Data Processing Methods Research Articles

Oxidative Stress Monitoring Platform: A Longitudinal In vitro Multinuclear (1H/19F) MR Spectroscopic Study.

Glutathione (GSH) is a master antioxidant that counters oxidative stress. Clinical studies have confirmed significant depletion of GSH in the hippocampus and the substantia nigra as an early diagnostic biomarker for Alzheimer's disease (AD) and Parkinson disease (PD), respectively. External agents like anesthetics (inhaled and intravenous) have a different impact on GSH. There is significant depletion of the serum GSH peroxidase level after surgery with isoflurane anesthesia that is not found in patients administered intravenous propofol. The objective of this study is to evaluate the GSH level associated with isoflurane in vitro phantom model using non-invasive magnetic resonance (MR) spectroscopy and to detect residual isoflurane in a solution. MRS data was generated utilizing a 3T MR scanner (Prisma, Siemens) equipped with a 64-channel 1H head coil and dual tune (19F/1H) head coil. The GSH data acquisition was performed using the MEGA-PRESS pulse sequence using experimental parameters: ON = 4.40 ppm, OFF = 5.00 ppm, TE = 120 ms, TR = 2500 ms, voxel size = 25 × 25 × 25 mm and average = 32. Isoflurane was detected using 19F MRS studies using 19F/1H head coil. GSH data was processed using KALPANA package and 19F data was processed using Siemens package. The GSH peak area (without isoflurane) in a phosphate-buffered solution (PBS) solution (control) showed a slow decline over time due to natural oxidation of GSH to dimeric glutathione (GSSG). On the contrary, the GSH peak area in similar model is reduced significantly (p = 0.016) due to isoflurane induced oxidation of GSH to GSSG compared to control. We also report a concise general method for data generation and processing of 1H MRS data for GSH as well as 19F monitoring platform using 19F MR spectroscopy. This is the first report wherein both 1H and 19F spectroscopy are applied to generate MRS data along with a unique data processing method. This method is highly sensitive and specifically detects GSH without ambiguity as well as isoflurane due to the unique chemical shift patterns of CF3 and CHF2 moieties. This non-invasive MRS approach is developed to monitor GSH-isoflurane interaction leading to oxidative stress and this approach can be extended for other inhaled anesthetics. This methodology using non-invasive 19F MR spectroscopy needs further development for future clinical studies.

Hands-on training about data clustering with orange data mining toolbox.

Data clustering is a core data science approach widely used and referenced in the scientific literature. Its algorithms are often intuitive and can lead to exciting, insightful results that are easy to interpret. For these reasons, data clustering techniques could be the first method encountered in data science training. This paper proposes a hands-on approach to data clustering training suitable for introductory courses. The education approach features problem-based training that starts with the data and gradually introduces various data processing and analysis methods, illustrating them through visual representations of data and models. The proposed training is suitable for a general audience, does not require a background in statistics, mathematics, or computer science, and aims to engage the audience through practical examples, an exploratory approach to data analysis with visual analysis, experimentation, and a gentle learning curve. The manuscript details the pedagogical units of the training, motivates them through the sequence of methods introduced, and proposes data sets and data analysis workflows to be explored in the class.

Aerial Images Segmentation with Graph Neural Network

Abstract. The development of remote sensing platforms and sensors, as well as the improvement of remote data processing tools and methods, create new opportunities for automatic updating of maps. Currently, aerial photographs serve as the main source for automatic map updates due to their accessibility and significant informational value. One of the core elements for image to maps transition is accurate image segmentation. Nowadays, machine learning methods demonstrate the best results in task of image segmentation. At its core, maps represent information about a certain area in a vector form, that not only contains visual information about area, but also reflects some relations between objects in the map. This quality makes a map more convenient for human perception than an aerial photograph (raster image). This study addresses the problem of accurate aerial image segmentation with taking the advantages of using graph neural network as the more adequate model of map structure. We use graph neural network for retrieving semantic and vector information about a captured area from its aerial image. The developed framework at first phase utilizes visual transformer for retrieving deep features from the input aerial image. The graph neural network then performs clustering of the extracted deep features to obtain semantic segmentation of the image. To train and evaluate the developed framework, a special dataset is collected and annotated. It contains more than 10k aerial photographs representing various types of objects taken in different years and seasons. The evaluation results on the created dataset proved the state-of-the-art performance of the developed framework.

