Automatic Data Processing Research Articles

A Chinese Knowledge Graph Dataset in the Field of Scientific Fitness

To promote the development of scientific fitness research and practice, we propose the Chinese Knowledge Graph Dataset in the Field of Scientific Fitness (FitKG-CN). This knowledge graph contains over 10,000 fitness-related terms, categorized into eight main groups: body parts, items of exercise, fitness movement, equipment and tools, exercise goals, anatomical structures, nutrients, and technical terms. The construction of FitKG-CN is based on authoritative data sources, undergoing rigorous preprocessing, including noise removal, format standardization, and normalization of entities and relationships. The data is manually annotated on a professional platform and ultimately stored in a Neo4j graph database for visualization. Additionally, we trained a Chinese SpERT model using the manually annotated data to enhance the automation of data processing. The experimental results show that the model achieved an F1 score of 94.05% in entity recognition tasks and 82.00% in relation extraction tasks, validating the effectiveness of the model and improving the scalability of the dataset.

Closing the phenotyping gap with non-invasive belowground field phenotyping

Abstract. Breeding climate-robust crops is one of the needed pathways for adaptation to the changing climate. To speed up the breeding process, it is important to understand how plants react to extreme weather events such as drought or waterlogging in their production environment, i.e. under field conditions in real soils. Whereas a number of techniques exist for aboveground field phenotyping, simultaneous non-invasive belowground phenotyping remains difficult. In this paper, we present the first data set of the new HYDRAS (HYdrology, Drones and RAinout Shelters) open-access field-phenotyping infrastructure, bringing electrical resistivity tomography, alongside drone imagery and environmental monitoring, to a technological readiness level closer to what breeders and researchers need. This paper investigates whether electrical resistivity tomography (ERT) provides sufficient precision and accuracy to distinguish between belowground plant traits of different genotypes of the same crop species. The proof-of-concept experiment was conducted in 2023, with three distinct soybean genotypes known for their contrasting reactions to drought stress. We illustrate how this new infrastructure addresses the issues of depth resolution, automated data processing, and phenotyping indicator extraction. The work shows that electrical resistivity tomography is ready to complement drone-based field-phenotyping techniques to accomplish whole-plant high-throughput field phenotyping.

Intangible Capital: Digital Colors in Romanesque Cloisters

This paper explores the possibility of counteracting the crisis of culture and institutions by investing in the identity values of the user-actor within digital spaces built for the purpose. The strategy is applied to the analysis of three Catalan cloisters (Spain), with a focus on the representation of the cloister of Sant Cugat (Barcelona). Heuristic picklocks are found in the semantic richness proposed by Marius Schneider exclusively on the verbal level. The authors interpret the contents and transcribe them into graphic signs and digital denotations of sounds and colors. They organize proprietary ontologies, or syntagmatic lines, to be entrusted to the management of computer algorithms. The syncretic culture that characterized the medieval era allowed the ability to mediate science and faith to be entrusted to the mind of the praying monk alone in every canonical hour. The hypothesis that a careful direction has programmed the ways in which to orient souls to “navigate by sight” urges the authors to find the criteria that advanced statistics imitates to make automatic data processing “Intelligent”. In step with the times and in line with the most recent directions for the Safeguarding of Heritage, the musical, astral, and narrative rhythms feared by Schneider are used to inform representative models, to increase not only the visual perception of the user (XR Extended Reality) but also to solicit new analogies and illuminating associations. The results return a vision of the culture of the time suitable for shortening the distances between present and past, attracting the visitor and, with him, the resources necessary to protect and enhance the spaces of the Romanesque era. The methodology goes beyond the contingent aspect by encouraging the ‘remediation’ of contents with the help of machine learning.

Анализ отзывов пациентов с использованием машинного обучения и лингвистических методов

With the advancement of digitalization, traditional methods of surveying consumers to assess their satisfaction with service quality are being replaced by approaches based on the automatic processing of text data from social media. This study aims to determine the degree of patient satisfaction with the quality of medical services by developing and testing an algorithm for classifying Russian-language text reviews collected from social media platforms. The focus is on analyzing the sentiment (positive/negative) of patient reviews about medical institutions and doctors, as well as identifying the review's subject—either the quality of medical services provided or the organization of patient care by the institution. A method was developed for classifying text reviews about the work of medical institutions posted by patients on two Russian doctor review platforms. Approximately 60,000 reviews were analyzed. Machine learning techniques, including various artificial neural network architectures, were tested. The classification algorithm demon-strated high efficiency, with the best performance achieved using a recurrent neural network architecture (accuracy = 0.9271). Incorporating named entity recognition into text analysis further enhanced the classification efficiency across all neural network-based classifiers. To improve classification quality, the study highlights the need for semantic seg-mentation of reviews by their subject and sentiment, followed by the separate analysis of these fragments.

