-SAAL method exhibited a superior accuracy value of 99.07% and 99.24% under TUSA and IMDB datasets over existing approaches.

Managing perceptions on the linguistic terms of qualitative scales

In this paper, we consider the problem of how individuals perceive the ordinal proximities between the linguistic terms of an ordered qualitative scale. We address the challenge of accurately measuring these ordinal proximities in ordered qualitative scales used in surveys. To tackle this issue, we have devised a visual procedure, managed through sliders, and an appropriate software that generates the metrizable ordinal measure which represents the ordinal arrangement of the proximities between the linguistic terms of an ordered qualitative scale. The procedure has been applied to real-world survey data to validate its effectiveness. The results indicate that the new proposal significantly outperforms traditional techniques in terms of accuracy and reliability, providing a robust tool for researchers in fields requiring precise ordinal data analysis.

On the Consistency of Stochastic Noise Properties and Velocity Estimations from Different Analysis Strategies and Centers with Environmental Loading and CME Corrections

The analysis of the Global NFavigation Satellite System (GNSS) time series provides valuable information for geodesy and geodynamics researcFh. Precise data analysis strategies are crucial for accurately obtaining the linear velocity of GNSS stations, enabling high-precision applications of GNSS time series. This study investigates the impact of different stochastic noise models on velocity estimations derived from GNSS time series, specifically under conditions of environmental loading correction and common mode error (CME) removal. By comparing data from various data centers, we find that post-correction, different analysis strategies exhibit high consistency in their noise characteristics and velocity estimation results. Across various analysis strategies, the optimal noise models were predominantly Power Law with White Noise (PLWN) and Fractional Noise with White Noise (FNWN), with the optimal noise models including COMB/JPL, COMB/SOPAC, and COMB/NGL for approximately 50% of the datasets. Most of the stations (approximately 80%) showed velocity differences below 0.3 mm/year and velocity estimation uncertainties below 0.1 mm/year. Nonetheless, variations in amplitudes and periodic signals persisted due to differences in the processing of raw GNSS observations. For instance, the NGL and JPL datasets, which were processed using GipsyX 2.1 software, showed higher amplitudes of the 5.5-day periodic signal. These findings provide a solid empirical foundation for advancing data analysis methods and enhancing the reliability of GNSS time series results in future research.

Intelligent fibers and textiles for wearable biosensors

AbstractIn conjunction with the advancement of digital healthcare, the field of wearable biosensors has experienced rapid growth in recent years and is projected to expand further in the coming years. As wearable biosensors enter their next phase, emphasis is increasingly placed on developing comfortable, breathable, washable, and lightweight intelligent fibers and textiles as key components. This review examines the contributions of both natural and synthetic fibers and textiles to wearable biosensors. The structure and preparation process of intelligent fibers and textiles are systematically elucidated, including the development of the conductive layer. Additionally, key micro–nano technologies that have emerged in this domain are highlighted such as built‐in power supplies, wireless data transmission, precise data analysis driven by artificial intelligence, and machine learning. Furthermore, advanced functionalities integrated into recent biosensing systems, such as heat management, real‐time displays, and human–machine interaction are summarized. Moreover, the applications of fiber‐based wearable biosensors for monitoring biophysical and biochemical signals are presented. Finally, the challenges faced by the community working on wearable sensors based on intelligent fibers and textiles are discussed alongside promising future directions.

Optimization of Chemical Engineering Processes in the Mining and Metal Industry: A Review

Process optimization is acutely vital: mining and a metal industries research. Through applying modelling and optimization tools, businesses see tremendous improvement in process efficiencies, declining costs of production, and ensuring high quality. The valuable of simulation tools have also been reflected in the design of the virtual plants which make possible to conduct test runs of different process scenarios thereby providing the opportunity to optimize the performance of the plants. So, even if these issues are close to being resolved, some challenges must still be tackled to approach the full field of process optimization enjoyment. One of the main drawbacks is the integration of sustainable development and environmental evaluation into optimization phase. This can be achieved by developing models that are amended to an environmental impact of each process parameters and afterwards an optimization of processes will be based on environment factors. The second challenge is for the research community to come up with more advanced and sophisticated mathematical models that are capable of not only capturing but also depicting the diverse and complex interactions that take place between different processes variables. Such an approach indeed requires implementation of more precise data analysis tools and techniques, including neural networks and genetic algorithms. While there are many difficulties in it way, the process optimization of a bright future is foreseeable. The fast growth of the technological sphere, such as machine-2-machine and big data analytics, enables process optimization which was unavailable before. By implementation of a set of sensors and real-time data analysis plant operators are being provided with necessary information that enables this way to exercise real-time decisions for the sake of the increase of the plant performance. Minimizing energy consumption, increasing the product yield, and less damaging to the environment is a potential focus for the research. Through the application of mathematical models, optimization algorithms, and simulation-based approaches, one can compare the different methods of each process and the produced product yields.