Soil information system of the Ukrainian Carpathians

The theoretical aspects and practical approaches to the creation of a soil information system on the example of the Ukrainian Carpathians, which is based on the use of vectorised large-scale soil mapping materials and GIS tools, are considered. The study is devoted to the development of a soil information system for the Ukrainian Carpathians, which integrates vectorised large-scale soil maps and GIS technologies to ensure efficient collection, storage and monitoring of soil data. The scientific novelty of the work is the creation of a multi-level information platform that combines data of different granularity and allows monitoring of soil transects, profiles, and genetic horizons, as well as determining spatial relationships between point and polygonal objects. Particular attention was paid to collecting cartographic materials reflecting the soil cover of the Ukrainian Carpathians and formalising the data for further integration into the soil information system. The system supports analytical tools that allow calculating various geometric, physical and chemical parameters of soil objects. The use of modern GIS tools enables accurate monitoring and forecasting of changes in soil cover under the influence of natural and anthropogenic factors, making it an important tool for the rational management of soil resources in the Carpathians. The developed system allows storing and updating information, which ensures its adaptability to changes in data analysis and processing methods. This not only increases the efficiency of monitoring, but also ensures the flexibility of the system for the specific needs of users. The practical significance lies in the possibility of using this system for soil protection, sustainable land management, and environmental safety. Expansion of this system to the entire territory of Ukraine will provide additional opportunities for effective monitoring and forecasting of changes in soil cover on a national scale, contributing to sustainable development and environmental protection. Key words: soil information systems; Ukrainian Carpathians; remote sensing; soil database.

Multimodal HSI Combined with Multiblock Data Fusion: A New Tool for the Study of Time-Dependent Alteration Processes in Dyed Textiles.

The present study describes an innovative approach for the study of time-dependent alteration processes. It combines an advanced hyperspectral imaging (HSI) system, to collect visible reflectance and fluorescence spectral data sets sequentially, with a tailored multiblock data processing method. This enables the modeling of chemical degradation maps and the early, spatially resolved detection of dye alteration in textiles. A chemometric method based on data fusion and principal component analysis was employed to identify spectral features of dye degradation, combining and enhancing information from reflectance and fluorescence HSI data. The most significant spectral profiles extracted were used to develop an asymmetric Gaussian-based pixel-by-pixel fitting model applied to the HSI fluorescence data set, enabling the reconstruction of degradation maps for rapid and intuitive visualization. In particular, changes in intensities and horizontal shift of dye emission peaks were pixel-by-pixel evaluated and fitted for the reconstruction of the degradation maps. Artificially aged wool samples tinted with indigo carmine (IC) dye served as a case study. IC is extensively used in textiles, and it is notable for its light sensitive. The results show that this approach effectively identifies spatial variations and chemical changes in dyed wool fibers, offering potential for sustainable conservation of historical textiles and other types of time-dependent processes. Thus, by amplifying variation in spectral profiles induced over time by aging, even minimal changes at early stages can be easily detected and localized, offering powerful tools for future studies on food and drug shelf life and stability, as well as forensic trace analysis.

Partial Alignment of Astrometric Position Excursions of International Celestial Reference Frame Quasars with Radio Jet Structures

Abstract Published analyses of very long baseline interferometry data for the sources included in the third International Celestial Reference Frame catalog have revealed object-specific, excess astrometric variability and quasi-coherent trajectories as functions of time. A fraction of these sources show markedly elongated distributions of positions on the sky measured with diurnal observations. Here we apply a novel statistical and data-processing method to the diurnal position measurements stretching over 40 yr to quantify the degree of elongation and its position angle, for each source with more than 200 data points. We find that 49% of the examined sources have distribution elongations in excess of 1.3. Robust uncertainties of the directions of maximal astrometric dispersion are computed by the bootstrapping method, and the results are compared with a larger catalog of radio jet directions by A. V. Plavin et al. (2022). Nearly one-half of the sources with smaller position angle uncertainties are found to have astrometric position excursions from their mean positions aligned with the radio jet structures within ±30°.