Experimental study of the possibility of detecting contamination of high-voltage insulators with the help of a thermal imager

ACTUALITY. External insulation is one of the most damaged elements of high-voltage equipment. Changes in climate, ecology have an increasing impact on it’s condition. The proportion of overhead transmission line (OTL) outages due to pollution varies by region and specific operating conditions, but on average it can range from 20% to 40% of all OTL failures. In regions with high levels of industrial pollution or near sea coasts, this figure can reach 50% or more. The main causes of outages are related to the accumulation of contaminants on insulators, which leads to overtopping in wet conditions. It is precipitation such as drizzle, fog or dew that has the greatest impact, combined with various solid conductive particles deposited on the insulator surface from the air, and forming a layer of surface contamination. Methods for diagnosing insulation are various, including thermal imaging. OBJECT. To study the effect of wetted contaminated surface of glass and polymer insulators on their temperature under laboratory conditions and to evaluate the possibility of diagnosing the contaminated condition. METHODS. Three dozen laboratory experiments with suspended glass and polymer insulators in a fog chamber with thermograms, control of leakage current and degree of contamination were carried out to achieve the goal. During the experiments, the degree and method of contamination and humidification conditions were changed. RESULTS. In the course of laboratory studies it was shown that thermal imaging cameras can diagnose even lightly contaminated insulation in the wetted state. The mean and rms values of temperature contrasts depend more on the level of contamination (greasiness) than on the degree of uniformity of contamination application. The methodology of construction of the insulation control system on overhead power lines and the algorithm of automated data processing are proposed.

A conceptual model for multivariate data integration in reservoir modeling: exploring the role of neural networks in petrophysical analysis

Reservoir modeling is crucial for optimizing oil and gas production, as it helps characterize subsurface properties like porosity, permeability, and fluid saturation. Integrating diverse data sources such as well logs, seismic data, and core analyses presents a significant challenge due to their differing scales, resolutions, and data types. Traditional methods often struggle to accurately integrate these data sources, resulting in suboptimal predictive accuracy. This review proposes a conceptual model that leverages neural networks to integrate multivariate data for enhanced reservoir characterization. Neural networks, with their ability to handle nonlinear relationships and large, complex datasets, offer a transformative approach to unify diverse data inputs, resulting in more accurate predictions of reservoir properties. The model focuses on using feedforward neural networks (FNNs), convolutional neural networks (CNNs), and autoencoders to merge well logs, seismic interpretations, and core sample data. This integration aims to improve the identification of productive zones, reservoir boundaries, and fluid distributions, thereby enhancing reservoir models. Additionally, the review explores how neural networks can manage data gaps, predict missing values, and quantify uncertainty, which are common challenges in reservoir studies. By automating data processing and facilitating real-time analysis, neural networks can accelerate decision-making in reservoir management and field development. This framework highlights the potential of AI-driven techniques to revolutionize reservoir modeling, offering a pathway to more efficient, accurate, and adaptive resource management. The conceptual model aims to advance predictive capabilities in reservoir analysis, addressing the complexities of multivariate data integration and opening doors for future innovations in the energy industry. Keywords: Conceptual Model, Multivariate Data, Reservoir Modeling, Petrophysical Analysis.