Comparative Efficacy of Animal Depression Models and Antidepressant Treatment: A Systematic Review and Meta-Analysis.

Animal models are critical tools in the study of psychiatric disorders; however, none of the current models fully reflect human stress-related disorders, even though most of the knowledge about the mechanisms of depression comes from animal studies. Animal studies are useful in pharmacological research, whereby we can obtain results that translate into patient treatment by controlling environmental factors, especially in behavioural research. The authors systematically reviewed this issue since medical databases provide access to many primary studies. A systematic review and meta-analysis were conducted based on 25 primary studies. The studies were identified in databases such as PubMed, Embase, and Web of Science (December 2022) according to the inclusion and exclusion criteria established at the beginning of the research and published in the form of a protocol, following the PRISMA and Cochrane Collaboration methodology for secondary studies and CAMARADES (CAMARADES Berlin, QUEST-BIH Charité) for secondary studies on animals. Forest plot analyses were performed (data presented as Mean Difference, Random Model, Inverse Variance), Risk of Bias assessment (Systematic Review Center for Laboratory animal Experimentation (SYRCLE) evaluation), quality assessment of included studies (Animal research: Reporting of In Vivo Experiments (ARRIVE)), and a range of data from source publications were compiled in tabular form. The study analysed the popularity of both animal depression models (ADM) and rat strains used in pharmacological research to test the efficacy of antidepressant drugs based on the immobility time (IT) factor (Forced Swimming Test). The study examined selective serotonin reuptake inhibitors, namely fluoxetine, sertraline, paroxetine, citalopram, and escitalopram. Additionally, the study addressed issues concerning the "data availability statement" because precise IT data analysis was impossible in the case of 212 papers. Our data confirm that the Chronic Unpredictable Mild Stress (CUMS) model is the most popular and versatile model used in preclinical depression research, while the two most popular rat strains were Wistar and Sprague-Dawley. The quality of included papers based on the ARRIVE assessment showed a ratio value equal to 0.63, meaning that studies were of intermediate overall quality. The Risk of Bias assessment based on the SYRCLE tool revealed a high risk related to the blinding and the random outcome assessment. In the meta-analysis, the results indicate that all analysed drugs demonstrated efficacy in reducing IT, and the most analysed drug was fluoxetine (confirmed based on 17 studies (19 models)). The analysis of the efficacy of ADMs showed that the most effective models were CUMS, Flinders Sensitive Line (genetic model), Social Isolation, Restraint Stress, and Low-dose Lipopolysaccharide (pharmacological model). Only 2.35% (5 out of 212) of corresponding authors responded to our data request. The study highlights the dominance of the CUMS model and the Wistar and Sprague-Dawley rat strains in preclinical depression research, affirming the efficacy of SSRIs, particularly fluoxetine, in reducing IT. The findings underscore the need for better data availability and methodological improvements despite intermediate overall study quality and notable bias risks. Enhanced transparency and rigorous assessment standards are essential for advancing the reliability of animal models in depression research.

Improving realty management ability based on big data and artificial intelligence decision-making.

Realty management relies on data from previous successful and failed purchase and utilization outcomes. The cumulative data at different stages are used to improve utilization efficacy. The vital problem is selecting data for analyzing the value incremental sequence and profitable utilization. This article proposes a knowledge-dependent data processing scheme (KDPS) to augment precise data analysis. This scheme operates on two levels. Data selection based on previous stagnant outcomes is performed in the first level. Different data processing is performed in the second level to mend the first level's flaws. Data processing uses knowledge acquired from the sales process, amenities, and market value. Based on the knowledge determined from successful realty sales and incremental features, further processing for new improvements and existing stagnancy mitigation is recommended. The stagnancy and realty values are used as knowledge for training the data processing system. This ensures definite profitable features meeting the amenity requirements under reduced stagnancy time. The proposed scheme improves the processing rate, stagnancy detection, success rate, and training ratio by 8.2%, 10.25%, 10.28%, and 7%, respectively. It reduces the processing time by 8.56% compared to the existing methods.

