Standard Data Processing Research Articles

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

CROST: a comprehensive repository of spatial transcriptomics.

The development of spatial transcriptome sequencing technology has revolutionized our comprehension of complex tissues and propelled life and health sciences into an era of spatial omics. However, the current availability of databases for accessing and analyzing spatial transcriptomic data is limited. In response, we have established CROST (https://ngdc.cncb.ac.cn/crost), a comprehensive repository of spatial transcriptomics. CROST encompasses high-quality samples and houses 182 spatial transcriptomic datasets from diverse species, organs, and diseases, comprising 1033 sub-datasets and 48043 tumor-related spatially variable genes (SVGs). Additionally, it encompasses a standardized spatial transcriptome data processing pipeline, integrates single-cell RNA sequencing deconvolution spatial transcriptomics data, and evaluates correlation, colocalization, intercellular communication, and biological function annotation analyses. Moreover, CROST integrates the transcriptome, epigenome, and genome to explore tumor-associated SVGs and provides a comprehensive understanding of their roles in cancer progression and prognosis. Furthermore, CROST provides two online tools, single-sample gene set enrichment analysis and SpatialAP, for users to annotate and analyze the uploaded spatial transcriptomics data. The user-friendly interface of CROST facilitates browsing, searching, analyzing, visualizing, and downloading desired information. Collectively, CROST offers fresh and comprehensive insights into tissue structure and a foundation for understanding multiple biological mechanisms in diseases, particularly in tumor tissues.

A dataset of soil water and heat dynamics from Pinus massoniana plantations under close-to-nature management in South Subtropics (2016–2021)

This study was conducted at the Tropical Forestry Experimental Center of the Chinese Academy of Forestry in Youyiguan Pingxiang Chongzuo Forest Ecosystem Observation and Research Station of Guangxi. Based on the automatic meteorological observation station, we monitored and analyzed the soil hydrothermal dynamics in Pinus massoniana plantations under close-to-nature management. The sample plots layout, index monitoring, and data quality control were carried out in accordance with relevant specifications. This dataset contains the data of soil temperature, soil volume water content, soil heat flux, as well as air temperature and air humidity in Pinus massoniana plantations of close-to-nature management in South Subtropics from 2016 to 2021. After standard data processing, including outlier elimination and data interpolation, we statistically derived the daily data of soil water and heat dynamics of Pinus massoniana, and explained the construction of the dataset. The establishment and sharing of this dataset are valuable for facilitating a comprehensive understanding of the characteristics of soil water and heat in Pinus massoniana plantations under close-to-nature management in South Subtropics. Furthermore, the dataset can serve as an important resource for in-depth research on the response of mixed coniferous broad-leaved forest to global climate change. It can provide essential to support the sustainable management and conservation of forest resources in South Subtropics.

Examining Challenges In The Adoption of Big Data In Health Care Institutions And Its Impact On Patients Satisfaction: An empirical study in Delhi, India

The study aims to investigate the obstacles and factors influencing the adoption of big data in healthcare organizations, and its subsequent impact on patient satisfaction. Big data in healthcare refers to the collecting, analysis, and use of clinical data from patients that is too massive or complex to be grasped by standard data processing methods. Adopting big data in health care will enable manages to render services to patient and customer satisfaction. However, in the health care sector, firms must overcome several hurdles and problems by adopting new technology. A detailed literature review was undertaken to examine many obstacles associated with the use of Big Data. A well-structured questionnaire was prepared in Likert scale to find the elements that influence big data adoption and its impact on patient satisfaction. To evaluate factors, exploratory factor analysis using SPSS 21 was performed, and Structural Equation Modelling (SEM) was performed to assess key significant factors that impact patient satisfaction. The data was gathered from employees associated with the hospitals. The survey received responses from 212 participants. Following the analysis of the data, it was found that five challenging factors influences big data adoption. These are data integration, data understanding, technology and infrastructure, lack of expert and regulation barrier. These factors explained 70.36% of variance. Whereas, SEM analysis indicated that both data integration, data understanding and lack of expertise significantly affect big data adoption Furthermore, big data adoption in hospitals will help in improving patient satisfaction.