المواجهة الجنائية للقرصنة الرقمية للبطاقة المصرفية وسبل الوقاية منها بالتشريع المغربي

Cybercrime has evolved in light of the rapid pace of the electronic revolution that has touched all aspects of life. This progress has not been without its drawbacks, as various financial and banking institutions have been affected by the increase in criminal activities that have exploited the rapid pace of e-commerce and the use of bank cards as a tool for monetary transactions. This paper focuses on the digital hacking crimes that have affected bank cards, which is one of the serious forms of electronic hacking that threatens the security of personal data and banking security according to Moroccan legislation and means of prevention. The study addressed the concepts of cybercrime and digital hacking, and discussed Moroccan legislation related to the offence of bank card hacking, starting with the law on terrorism, Law 03-07 supplementing the Criminal Code and Law 43-20, and legal approaches to this offence. One of the most important findings of the research is that the Moroccan legislator, in order to better protect automated data processing systems, updated its legal arsenal by issuing Law No. 07-03, under which the offence of bank card hacking falls within the scope of the offences of fraudulent access, forgery and information fraud. The problem is manifested in the multiplicity of texts criminalising and punishing this offence and the overlap between them, which makes them all applicable due to their multiple descriptions. Draft Law No. 03. 23, amending and supplementing the Code of Criminal Procedure, addresses many important issues related to this subject. The study recommends strengthening and updating the legal system in accordance with the latest developments and best practices, considering fair trial guarantees. The study also recommends strengthening the technical preventive role and professional training in the competent authorities and updating it regularly to keep abreast of the latest security threats and address them effectively, develop awareness and education campaigns, and strengthen institutional, regional and international cooperation.

Using machine learning to predict disease outbreaks and enhance public health surveillance

Disease outbreaks pose significant challenges to public health systems, often requiring rapid response strategies to mitigate widespread health and economic impacts. Traditional methods of outbreak prediction and surveillance, while effective, often lack the capacity to process and analyse the vast quantities of heterogeneous data generated in modern healthcare ecosystems. Machine learning (ML) offers transformative potential in this domain, leveraging its ability to process large datasets, identify complex patterns, and provide real-time insights. By integrating diverse data sources such as electronic health records (EHRs), social media feeds, climate data, and genomic sequences, ML algorithms can predict disease outbreaks with unprecedented accuracy. Supervised learning models, for instance, have been successfully applied to forecast influenza trends, while unsupervised clustering techniques have been employed to detect anomalies indicative of emerging infectious diseases. Moreover, ML facilitates advanced public health surveillance by automating data processing pipelines, enhancing real-time monitoring capabilities, and enabling resource optimization for outbreak responses. Despite these advances, the adoption of ML in public health surveillance is not without challenges. Issues related to data privacy, ethical considerations, algorithm interpretability, and integration with existing public health infrastructures remain significant hurdles. Addressing these challenges requires a multidisciplinary approach, incorporating robust data governance frameworks, improved algorithm transparency, and collaborations between technology developers and public health stakeholders. This paper highlights the critical role of ML in transforming public health surveillance, focusing on its application in disease outbreak prediction. It underscores the importance of continued innovation, regulatory support, and ethical considerations in advancing ML-driven solutions for global health security.

Behavioral Intervention Functions in Type 2 Diabetes Apps: Literature Review.

As type 2 diabetes (T2D) is expected to increase, self-management becomes more crucial. Mobile apps are increasingly supporting self-management with tasks like blood glucose monitoring and medication management. Understanding the behavioral intervention functions used by diabetes apps today, is essential for improving future apps and systems for diabetes management. To analyze the behavioral intervention functions used in apps for managing T2D that integrate the three main elements: medication management, nutrition tracking, and blood glucose management. We conducted a literature review on T2D diabetes apps using SCOPUS, PubMed, and PsycINFO. After screening and removing duplicates, we analyzed app details and behavioral intervention functions based on the Behavior Change Wheel (BCW) framework. We reviewed 644 scientific publications describing diabetes apps in clinical studies, narrowing it down to 20 studies, including 16 unique apps, after screening and exclusions. These studies were published between 2016 and 2024. Among the identified apps, automatic processing of medication data was reported in one study, while blood glucose data were automatically processed in 13 studies. Nutrition data processing varied. Most apps used Enablement and Persuasion as behavioral intervention functions, with Education and Training reported less frequently. Environmental Restructuring, Incentivization, Coercion, Restriction, and Modeling were not reported as being used in any studies. This review shows that while Enablement and Persuasion are common, other behavioral intervention functions seem to be underused or underreported. Future research could explore the potential of integrating additional behavioral intervention functions to enhance diabetes app efficacy and users' self-management.