Label-free surface-enhanced Raman spectroscopy analysis method for liquid biopsy and its application in serum-based lung cancer diagnosis and classification

Liquid biopsy offers promise for the diagnosis of malignant tumors or their precancerous lesions at the subclinical stage, which is crucial for improving the survival rate of cancer. Label-free surface-enhanced Raman spectroscopy (SERS) has become an emerging detection method in liquid biopsy. The accuracy of SERS-based diagnosis relies on both precise measurement and effective data analysis. In our previous study, a large laser spot SERS method was proposed for the precise detection of complex liquids. In this work, a logical Raman data analysis method was developed, to address the lack of Raman characteristics and biological significance in current SERS-based diagnosis data analysis. The measured Raman data is classified to obtain a weighting matrix of classification criteria, and this matrix is used to establish a material correspondence with the Raman spectra, extract key Raman peaks, determine corresponding biomolecules and biological processes, thus enabling a logical analysis of the data. This method has been used to analyze the SERS data of lung cancer, it can not only show exceptional performance in diagnosing lung cancer and differentiating small cell lung cancer, but also obtain the Raman diagnostic and classification criteria with clear numerical values and reasonable biological significance, demonstrating its great potential value in precise biosensing.

Modification of 2fn-Recuv Test Bench

The objective of this study is to modify the 2fn-recuv test bench. The test bench is used to carry out drone flight simulations. It can provide a controlled environment to ensure the drone’s safety. The test bench allows precise data collection and analysis for an unmanned aerial vehicle (UAV). First, a literature review is conducted to understand and analyse the design of the previous test bench. The limitations of CNC machines are also being studied to ensure the modified design can be fabricated. Based on the findings and results, a 3D drawing is created, taking into account the need for machining. The first prototype is made using a 3D printer. It allows for initial testing and checking for possible failures of the design. The completed part of the prototype is then fabricated using a CNC machine. The new test bench is made of aluminium to give it more rigidity and strength. Stress analysis is done using Solidworks 2020 to ensure the test bench can withstand the weight of the drone. The test bench is tested using Labview to record the angle of each movement of the drone, such as pitch, roll, and yaw. The maximum and minimum values indicate the angle of the test bench for the drone’s flight simulation.

Deep learning models and methods for solving the problems of remote monitoring of forest resources

Relevance. The need for precise data analysis in remote monitoring of Earth's forest resources through satellites and unmanned aerial vehicles. Aim. Analysis of the current research status in forest remote monitoring via satellites and unmanned aerial vehicles, formulation of directions for the prospective development of this area; implementation and investigation of new deep learning models for analyzing high and very high-resolution images of coniferous forests. Objects. Hardware, models, methods, information systems, and technologies for real-time analysis of remote monitoring data of forest resources, obtained in the form of high and very high-resolution images. Methods. Deep learning models and methods for classifying trees in images; methodology for conducting real-time remote forest monitoring; methods for training, validation, and research of convolutional neural networks. Results and conclusions. Analytical review of models, methods, and information technologies for real-time analysis of remote forest monitoring data; list of formulated directions for prospective development of methodology and tools for efficient remote forest monitoring; development of two models, Mo-U-Net and Mo-Res-U-Net, based on the classical U-Net model. Two datasets based on imagery from an unmanned aerial vehicle were created for training, validation, and research of these models. The research results were obtained for solving multiclass classification tasks of Siberian fir (A. sibirica) and Siberian pine (P. sibirica) trees infested by insect pests. The studies showed that unlike the classical U-Net model, these models provide a higher classification accuracy for all classes of A. sibirica and P. sibirica trees, including intermediate classes, with IoU and mIoU metrics above the threshold value of 0.5, indicating the practical value of such models for the forestry industry.

Analysis of Financial Data Issues in Real Estate Enterprises During the Era of Big Data

In the era of big data, financial data management has become an indispensable part of modern enterprise management for real estate enterprises, characterized by diverse sources, formats, quality, and security. By providing precise data analysis, it helps companies grasp the financial context and leads them towards more scientific and efficient management paths. In the future, as technology continues to advance, big data will play a greater role in the financial field, driving enterprises to achieve continuous innovation and development. To excel in the financial management of real estate enterprises in the big data era, it is essential to cultivate advanced data analysis talents, adopt new analytical tools, enhance the application of results, and fully leverage the advantages of big data.