VariantscanR: an R-package as a clinical tool for variant filtering of known phenotype-associated variants in domestic animals

BackgroundSince the introduction of next-generation sequencing (NGS) techniques, whole-exome sequencing (WES) and whole-genome sequencing (WGS) have not only revolutionized research, but also diagnostics. The gradual switch from single gene testing to WES and WGS required a different set of skills, given the amount and type of data generated, while the demand for standardization remained. However, most of the tools currently available are solely applicable for human analysis because they require access to specific databases and/or simply do not support other species. Additionally, a complicating factor in clinical genetics in animals is that genetic diversity is often dangerously low due to the breeding history. Combined, there is a clear need for an easy-to-use, flexible tool that allows standardized data processing and preferably, monitoring of genetic diversity as well. To fill these gaps, we developed the R-package variantscanR that allows an easy and straightforward identification and prioritization of known phenotype-associated variants identified in dogs and other domestic animals.ResultsThe R-package variantscanR enables the filtering of variant call format (VCF) files for the presence of known phenotype-associated variants and allows for the estimation of genetic diversity using multi-sample VCF files. Next to this, additional functions are available for the quality control and processing of user-defined input files to make the workflow as easy and straightforward as possible. This user-friendly approach enables the standardisation of complex data analysis in clinical settings.ConclusionWe developed an R-package for the identification of known phenotype-associated variants and calculation of genetic diversity.

On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena.

Many techniques in machine learning attempt explicitly or implicitly to infer a low-dimensional manifold structure of an underlying physical phenomenon from measurements without an explicit model of the phenomenon or the measurement apparatus. This paper presents a cautionary tale regarding the discrepancy between the geometry of measurements and the geometry of the underlying phenomenon in a benign setting. The deformation in the metric illustrated in this paper is mathematically straightforward and unavoidable in the general case, and it is only one of several similar effects. While this is not always problematic, we provide an example of an arguably standard and harmless data processing procedure where this effect leads to an incorrect answer to a seemingly simple question. Although we focus on manifold learning, these issues apply broadly to dimensionality reduction and unsupervised learning.

Alternative Identification of Glycosides Using MS/MS Matching with an In Silico-Modified Aglycone Mass Spectra Library.

Glycosylation of metabolites serves multiple purposes. Adding sugars makes metabolites more water soluble and improves their biodistribution, stability, and detoxification. In plants, the increase in melting points enables storing otherwise volatile compounds that are released by hydrolysis when needed. Classically, glycosylated metabolites were identified by mass spectrometry (MS/MS) using [M-sugar] neutral losses. Herein, we studied 71 pairs of glycosides with their respective aglycones, including hexose, pentose, and glucuronide moieties. Using liquid chromatography (LC) coupled to electrospray ionization high-resolution mass spectrometry, we detected the classic [M-sugar] product ions for only 68% of glycosides. Instead, we found that most aglycone MS/MS product ions were conserved in the MS/MS spectra of their corresponding glycosides, even when no [M-sugar] neutral losses were observed. We added pentose and hexose units to the precursor masses of an MS/MS library of 3057 aglycones to enable rapid identification of glycosylated natural products with standard MS/MS search algorithms. When searching unknown compounds in untargeted LC-MS/MS metabolomics data of chocolate and tea, we structurally annotated 108 novel glycosides in standard MS-DIAL data processing. We uploaded this new in silico-glycosylated product MS/MS library to GitHub to enable users to detect natural product glycosides without authentic chemical standards.