Leveraging Artificial Intelligence and Reverse Tip Sample Configuration for Automation of Data Processing in Quantitative Scanning Spreading Resistance Microscopy

Scanning probe microscopy (SPM) has become a vital metrology tool for characterizing nanoscale devices with exceptional spatial resolution, driving advances in various fields. However, its low overall throughput remains a major limitation. To address this, the high‐efficiency data acquisition capabilities of reverse tip sample (RTS) SPM are combined with automated data processing via artificial intelligence (AI)‐based computer vision algorithms. The effectiveness of this approach is demonstrated through a case study of scanning spreading resistance microscopy (SSRM). YOLO (You Only Look Once) models are trained to detect each layer in SSRM resistance maps of a calibration sample, serving as a key step in automating the quantitative SSRM data processing workflow. Models trained on mixed datasets of standard and RTS SPM images (ratio 1:4) achieve an excellent accuracy of 97.8%, while reducing the data collection time fivefold compared to using solely standard calibration datasets. Additionally, the model's strong ability to effectively recognize and exclude measurement artifacts during layer selection further enhances its suitability for real‐world applications. This work significantly accelerates the SSRM data analysis by automating the workflow and highlights the potential of RTS SPM as a high‐throughput solution for generating AI training data, facilitating faster AI model deployment in SPM applications.

Machine Learning Application for High-Speed FTIR Absorption Spectra Analysis

"With this work our main objective is to find the best general (baseline) model for analyzing unknown spectra using Fourier infrared transformed spectroscopy (FT-IR) coupled with machine learning (ML) algorithms. This goal allows us to identify the best methodology applied for inline analysis of different experimental spectra for qualitative structural information obtained with types of structures that generate absorption or emission peaks. This methodology opens new perspectives for automated data processing using flexible algorithms and machine learning to encode experimental data for future applications. The results provide a good perspective on Machine Learning algorithms for applied sciences research. For our case study (FTIR experimental data) our model allows for peak feature extraction with a relative low, close to machine standard deviation, error budget. The best model identified is a specialized model but the standard, fully connected network models are evaluated."

Versatile MRI acquisition and processing protocol for population-based neuroimaging.

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

Classification Model for Effective Employee Segmentation

In this work, an efficient classification model for staff segmentation is developed. The ensemble is based on machine learning principles, allowing the exploration of the performance of various classification methods and the tuning of hyperparameters to optimize system performance. Additionally, it provides the ability to compare the metric results of trained models, enabling the selection of the best strategy for each problem. The work considers an efficient and automated data processing pipeline, which includes data collection, cleaning, and transformation processes that can be applied in various fields where efficient data processing is required.

Cerebrovascular reactivity as a candidate biomarker in small vessel disease related VCID

AbstractBackgroundThe brain’s ability to perform a cognitive task is a dynamic process and requires small blood vessels to dilate or constrict in real time to adjust blood flow in a region‐specific manner. Cerebrovascular Reactivity (CVR) measures the ability of vessels to react to vasoactive challenges. In this work, we investigated the role of CVR as a possible biomarker in small vessel disease related vascular contributions to cognitive impairment and dementia (VCID), as part of the NINDS‐funded MarkVCID study.MethodsThis work consisted of 4 studies. CVR was measured by MRI signal changes in response to inhalation of 5% carbon dioxide (CO2) (Figure 1). Study 1 was a single‐site study of 72 participants (aged 69±8 years, 28 normal cognition and 44 cognitive impairment), in which we examined the relationship between CVR and cognitive function, CSF‐measured AD pathological markers, and CDR‐sum‐of‐boxes. Study 2 conducted an instrumental validation to benchmark the test‐retest reproducibility of the CVR measure. Study 3 developed an automatic, cloud‐based tool for automatic and standardized processing of CVR MRI data. Study 4 was a multi‐site biological validation study in which CVR was measured across 3 sites and data from each site were separately analyzed to investigate the relationship between CVR and cognitive function.ResultsStudy 1 revealed that whole‐brain CVR was associated with MoCA (β=29.64, 95% CI, 9.94 to 49.34) and global composite scores (β=4.32, 95% CI, 0.05 to 8.58), and these associations were independent of CSF‐measured amyloid and tau markers. CVR was also associated with CDR‐SB score. Study 2 revealed that inter‐scanner CoV of CVR was 6.90 ± 5.08% and had an ICC of 0.8498. Study 3 showed that CVR processing can be standardized and the processing pipeline can be accessed by any researchers around the world through an installation‐free cloud platform referred to as MRICloud (https://braingps.mricloud.org/cvr.v5). Study 4 showed that CVR was significantly associated with MoCA in data analyzed separately in each of the three sites through a pre‐defined analysis (p=0.003, 0.002, 0.046 for the three sites, respectively; when pooling data together p=3.7*10^‐6) (Figure 2).ConclusionCVR measured with MRI is a promising candidate biomarker in VCID.