Enhancing cross-subject emotion recognition precision through unimodal EEG: a novel emotion preceptor model

Affective computing is a key research area in computer science, neuroscience, and psychology, aimed at enabling computers to recognize, understand, and respond to human emotional states. As the demand for affective computing technology grows, emotion recognition methods based on physiological signals have become research hotspots. Among these, electroencephalogram (EEG) signals, which reflect brain activity, are highly promising. However, due to individual physiological and anatomical differences, EEG signals introduce noise, reducing emotion recognition performance. Additionally, the synchronous collection of multimodal data in practical applications requires high equipment and environmental standards, limiting the practical use of EEG signals. To address these issues, this study proposes the Emotion Preceptor, a cross-subject emotion recognition model based on unimodal EEG signals. This model introduces a Static Spatial Adapter to integrate spatial information in EEG signals, reducing individual differences and extracting robust encoding information. The Temporal Causal Network then leverages temporal information to extract beneficial features for emotion recognition, achieving precise recognition based on unimodal EEG signals. Extensive experiments on the SEED and SEED-V datasets demonstrate the superior performance of the Emotion Preceptor and validate the effectiveness of the new data processing method that combines DE features in a temporal sequence. Additionally, we analyzed the model’s data flow and encoding methods from a biological interpretability perspective and validated it with neuroscience research related to emotion generation and regulation, promoting further development in emotion recognition research based on EEG signals.

ОСОБЛИВОСТІ СПРИЙМАННЯ ОКУПОВАНИХ МІСТ В УМОВАХ ВІЙНИ

Purpose of the article is to empirically determine the peculiarities of perception of occupied cities under the stress of war. Methods. To achieve this goal, the author's questionnaire “Characteristics of Perception of Occupied, Frequently Shelled and Relatively Safe Cities of Ukraine” was used, as well as psychodiagnostic methods aimed at determining: coping strategies by R. Lazarus; stress level by V. Shcherbatykh; anxiety by C. Spielberger. The methods of statistical data processing were correlation and comparative analysis. The group of respondents consisted of 148 citizens of Ukraine (23 men, 125 women) aged 14 to 57 years, who at the time of the study (in 2023) were in 19 different regions of Ukraine and in European countries. Results. The respondents showed a high degree of stress and confrontation coping strategy, as well as its connection with the subjective experience of the value of cities, their emotional significance and “ourness”. The subjective value of the occupied cities is interconnected with the attribution of suffering to their inhabitants, the tragic nature of events in them, and their perceptual value. Gender differences have been identified: women attribute a relatively higher level of “ourness”, emotional significance and value to all Ukrainian cities than men. Women are characterized by a higher level of tragedy in the perception of all the cities assessed; they are more likely to use such coping strategies as distancing, escape, seeking social support, and positive reassessment than men. Differences in stress levels were also found: this level is paradoxically lower among residents of regions closer to the front line than among residents of western regions and people abroad. Residents of the western regions of Ukraine have a lower level of attributed “belongingness” to occupied and de-occupied cities. Conclusions. Experiencing the stress of war and the confrontation coping strategy actualized by it are related to the peculiarities of perception of the occupied cities. They are perceived as tragic, subjectively valuable, and spatially larger. Women ascribe a higher level of “ourness” and emotional significance to all Ukrainian cities and at the same time show a greater variety of coping strategies than men. Residents of the regions close to the front line have lower overall levels of stress and its physical signs than Ukrainian citizens from the western regions and European countries. Residents of the western regions of Ukraine have a lower level of attributed “belongingness” to both occupied and de-occupied cities.

Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics

Background: The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. Methods: In this work, we compare the performance of multiple neural network architectures in the task of converting simulated 100 MHz NMR spectra to 400 MHz with the goal of improving the quality of the low-field spectra for analyte quantification. Multi-layered perceptron networks are also used to directly quantify metabolites in simulated 100 and 400 MHz spectra for comparison. Results: The transformer network was the only architecture in this study capable of reliably converting the low-field NMR spectra to high-field spectra in mixtures of 21 and 87 metabolites. Multi-layered perceptron-based metabolite quantification was slightly more accurate when directly processing the low-field spectra compared to high-field converted spectra, which, at least for the current study, precludes the need for low-to-high-field spectral conversion; however, this comparison of low and high-field quantification necessitates further research, comparison, and experimental validation. Conclusions: The transformer method of NMR data processing was effective in converting low-field simulated spectra to high-field for metabolomic applications and could be further explored to automate processing in other areas of NMR spectroscopy.

Analisis Isu Strategis Rancangan Teknokratik RPJMD Kabupaten Badung Tahun 2025-2029

The writing of this policy paper is to describe the formulation of strategic issues for medium-term development planning in Badung Regency in 2025-2029. This study uses a qualitative method, namely an in-depth data processing method with data from observations, and literature. There are ten strategic issues of the 2025-2029 Badung Regency RPJMD technocratic design which are produced through weighting based on their linkage with global, national and Bali Province strategic issues, namely: geopolitics and geoeconomics; conversion of agricultural land; blue economy; Environment; technological developments; population explosion; climate change and disasters; public service infrastructure; green workforce; and talent management. The suggestion that can be implemented is that the Badung Regency Government needs to carry out a more in-depth discussion on the strategic issue of the 2025-2029 Badung Regency RPJMD technocratic design together with all regional apparatus and related stakeholders so that the development of Badung Regency has the right goals and objectives and can answer development problems in the next five years.