Divergent double subduction of Bangong-Nujiang Ocean revealed by high-resolution magnetotelluric data at 86°E in the northern Tibetan Plateau

The Tibetan Plateau was mainly produced by the assembly of several terranes resulting from ocean closure and continental collision. The formation and evolution of the Bangong-Nujiang Ocean (BNO) played a prominent role in the Tibetan Plateau accretion before the Cenozoic collision between the Indian and Eurasian continents. The closure mode of the BNO between the Lhasa and Qiangtang terranes has been controversial, involving subduction polarity, i.e., regarding whether there was only northward subduction beneath the Qiangtang terrane or both southward and northward subduction beneath the Lhasa and Qiangtang terranes. To resolve this issue, a dense broadband magnetotelluric (MT) profile transecting the Bangong-Nujiang suture zone (BNS) at 86°E was completed. After standardized data processing and analysis, geological interpretation can be performed on the two-dimensional (2-D) inversion results of the MT data. Through a large number of inversion experiments, we obtained a reliable lithospheric electrical structure beneath the BNS and its adjacent regions. Our results show that the upper crust in our study area is featured by high resistivity overall, but it is cut by several fault zones, indicating that it may be influenced by the late strike-slip fault caused by the India-Asia collision. The middle-lower crust of the northern Lhasa terrane is highly conductive and may represent juvenile crust. The existence of a northward and southward inclined electrical gradient zone in the BNS may indicate that the BNO was closed by north-south bidirectional subduction.

Informatization process of wind and solar resource power generation: Empirical abstraction and packing algorithm

The development of software tools is critical to meeting the changing needs of the wind and solar resource generation industries. By identifying some of the limitations of existing systems, such as fragmentation in data query and plant management, as well as a lack of data resource management. In response to these issues, it is proposed to use a hybrid deep network model for simulation data to develop a management platform for wind and solar resource observation data. High-quality real-time measurement data and standardized data processing can be collected stably using these tools, which can significantly improve the development efficiency of landscape resource power generation projects and save development costs.

Tanana River Test Site Model Verification Using the Marine and Hydrokinetic Toolkit (MHKiT)

The marine energy (ME) industry historically lacked a standardized data processing toolkit for common tasks such as data ingestion, quality control, and visualization. The marine and hydrokinetic toolkit (MHKiT) solved this issue by providing a public software deployment (open-source and free) toolkit for the ME industry to store and maintain commonly used functionality for wave, tidal, and river energy. This paper demonstrates an initial model verification study in MHKiT. Using Delft3D, a numerical model of the Tanana River Test Site (TRTS) at Nenana, Alaska was created. Field data from the site was collected using an Acoustic Doppler Current Profiler (ADCP) at the proposed Current Energy Converter (CEC) locations. MHKiT is used to process model simulations from Delft3D and compare them to the transect data from the ADCP measurements at TRTS. The ability to use a single tool to process simulation and field data demonstrates the ease at which the ME industry can obtain results and collaborate across specialties, reducing errors and increasing efficiency.

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated.

CryoEM single particle reconstruction with a complex-valued particle stack

Single particle reconstruction (SPR) in cryoEM is an image processing task with an elaborate hierarchy that starts with many very noisy multi-frame images. Efficient representation of the intermediary image structures is critical for keeping the calculations manageable. One such intermediary structure is called a particle stack and contains cut-out images of particles in square boxes of predefined size. The micrograph that is the source of the boxed images is usually corrected for motion between frames prior to particle stack creation. However, the contrast transfer function (CTF) or its Fourier Transform point spread function (PSF) are not considered at this step. Historically, the particle stack was intended for large particles and for a tighter PSF, which is characteristic of lower resolution data. The field now performs analyses of smaller particles and to higher resolution, and these conditions result in a broader PSF that requires larger padding and slower calculations to integrate information for each particle. Consequently, the approach to handling structures such as the particle stack should be reexamined to optimize data processing.Here we propose to use as a source image for the particle stack a complex-valued image, in which CTF correction is implicitly applied as a real component of the image. We can achieve it by applying an initial CTF correction to the entire micrograph first and perform box cutouts as a subsequent step. The final CTF correction that we refine and apply later has a very narrow PSF, and so cutting out particles from micrographs that were approximately corrected for CTF does not require extended buffering, i.e. the boxes during the analysis only have to be large enough to encompass the particle. The Fourier Transform of an exit-wave reconstruction creates an image that has complex values. This is a complex value image considered in real space, opposed to standard SPR data processing where complex numbers appear only in Fourier space. This extension of the micrograph concept provides multiple advantages because the particle box size can be small and calculations crucial for high resolution reconstruction such as Ewald sphere correction, aberration refinement, and particle-specific defocus refinement can be performed on the small box data.