Non-invasive recording of heartbeats in Danio rerio and Daphnia magna to assess the toxicity of imidacloprid and glyphosate

Non-invasive optical registration and subsequent analysis of heart rate (HR) and heart rate variability (HRV) in transparent aquatic animals have recently been proposed as convenient toxicological endpoints, well-suited for automation data acquisition and processing. This approach was evaluated in experiments involving juvenile Daphnia magna and zebrafish (Danio rerio) embryos exposed to glyphosate solutions (20 mg/L, 2 mg/L, 0.2 mg/L, and 0.02 mg/L) and imidacloprid solutions (30 mg/L, 3 mg/L, 0.3 mg/L, and 0.03 mg/L). The findings indicate that cardiac performance assessment is a promising approach for short-term toxicity evaluation. However, the sensitivity of this physiological endpoint to various external factors may limit its broader application. Results from the two model species highlight their differing sensitivities to the tested substances, emphasizing the need for thorough preliminary studies before establishing this method as a standardized toxicological tool. The potential development and improvement of techniques for assessing heart rate in zebrafish and daphnids are discussed.

Mapping the trajectories of digital technology innovation in the construction industry: an integrated SPNP and Louvain approach

PurposeDespite the continuous development and application of new digital technologies in the construction industry, there has been little research on digital technology trajectories in the construction industry. The study addresses the issue faced by the construction industry in exploring digital technology trajectories: how to comprehensively identify and analyse digital technology pathways across multiple technology fields in the construction industry.Design/methodology/approachFirstly, the digital technology patent identification and classification method based on text mining is used to identify digital technology patents and construct a digital technology innovation network. Second, the main path of the digital technology innovation network is identified with the help of SPNP. Then, the subpaths of the digital technology innovation network are identified with the help of the Louvain algorithm and SPNP. Finally, starting from the technology nodes where the main path and subpaths intersect, the technological similarity of the paths is analysed to explore the evolutionary characteristics of the technology trajectories. In light of this, the developed method is applied to the global construction industry patent dataset to analyse the trajectories of digital technologies.FindingsThe technological innovation path in the construction industry starts with construction materials and gradually expands to intelligence, automation and digital data processing technology. Equipment and devices with electronic digital data processing capabilities as well as improvements in green building technologies and user experience-enhancing technologies, may be the future of the construction industry. With the increasing demand for green buildings and intelligent buildings, the direction of digital technology innovation in the construction industry is gradually tilted towards these areas. In addition, influenced by geographic and economic factors, there is a spatial clustering effect of digital technology innovation in the construction industry.Research limitations/implicationsFuture research should analyse in depth the performance of different countries and regions in digital technology innovation and explore the root causes, motivations and influencing factors behind it, such as the policy environment, the level of the economy and the investment in research and development. Exploring the reasons affecting digital technology innovation can help formulate more targeted policies and promote cooperation and exchange of digital technology innovation in the global construction industry. Meanwhile, to solve the problems of overly broad IPC categorization and the difficulty of accurately describing cross-field innovations, combining IPC co-occurrence networks with patent citation networks is an effective strategy. This strategy can track technologically interrelated patents and provide more specific contents to know the advantages and challenges of the construction industry in the field of digital technology innovation.Practical implicationsThe study has practical implications for the construction industry. The identification of digital technology innovation trajectories provides valuable insights for industry firms and research institutes. It helps them understand the current and future directions of digital technology in construction, enabling them to stay at the forefront of technological advancements. The findings highlight the importance of focusing on areas such as solar energy utilisation, green energy, intelligence, automation and data applications. This knowledge can guide firms in developing new building materials, incorporating digital information technologies and enhancing user experiences. The study’s results can inform strategic decision-making, technology adoption and innovation management in the construction sector.Social implicationsThe social implications of this study are significant for various stakeholders. The identification of digital technology innovation trajectories in the construction industry highlights the potential benefits for society. The focus on green energy, intelligent buildings and enhanced user experiences aligns with the increasing demand for sustainability, energy efficiency and comfortable living environments. These technological advancements can contribute to reducing environmental impact, improving quality of life and promoting sustainable development. The findings can inform policymakers, urban planners and architects in shaping regulations, designing sustainable cities and creating buildings that prioritize energy efficiency and user well-being. Ultimately, the study’s social implications aim to foster a more sustainable and livable built environment.Originality/valueAn identification method integrated with SPNP and the Louvain algorithm is developed to map digital technology innovation trajectories in the construction industry. This study helps to reveal the trajectories of digital technology innovation, provides new perspectives, insight and ideas for research in related fields and has great potential for applications in practice to promote the innovation and development of the construction industry.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