Research on Corporate Brand Marketing Strategies in the Context of the Digital Economy

With the rapid development of the digital economy, new opportunities and challenges have emerged for business development. Companies seeking stable growth in the fierce industry competition must recognize the importance of brand marketing. The digital economy, with its unique appeal, provides expansive development space for corporate brand marketing. However, brand building is not an overnight achievement but a long-term process gradually shaped in consumers' minds. Thus, companies must adapt to market developments and leverage the power of the digital economy to achieve a digital transformation of brand marketing. The digital economy enables deep interaction between businesses and consumers. Through precise data analysis and personalized recommendations, companies can better understand consumer needs and enhance brand loyalty. By fully utilizing digital marketing tools, companies can quickly spread brand messages, expand brand awareness, and enhance market competitiveness. However, influenced by objective factors, certain issues arise in practice. Businesses need to transform their marketing ideas, create products with unique characteristics, find accurate brand positioning, develop targeted marketing strategies, and enhance brand competitiveness.

Statistical mirroring: A robust method for statistical dispersion estimation

This study introduces statistical mirroring as an innovative approach to statistical dispersion estimation, drawing inspiration from the Kabirian-based isomorphic optinalysis model, aimed at enhancing robustness and mitigating biases in estimation methods. Beyond scale-invariant characteristics, the proposed estimators emphasize scaloc-invariant robustness, thereby addressing a critical gap in dispersion estimation. By highlighting statistical meanic mirroring, alongside other forms of proposed statistical mirroring, the study underscores the adaptability and customization potential. Through extensive Monte Carlo simulations and real-life applications, in comparison with classical estimators, the results of the performance evaluation of the proposed estimators demonstrate robustness, efficiency, and transformations-invariance. The research offers a paradigm shift in addressing longstanding challenges in dispersion estimation, offering a new category of dispersion estimation and increased resistance to outliers. Notable limitations include selecting and evaluating the proposed statistical meanic mirroring under Gaussian and Gaussian mixture model distributions. This research paper significantly contributes to statistical methodologies, offering avenues for expanding knowledge in dispersion estimation. It recommends further exploration of proposed estimators across various statistical mirroring types and encourages comparative studies to establish their effectiveness, thereby advancing statistical knowledge and tools for precise data analysis.•The proposed methodology involves preprocessing transformations, statistical mirror design, and optimization to transform a univariate set into a bivariate one, facilitating the fitting of an isomorphic optinalysis model.•Estimators rely on a foundational bijective mapping of isoreflective pairs, deducing the probability of proximity or deviation from any defined center. This contrasts with classical estimators that utilize average or median deviations from a mean or median center.

The critical thinking research trend in Indonesia’s language education journals

In the 21st century, education aims to develop research writing skills for critical thinking, emphasizing creative, collaborative, and communicative thinking skills in all programs. The study aims to gather information about a variety of studies discussing critical thinking skills in Indonesia using content analysis in many scientific language education journals published in Indonesia. This study used content analysis. Data were obtained by collecting papers that had been published in language education publications across Indonesia from 2016 to 2023, reading carefully, classifying based on the research instrument, and data was analyzed using classical content analysis technique. The results of this study, there have been more publications over the last three years that highlight critical thinking abilities. The majority of the research design used in those papers was quantitative. Furthermore, the most targeted subjects and materials were "language acquisition" and senior high school students in their tenth grade. The most widely used data analysis technique and instrument were the t-test and the test. The study suggests future studies to enhance critical thinking skills by expanding research writing options, selecting appropriate data collection instruments, validating findings, and using precise data analysis methods.

Analysis of Nutritional Status and Physical Activity on the Level of Physical Fitness of Middle School Students in Indonesia

The aim of this research is to analyze nutritional status and physical activity on the physical fitness level of junior high school students based on secondary data or previous research journal articles. Data collection for literature reviews used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow method. Search for journal articles in literature reviews using methodological criteria (1) required variables (2) consistent data collection methods (3) valid research results (4) clear and precise data analysis. The results show (1) nutritional status is related to physical fitness, (2) physical activity is related to physical fitness. Conclusion: encouraging health sports and health education as a solution to physical fitness problems based on physical activity and nutritional status factors. External roles are very supportive in achieving physical fitness levels, such as educators and parents