A testing and data processing method of projectile explosion position based on three UAVs’ visual spatial constrain mechanism

To accurately obtain the position of the projectile explosion relative to the attack target, this paper propose a new method for measuring the explosive position of projectile using three UAVs’ visual spatial constrain, this method is based on the platform of the UAV equipped with a high-speed camera, and uses the UAV to control the imaging angle of the high-speed camera to form the visual rendezvous mechanism of multiple high-speed cameras. Based on obtaining the pixel coordinate position of projectile explosion in each high-speed camera, we establish the measurement geometric calculation model of the projectile explosive position, and give the coordinates calculation function of projectile explosive that relative to the attack target. To determine the projectile explosion real position, we put forward a measurement data fusion processing method to obtain the real projectile explosion position, and give the data fusion processing algorithm based on projectile explosion position data under the pairwise UAVs visual rendezvous. we also established the engineering uncertainty error calculation model of the test system. Under the testing range of 150 m × 150 m × 30 m, we verify the feasibility and correctness of the proposed test method, the results show that the method proposed in this paper is not only suitable for three-direction coordinate measurement of projectile explosive in the high altitude, but also its measurement error is less than 0.3 m compared with multi-screen optical method.

A data processing method for two-Color pyrometers in accurate temperature measurement of high-temperature flow fields

A data processing method for two-Color pyrometers in accurate temperature measurement of high-temperature flow fields

Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model

In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.

Computer Network and Database Security Technology Optimization

With the continuous development of computer network technology, its applications in daily life and work have become increasingly widespread, greatly improving efficiency. However, certain security risks remain. To ensure the security of computer networks and databases, it is essential to enhance the security of both through optimization of technology. This includes improving management practices, optimizing data processing methods, and establishing comprehensive laws and regulations. This paper analyzes the current security risks in computer networks and databases and proposes corresponding solutions, offering reference points for relevant personnel.

The Effect of Leverage, Business Risk, and Investment Decisions on Company Value with Dividend Policy as a Moderating Variable in Coal Companies Listed on the Indonesia Stock Exchange in 2020-2022

This study aims to determine the influence of leverage, business riskand investment decisions on company value with dividend policy asa moderating variable. This type of research is quantitative research.The population in this study includes all coal companies. The samplein this research was 22 companies. The sampling method used inthis research is purposive sampling, while the data processingmethod used in this research is moderated regression analysis. Theresearch results show that leverage and business risk have asignificant negative effect on company value. Investment decisionshave no effect on company value. Dividend policy as a moderatingvariable weakens the influence of leverage and investment decisionson company value. Dividend policy as a moderating variable is ableto strengthen the influence of business risk on company value.

Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization.

Generalizing real-world data has been one of the most difficult challenges for application of machine learning (ML) in practice. Most ML works focused on improvements in algorithms and feature representations. However, the data quality, as the foundation of ML, has been largely overlooked, also leading to the absence of data evaluation and processing methods in ML fields. Motivated by the challenge and need, we selected an important but difficult reorganization energy (RE) prediction task as a test platform, which is an important parameter for the charge mobility of organic semiconductors (OSCs), to propose a data-quality-navigated strategy with chemical intuition. We developed a data diversity evaluation based on structure characteristics of OSC molecules, a reliability evaluation method based on prediction accuracy, a data filtering method based on the uncertainty of K-fold division, and a data split technique by clustering and stratified sampling based on four molecular descriptor-associated REs. Consequently, a representative RE data set (15,989 molecules) with high reliability and diversity can be obtained. For the feature representation, a complementary strategy is proposed by considering the chemical nature of REs and the structure characteristics of OCS molecules as well as the model algorithm. In addition, an ensemble framework consisting of two deep learning models is constructed to avoid the risk of local optimization of the single model. The robustness and generalization of our model are strongly validated against different OSC-like molecules with diverse structures and a wide range of REs and real OSC molecules, greatly outperforming eight adversarial controls. Collectively, our work not only provides a quick and reliable tool to screen efficient OSCs but also offers methodological guidelines for improving the generalization of ML.