A Proposed Method of Automating Data Processing for Analysing Data Produced from Eye Tracking and Galvanic Skin Response

The use of eye tracking technology, together with other physiological measurements such as psychogalvanic skin response (GSR) and electroencephalographic (EEG) recordings, provides researchers with information about users’ physiological behavioural responses during their learning process in different types of tasks. These devices produce a large volume of data. However, in order to analyse these records, researchers have to process and analyse them using complex statistical and/or machine learning techniques (supervised or unsupervised) that are usually not incorporated into the devices. The objectives of this study were (1) to propose a procedure for processing the extracted data; (2) to address the potential technical challenges and difficulties in processing logs in integrated multichannel technology; and (3) to offer solutions for automating data processing and analysis. A Notebook in Jupyter is proposed with the steps for importing and processing data, as well as for using supervised and unsupervised machine learning algorithms.

Extracting Data from Medical Records for Monitoring Diseases and Generating Medical Alerts

Background: Automated data processing is creating and implementing technology that automatically processes data. This computer tool is recommended for doctors because it supports their everyday work, assists in medical diagnosis, and enhances patient care. The aim of this paper is to propose an informatic tool that can extract the values of some parameters of interest from blood test sheets in order to get medical alerts and to monitor the chronic disease. Methods: An application, written in Python, was developed Results: The computer tool extracted automatically the values of glucose, triglycerides, HDL-cholesterol, total cholesterol, and LDL-cholesterol from medical sheets (text-based file or graphic file, respectively), and saved them in a database, accessed and represented in a graphic form the most recent values of these parameters; alerts according to metabolic syndrome criteria and Framingham risk score were generated. Conclusions: This tool contributes to the management of the medical process, saving precious time and helping the doctor in detecting current or future health problems.

Modern risk assessment of the influences of natural and military man-made factors on the state of the historical buildings of the Kyiv-Pechersk Lavra

During its thousand-year history, the Kyiv-Pechersk Lavra has experiences many disasters. At the time when the fight against religion was part of the state ideology, the cathedral and monastery were closed, and its countless treasures were confiscated. Even in the turbulent 1930s, when the bolsheviks destroyed dozens of unique temples and churches in Kyiv, the Assumption Cathedral remained intact. Black days for the age-old shrine came during the German occupation in the fall of 1941. First, the shrine was looted, and many material and artistic valuables were taken. In November 1941, a series of explosions destroyed the main temple of the Kyiv-Pechersk Lavra, which had served as a tomb for the Kiev princes. In February 2022, the war had started again in our country, and modern scientific technology allows us to assess the impact of military events on buildings of Kyiv-Pechersk Lavra from viewpoint of the level of seismic hazard assessment, which have lasted in Ukraine for more than two years, because Kyiv-Pechersk Lavra is listed as a UNESCO World Heritage Site. The article presents the results of the analysis of the operation of devices for various purposes ― from the recording of seismic-acoustic waves, which have a high frequency of natural oscillations, to low-frequency ones, which are recorded by a laser inclinometer. The article also presents the results of modern automated data processing using various methods ― from the spectral method of signal recognition to the modern autopicker method of determining the time of arrival of waves AIC, which allows you to clearly determine the arrival time of vibration waves of various origins ― from the formation of cracks in historical buildings of the Kyiv-Pechersk Lavra to vibration waves of technical and natural origin.