A Survey of Implicit Discourse Relation Recognition

A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between them. The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. Indeed, the IDRR task is important to diverse downstream natural language processing tasks, such as text summarization, machine translation and so on. This article provides a comprehensive and up-to-date survey for the IDRR task. We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history. In each solution category, we present and analyze the most representative methods, including their origins, ideas, strengths and weaknesses. We also present performance comparisons for those solutions experimented on a public corpus with standard data processing procedures. Finally, we discuss future research directions for discourse relation analysis.

Processing of Accelerometry Data with GGIR in Motor Activity Research Consortium for Health

The Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of clinical and community studies that employ common digital mobile protocols and collect common clinical and biological measures across participating studies. At a high level, a key scientific goal which spans mMARCH studies is to develop a better understanding of the interrelationships between physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. mMARCH studies employ wrist-worn accelerometry to obtain objective measures of PA/SL/CR. However, there is currently no consensus on a standard data processing pipeline for raw accelerometry data and few open-source tools which facilitate their development. The R package GGIR is the most prominent open-source software package for processing raw accelerometry data, offering great functionality and substantial user flexibility. However, even with GGIR, processing done in a harmonized and reproducible fashion across multiple analytical centers requires a nontrivial amount of expertise combined with a careful implementation. In addition, there are many statistical methods useful for analyzing PA/SL/CR patterns using accelerometry data which are implemented in non-GGIR R packages, including methods from multivariate statistics, functional data analysis, distributional data analysis, and time series analyses. To address the issues of multisite harmonization and additional feature creation, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data via GGIR, merging GGIR, and non-GGIR features of PA/SL/CR together, implementing several additional data and feature quality checks, and performing multiple analyses including Joint and Individual Variation Explained, an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. The pipeline is easily modified to calculate additional features of interest, and allows for studies not affiliated with mMARCH to apply a pipeline which facilitates direct comparisons of scientific results in published work by mMARCH studies. This manuscript describes the pipeline and illustrates the use of combined GGIR and non-GGIR features by applying Joint and Individual Variation Explained to the accelerometry component of CoLaus|PsyCoLaus, one of mMARCH sites. The pipeline is publicly available via open-source R package mMARCH.AC.

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids.

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing. An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise. Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility. A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.

Quantitative Evaluation of Imputation Methods Using Bounds Estimation of the Coefficient of Determination for Data-Driven Models with an Application to Drilling Logs

Summary With the constantly increasing quantity of data recorded in the oil and gas industry, data analytics and data-driven algorithms are gaining popularity. Meanwhile, they are highly sensitive to employed data management methods. Using drilling data as an example, the methods for data quality improvement play an ever increasing role in the data preparation phase. One of the most common data issues, especially in real-time sensing systems, is missing data (gaps between measured points). Various data imputation approaches (forward filling, interpolation, regression modeling, etc.) have been used to fill gaps to complete data sets as standard data processing procedures. The metrics, such as coefficient of determination (R2), mean absolute error, root mean square error, and mean absolute percentage error, are common ways to evaluate the imputation approaches, assuming that the ground truth data are at hand. In reality, the ground truth of missing data is not available for quantitative method evaluation. Especially when data are received occasionally, the ground truth is impossibly or inaccurately estimated, leading to tricky situations. For instance, how to evaluate infilling methods quantitatively or how to compare the methods’ behaviors? To the best of the authors’ knowledge, there are so far few existing methods to quantitatively estimate the accuracy of data imputation without the ground truth. To some extent, one may lack proven confidence in using imputed data for following up data analysis and data-driven modeling. In this study, a novel approach has been developed to quantitatively evaluate the data imputation quality. The presented method is built on an analytical estimation of the R2 bounds in the context of linear interpolation. The reasons for choosing linear regression as a benchmarking method are twofold. First, the bounds evaluation of R2 can be determined analytically for data imputation in the context of linear regression. Second, linear regression is a relatively simple imputation method, and we assume that more sophisticated methods, such as random forest (RF), k-nearest neighbors (KNN), and other iterative imputation techniques, can achieve higher accuracy with proper parameter tuning. The evaluation method is demonstrated by examples using real-time drilling data logs. Moreover, the application to drilling logs also enables informed decision-making in relation to gap filling and data resampling workflows.