Exploration of Machine Learning Model for Diabetes Prediction

Diabetes is a metabolic disorder characterized by a malfunction in insulin release, resulting in a rise in blood sugar levels in the body. Diabetes diagnosis must be made on time and precisely in order to be effective and enhance patient outcomes. The diabetes management presents a formidable challenge in modern healthcare, demanding a combination of timely interventions, precise data analysis, and personalized medical services. Furthermore, there exists a growing demand for advanced predictive models that not only provide accurate forecasts but also offer transparency and interpretability. The study objectives are to develop an innovative machine learning model for data-driven diabetes prediction and medical services. To identify the machine learning model that best suits the proposed system, a literature review related to different methods used in diagnosing diabetes is conducted. The merits and demerits of each existing system were identified to devise a proposed model that seamlessly integrates data from various sources, including blood glucose levels and patient health information. Based on the review, the study suggests an innovative machine learning model that utilizes the Explainable Decision Tree Model, leveraging cloud computing to analyse diabetes patient data and make predictions. The integration of cloud computing allows for seamless data integration from various sources. This research project represents a significant step forward in personalized diabetes care, enabling patients to proactively manage their condition while providing healthcare professionals with a powerful tool for delivering tailored medical services.

Beyond the ice: decoding Lake Mertzbakher’s response to global climate shifts

This study addresses the critical problem of understanding the changing dynamics of glacier meltwater in Lake Mertzbakher, a challenge heightened by ongoing global climate change. Employing the innovative method of the Google Earth Engine (GEE) platform, this research meticulously extracted surface water data at 60 time points during the years 2000, 2005, 2010, 2015, and 2021. This approach represents a significant advancement over previous methods by offering more frequent and precise data analysis. We incorporated meteorological factors such as temperature and precipitation to assess their influence on the monthly changes in the glacier lake area. Our findings indicate a pronounced outburst in July, leading to a substantial decrease in the lake’s area, which reaches its lowest in September. Through detailed partial regression analysis, we established a hierarchy of meteorological influences on the lake’s area, identifying minimum temperature (r = 0.245), mean temperature (r = −0.239), precipitation (r = 0.228), radiation (r = 0.154), and maximum temperature (r = 0.128) as key factors. Additionally, our use of a structural equation model unveiled the most impactful elements, with mean temperature (r = −3.320), minimum temperature (r = 2.870), radiation (r = 0.480), and precipitation (r = 0.470) leading the effects. These insights mark a substantial contribution to our understanding of glacier lake dynamics, offering crucial data for predicting and managing glacier lake floods. This study’s novel methodology and comprehensive analysis underscore its significance in enhancing disaster prevention and preparedness strategies amidst the challenges of global climate change.

Optimizing and characterizing the Timepix2 hybrid pixel detector: enhancingperformance and precision for scientific and industrial applications

The introduction of the new hybrid pixel detector Timepix2, as a successor to the well-known Timepix detector, has presented new opportunities for optimizing and characterizing this novel device. In this paper, we study the Timepix2 detector optimization and analyze its behavior, which enables better parameter setting for specific applications, resulting in enhanced device performance. Our newly developed equalization process, in conjunction with the device optimization, has led to significant improvements in the detector's accuracy and performance, facilitating more precise data collection and analysis. These advancements pave the way for the broader utilization of Timepix2 in numerous applications, such as space weather monitoring, X-ray diffraction, etc. Overall, our study provides valuable insights into the optimization and characterization of Timepix2, highlighting its potential as a powerful tool in various scientific, industrial and space applications.

Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-based IoT Sensor Communication for Remote Healthcare Analysis

In the remote healthcare industry data analytics denotes the computerization of collection, processing, and exploring complicated data to acquire finer perceptions and validate healthcare practitioners to produce familiar decisions. Healthcare basics in the modern age are vital challenges specifically in developing countries owing to the shortfall of difficult hospitals and medical professionals. As fuzzy systems have reformed several areas of work, health has also made the most of it. In this paper, the purpose of the study is to introduce a novel and intelligent remote healthcare system based on modern technologies like the Internet of things (IoT) and Neutrosophic fuzzy systems to ensure precise data analysis with lesser time and energy consumption. In this study, a novel method called, Blinder Oaxaca-based Shapiro Wilk Neutrosophic Fuzzy (BO-SWNF) data analytics for remote healthcare is designed. Data collection is performed with the WESAD dataset. Duplicated data are eliminated by Blinder Oaxaca Linear Regression-based Preprocessing model. With the application of the Blinder Oaxaca function, energy efficiency is enhanced. Finally, the Shapiro Wilk Neutrosophic Fuzzy algorithm is applied for ensuring robust data analysis. The experimental results of the proposed BO-SWNF envisage the data for finer comprehension of attribute distribution. The result is conducted by using PYHTON application to analyze stress detection with the WESAD dataset. The proposed BO-SWNF method achieved an overall accurate data analysis of 12% with minimum time ensuring 56%improvement and minimizing energy consumption by 54%.

Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems.

The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.