Automatic Data Processing Research Articles

Towards automatic detection and classification of swimming pectinids behaviour: first developments on great scallops (Pecten maximus)

ABSTRACT Over the past decades, valvometric techniques have been commonly used to record valve opening activities of bivalves. Various relationships with environmental variations have been elucidated through different types of metrics extracted from valvometric signals (e.g. valve opening, cyclicity, specific behaviours). Although automated data processing methods exist, many specific behaviours are still annotated manually. This study proposes an algorithm to detect and classify the behaviours performed by the great scallop (Pecten maximus) in two categories: jump-like (JL) behaviours and other behaviours (OBs). These two categories differ in the shape of their valvometric signal, JL being movements of high amplitudes associated with ‘displacement movements’ (rotation, swimming, jumping, flipping) and OB grouping all other movements of lower amplitudes (‘common movements’), such as partial closures, which are produced routinely. This algorithm has been developed and tested on 10 scallop valve opening time series recorded using fully autonomous valvometers based on the Hall effect principle. The algorithm detected 93.65% ± 5.5 of manually annotated behaviours produced by scallops, with a false detection rate of less than 6.3% ± 5.5. Classification performances vary according to the type of behaviour. JL behaviours and OBs were well classified at 83.72% ± 23.09 and 98.92% ± 1.80, respectively. Analysis of the algorithm's outputs, highlighting potential daily trends in the production of certain behaviours, shows their relevance for acquiring information on the biology of scallops. By providing an efficient and flexible detection and classification method, this study is a first step towards the automation of bivalve behaviour detection. This study also highlights the importance of simultaneously using Hall sensors and accelerometers to accurately classify the complex behaviours of mobile bivalves such as P. maximus.

Visualization Program Design for Complex Piping Systems in Marine Engine Simulation Systems

This study is dedicated to the development of an advanced ship piping network programming tool to address the challenges faced by traditional text-based design and computation methods when dealing with complex and large-data-volume piping systems, such as burdensome programming tasks, high error rates, and difficulty in troubleshooting faults. Leveraging Microsoft’s WPF technology and the C# language, combined with Excel as a data input platform, this tool provides an intuitive graphical user interface, allowing users to intuitively build and analyze ship piping network models by dragging and dropping controls. The tool not only simplifies the design process of complex piping systems but also significantly improves efficiency and accuracy through automated data processing and calculations. It supports user customization of key pipeline characteristics, such as maximum flow and direction, further enhancing the applicability and accuracy of the piping network model. In addition, with optimized interaction design and data management methods, the tool significantly reduces the learning difficulty for users, while improving the reliability of design and efficiency of troubleshooting. The results of this study show the tool not only technically outperforms traditional methods but also provides a new efficient, intuitive, and user-friendly tool for the teaching and engineering applications of ship piping networks, paving a new path for the design and optimization of ship piping network systems, with significant practical application value and theoretical significance. Looking forward, this tool is expected to play a broader role in the instruction and industrial practices associated with ship piping networks, moving the field toward more efficient and intelligent development.

Semantic sensor data annotation method for industrial scene efficiency optimization to enable digital economy

AbstractIn the digital economy era, efficiently leveraging the vast amount of sensor data generated by the Industrial Internet of Things (IIoT) is essential. This paper presents an innovative semantic annotation method for industrial sensor data, designed to optimize data processing and enhance system efficiency. Our method combines cluster analysis, ontology development, and rule‐based reasoning to automatically annotate IIoT sensory data. By utilizing data aggregation and filtering mechanisms, which incorporate the DBSCAN (Density‐Based Spatial Clustering of Applications with Noise) algorithm and a rule engine, we significantly reduce the data volume required for annotation. The Semantic Web Rule Language aids in naming new concepts and properties identified through clustering, contributing further to the automation of data processing. Experimental results, using public datasets, validate the effectiveness of our method, showing a reduction in data volume by about 20% and underscoring its potential in enhancing industrial systems' automation and overall efficiency.

Experimental Study of Wind Pressures on Low-Rise H-Shaped Buildings

Recognizing the urgent need for mitigating global warming, natural ventilation presents a potential strategy to reduce cooling energy demands, enhance thermal comfort, and contribute to indoor air quality. H-shaped buildings are prevalent worldwide, and they constitute the majority of the social housing construction in Brazil. Research suggests that the inadequate design of these buildings can result in poor ventilation; however, investigations about their natural ventilation performance are limited. Thus, the present contribution aims to determine the impact of the geometric characteristics of H-shaped buildings on the pressure distribution through wind tunnel experiments. Three models were tested in the wind tunnel experiments, representing different proportions. Their scales were configured to comply with the 5% obstruction limit allowed for wind tunnel testing, which was performed for 20 wind attack angles. Moreover, a scour test was carried out to allow a better understanding of the wind flow. Python scripting was developed to automate data processing, which is openly available in this paper. The results indicate that the proportion of the model influences the pressure distribution on roofs and leeward walls. Additionally, the depth of the recessed cavity affects its side surfaces and can result in a mirrored behavior on the frontal face of deep cavities (i.e., the wind direction is 45°). The model height influences the windward surfaces in its lower portion, since taller models present a recirculation vortex that modifies the pressure near the ground.