ОЦІНЮВАННЯ ЯКОСТІ РОБОТИ КОТКА ПРИКОЧУВАЛЬНОГО ПРИЗМАТИЧНОГО «СТЕП КПП-6»

Summary The purpose of research. Evaluation of the quality of technological process performance by the «STEP KPP-6» roller according to such criteria as the intensity and degree of soil compaction, the uniformity of bottom compaction, the degree of destruction of the cracked surface of the soil, the correspondence of the values obtained during the tests to the levels of professional assessment. Research methods. Analytical review of the design, field experiment, determination of indicators by standardized methods and statistical data processing. Results. The research results proved that the prismatic «STEP KPP-6» roller stably performs the technological process of rolling with satisfactory indicators of the quality of work and can be effectively used for its intended purpose. Rolling the surface of the soil, the roller at the same time intensively acts on the bottom, compacting it, which ensures close adherence of the seed to the bottom of the seed bed to obtain uniform and friendly seedlings. The density of the soil in a layer up to 15 cm before the passage of the roller was 0.97 g/cm3, after the passage was 1.15 g/cm3, compaction was 0.18 g/cm3. With the increase in the depth of preliminary tillage after passing the roller, a sharp decrease in the degree of soil compaction is noted: from 72% (at an average depth of 5 cm) to 58% (at an average depth of 10.6 cm). It is also significant to reduce the intensity and uniformity of bottom compaction from 100% to 32% and from 24% to 10% respectively. In addition, the roller ensures the destruction and loosening of the cracked surface of the field by 88% in one pass and by up to 97% in two passes both before sowing and in the presence of seedlings of cultivated plants. Conclusions. Research has established that the «STEP KPP-6» roller has universal structural and technological solutions, meets the technological requirements of a consumer, performs the rolling process with high quality, which, along with an attractive price, puts the machine in the line of high-quality import substitution offers. The use of the «STEP KPP-6» roller is recommended before sowing after deep cultivation for sedimentation of the soil, formation of lower horizons and prevention of their drying by covering the surface with a loosened layer of soil and after sowing to ensure contact of seeds with the soil, effective at destroying the cracked soil surface without and with plants. Key words: rolling, roller, soil density, compaction, tillage depth, crust destruction, cracked soil surface.