Enhancing Multi-Hole Pressure Probe Data Processing in Turbine Cascade Experiments Using Structural Risk Minimization Principle

Multi-hole pressure probes are crucial for turbomachinery flow measurements, yet conventional data processing methods often lack generalization for complex flows. This study introduces an innovative approach by integrating machine learning techniques with the structural risk minimization (SRM) principle, significantly enhancing the generalization capability of regression models. A comprehensive framework has been developed, combining SRM-based machine learning regression methods, such as ridge regression and kernel ridge regression, with hyperparameter optimization and S-fold cross-validation, to ensure robust model selection and accuracy. Validated using the McCormick function and applied to VKI-RG transonic turbine cascade measurements, the SRM-based methods demonstrated superior performance over traditional empirical risk minimization approaches, with lower error ratios and higher R2 values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a 0.63122° discrepancy in exit flow angle predictions, guiding the probe design and calibration strategies. This study presents a holistic workflow for improving multi-hole pressure probe measurements under high-subsonic conditions, representing a meaningful enhancement over traditional empirical methods and providing valuable references for practical applications.

Data Processing of Used Car Export Service Flow Based on Zookeeper + Kafka

The used car export industry has shown a trend of rapid growth in recent years. The export of used cars involves data pertaining to vehicle information, market demand, logistics, customs, and numerous other aspects. These data are constantly generated in the form of streams, posing challenges to the data processing capabilities of service platforms. Traditional data processing methods struggle to meet the requirements of real-time and large-scale data processing, thus necessitating an efficient streaming data processing architecture. Based on this, this paper proposes a streaming data processing scheme for a used car export service platform utilizing Zookeeper and Kafka. It describes the architecture design and data processing flow, analyzes the advantages of the scheme in terms of reliability, scalability, and real-time performance, and demonstrates its effectiveness through experiments and case studies.

Kanban method in digital data processing

AbstractCurrent digital education operates as a push-system, despite the content and subject characteristics suggesting a need for a pull-system. One of the features of the push-system is a tool-centered approach, where the focus on tools, including both hardware and software, is the belief that it is possible to build knowledge inventory. The consequences of the push digital education approaches are that (1) the fundamental concepts of Computer Science are not being transferred, (2) the lack of the development of supporting methods, (3) and education does not seem to be interested in revealing the root causes and to be open for fundamental changes. This paper proposes the extension of the theory of the industrial pull systems to present as a potential solution to increase the effectiveness and efficiency of digital education and reduce or eliminate data processing inefficiencies generated by undereducated but misled end-users. In addition to theoretical discussions, it also delves into the detailed analysis, design, implementation, and testing of a real-world data processing problem. The presented problem, its analysis, solution, and accompanying discussion reveal how one of the tools (kanban) of lean production can be adapted to support the Just-in-Time philosophy in digital education, focusing on end-user programming.

Innovative development of territories based on the integrated use of social, resource and environmental potential

This article investigates the interrelationships and interdependencies inherent in the integrated use of the territory’s social, resource, and environmental potential to achieve its sustainable development. The basic hypotheses formulated during this study were as follows: Sustainable development of the territory’s resource and ecological potential cannot be achieved if its social potential is reduced; Sustainable development of the territory’s ecological and social potential can be achieved only if it is economically feasible, that is if it contributes to the sustainable development of the territory’s resource potential; Sustainable development of the territory’s resource and social potential cannot be achieved while its ecological potential deteriorates; Ensured sustainable development of territories in today’s world is only possible with simultaneous integrated use of the territory’s social, resource, and environmental potential. The aim of this research is to investigate the interrelationships among these three components and to determine their impact on the overall development of the regions. A comprehensive multi-stage research project based on the analysis of statistical information and survey results was designed and applied to meet the research objective. Methodologically, the study was based on a quantitative approach, which led to the integrated use of economic-statistical and econometric data processing methods. All these hypotheses were tested and found to be true in the Kizilsu Kyrgyz Autonomous Prefecture of the People’s Republic of China. The results indicate a direct and stable relationship between the development of resource potential, ecological potential, and social potential of the territories. Specifically, regions with high resource potential exhibit better social indicators, while ecological investments contribute to the improvement of public health and quality of life. The originality of this study lies in its comprehensive approach to examining the interconnections among resources, social aspects, and ecological sustainability, thereby contributing to the international literature in the field of sustainable development. These results can be of use to state and municipal government experts when planning territorial development, as well as to academic researchers, including to identify promising avenues for later research.