ДОСЛІДЖЕННЯ ЗНОСУ ДИСКІВ АГРЕГАТА ГРУНТООБРОБНОГО ДЛ-2,5 ЗА ЧАС ВИПРОБУВАНЬ В УМОВАХ ДОСЛІДНОЇ ЕКСПЛУАТАЦІЇ

Summary The purpose of research. Evaluation of the quantitative and qualitative parameters of the wear of the disc working bodies of the DL-2.5 soil tillage unit on the basis of monitoring their condition during tests in the conditions of experimental operation over a 6-year period and forecasting the possibility of further operation of the unit. Research methods. Analysis of the machine design, determination of the linear dimensions of discs by standardized measurement methods and statistical data processing. Research results. The intensity of the wear of the disk working bodies depending on the earnings, the forecast of the resource before the loss of quality is established. Conclusions. On the basis of experimental studies, objective and reliable information was obtained about the parameters of the wear of the disc working bodies of the DL-2.5 soil-cultivation unit in the conditions of economic operation in the forest-steppe zone of Ukraine. During the six-year period of testing, with a total volume of work performed by the unit of 2430.6 ha (2017 – 395,4 ha; 2018 – 413,3 ha; 2019 – 456,3 ha; 2020 – 357,6 ha; 2021 – 400,0 ha; 2022 – 408,0 ha) and the achievement of cultivation per disc - 120 ha, the wear of the working edge of the discs of the front and rear rows amounted to 21,1 mm and 11,5 mm, respectively, for the permissible value of 35 mm, or in percentages, respectively - 60,2 and 32,8. Accordingly, the estimated residual resource is forecast at the level of 39,8 % and 67,2 %. According to the results of resource tests during 2017-2022, the unit maintained its operable condition, provided the necessary regulation, aggregation, transportation and had an aesthetic appearance. The obtained results can be used by machine builders as an informative base for fine-tuning disks and forming standards for their performance, and by manufacturers of g. products, as information on the choice of reliable equipment and the potential of its service life. Prospects of research in this direction consist in the possible multiplicity of accounting for the general conditions of operation of disk tools, the variability of structural and technological schemes, and can also be used as methodical material for p. g. machines that have discs in their composition. Keywords: soil tillage unit; trial; disk working body; boring steel; wear and tear; projected resource.

Coronal seismology by slow waves in non-adiabatic conditions

Slow magnetoacoustic waves represent an important tool for probing the solar coronal plasma. The majority of seismological methods with slow waves are based on a weakly non-adiabatic approach, which assumes the coronal energy transport has only weak effects on the wave dynamics. Despite it significantly simplifies the application of coronal seismology by slow waves, this assumption omits a number of important and confidently observed effects and thus puts strong limitations on the reliability of seismological estimations. We quantitatively assess the applicability of the weak thermal conduction theory to coronal seismology by slow waves. We numerically model the linear standing slow wave in a 1D coronal loop, with field-aligned thermal conduction κ‖ as a free parameter and no restrictions on its efficiency. The time variations of the perturbed plasma parameters, obtained numerically with full conductivity, are treated as potential observables and analysed with the standard data processing techniques. The slow wave oscillation period is found to increase with κ‖ by about 30%, indicating the corresponding modification in the effective wave speed, which is missing from the weak conduction theory. Phase shifts between plasma temperature and density perturbations are found to be well consistent with the approximate weakly conductive solution for all considered values of κ‖. In contrast, the comparison of the numerically obtained ratio of temperature and density perturbation amplitudes with the weak theory revealed relative errors up to 30–40%. We use these parameters to measure the effective adiabatic index of the coronal plasma directly as the ratio of the effective slow wave speed to the standard sound speed and in the polytropic assumption, which is found to be justified in a weakly conductive regime only, with relative errors up to 14% otherwise. The damping of the initial perturbation is found to be of a non-exponential form during the first cycle of oscillation, which could be considered as an indirect signature of entropy waves in the corona, also not described by weak conduction theory. The performed analysis and obtained results offer a more robust scheme of coronal seismology by slow waves, with reasonable simplifications and without the loss of accuracy.

Study On Rate of Penetration Prediction Model of Offshore Drilling Machinery and Structure Optimization Base on Deep Learning

With the development of modern downhole monitoring technologies such as measurement while drilling, the data obtained during drilling has the characteristics of huge volume, rich variety, and strong timeliness, which greatly promotes big data and artificial intelligence in drilling rate prediction from the data level. Progress. How to further effectively excavate and use drilling big data, improve drilling efficiency, and reduce drilling risks is still a hot research topic. This article explores the real-time drilling data of an exploratory well in the South China Sea, and establishes a reasonable machine learning model to accurately predict the ROP and its changing trend. The research first cleans the real-time drilling data, and improves the data quality used for modeling through standardized data processing; then establishes a deep neural network, optimizes input and output parameters, trains the model, and validates the prediction results; finally, optimizes the model structure to improve prediction The accuracy and efficiency of the model. The research results show that when the amount of data is sufficient and the missing values are few, the neural network can more accurately predict the ROP and its change trend, and at the same time, reasonable optimization of the model can also improve the accuracy and calculation efficiency of the drilling rate prediction.