Ultra-dense Motion Capture: An exploratory full-automatic approach for dense tracking of breast motion in 4D.

Understanding the dynamic deformation pattern and biomechanical properties of breasts is crucial in various fields, including designing ergonomic bras and customized prostheses, as well as in clinical practice. Previous studies have recorded and analyzed the dynamic behaviors of the breast surface using 4D scanning, which provides a sequence of 3D meshes during movement with high spatial and temporal resolutions. However, these studies are limited by the lack of robust and automated data processing methods which result in limited data coverage or error-prone analysis results. To address this issue, we identify revealing inter-frame dense correspondence as the core challenge towards conducting reliable and consistent analysis of the 4D scanning data. We proposed a fully-automatic approach named Ulta-dense Motion Capture (UdMC) using Thin-plate Spline (TPS) to augment the sparse landmarks recorded via motion capture (MoCap) as initial dense correspondence and then rectified it with a sophisticated post-alignment scheme. Two downstream tasks are demonstrated to validate its applicability: virtual landmark tracking and deformation intensity analysis. For evaluation, a dynamic 4D human breast anthropometric dataset DynaBreastLite was constructed. The results show that our approach can robustly capture the dynamic deformation characteristics of the breast surfaces, significantly outperforms baselines adapted from previous works in terms of accuracy, consistency, and efficiency. For 10 fps dataset, average error of 0.25 cm on control-landmarks and 0.33 cm on non-control (arbitrary) landmarks were achieved, with 17-70 times faster computation time. Evaluation was also carried out on 60 fps and 120 fps datasets, with consistent and large performance gaining being observed. The proposed method may contribute to advancing research in breast anthropometry, biomechanics, and ergonomics by enabling more accurate tracking of the breast surface deformation patterns and dynamic characteristics.

GlYcoLISA: antigen-specific and subclass-specific IgG Fc glycosylation analysis based on an immunosorbent assay with an LC-MS readout.

Immunoglobulin G (IgG) fragment crystallizable (Fc) glycosylation modulates effector functions such as antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity. Consequently, assessing IgG Fc glycosylation is important for understanding the role of antibodies in infectious, alloimmune and autoimmune diseases. GlYcoLISA determines the Fc glycosylation of antigen-specific IgG by an immunosorbent assay with a liquid chromatography-mass spectrometry (LC-MS) readout. Detection of antigen-specific IgG glycosylation in a subclass- and site-specific manner is realized by LC-MS-based glycopeptide analysis after proteolytic cleavage. GlYcoLISA addresses challenges related to the low abundance of specific IgG and the high background of total IgG by using well-established immunosorbent assays for purifying antibodies of the desired specificity using immobilized antigen. Alternative methods with sufficient glycan resolution lack these important specificities. GlYcoLISA is performed in a 96-well plate format, and the analysis of 160 samples takes ~5 d, with 1 d for sample preparation, 2 d of LC-MS measurement and 2 d for partially automated data processing. GlYcoLISA requires expertise in LC-MS operation and data processing.

Application of the performance of machine learning techniques as support in the prediction of school dropout

This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico. The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total. Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%.

Artificial neural network for deciphering the structural transformation of condensed ZnO by extended x-ray absorption fine structure spectroscopy

Pressure-induced structural phase transitions play a pivotal role in unlocking novel material functionalities and facilitating innovations in materials science. Nonetheless, unveiling the mechanisms of densification, which relies heavily on precise and comprehensive structural analysis, remains a challenge. Herein, we investigated the archetypal B4 → B1 phase transition pathway in ZnO by combining x-ray absorption fine structure (XAFS) spectroscopy with machine learning. Specifically, we developed an artificial neural network (NN) to decipher the extended-XAFS spectra by reconstructing the partial radial distribution functions of Zn–O/Zn pairs. This provided us with access to the evolution of the structural statistics for all the coordination shells in condensed ZnO, enabling us to accurately track the changes in the internal structural parameter u and the anharmonic effect. We observed a clear decrease in u and an increased anharmonicity near the onset of the B4 → B1 phase transition, indicating a preference for the iT phase as the intermediate state to initiate the phase transition that can arise from the softening of shear phonon modes. This study suggests that NN-based approach can facilitate a more comprehensive and efficient interpretation of XAFS under complex in-situ conditions, which paves the way for highly automated data processing pipelines for high-throughput and real-time characterizations in next-generation synchrotron photon sources.

Design and Implementation of a Three-phase Asynchronous Motor Intelligent Test System

Background: The locomotive traction motor has gradually shifted from DC motors to AC motors in high-speed railways, and the traction motor needs to be regularly maintained and tested frequently. Objective: The aim of this study was to test the related motor performance by obtaining and analyzing motor data from the test according to the standard "Three-phase Asynchronous Motor Test Method (GB-T 1032-2012)". The performance of the tested motor has been evaluated to meet the relevant requirements of the application. Thus, reasonable and scientific references have been offered for the maintenance and repair of the motor. Methods: A three-phase AC asynchronous motor test system based on LabVIEW and an AC power dynamometer was constructed based on the needs of the factory and type test of a three-phase AC asynchronous motor. Ambient temperature measurement, insulation resistance measurement, DC resistance measurement, no-load characteristics test, overspeed test, blocking characteristics test, load test, and temperature rise test have been carried out. The data on voltage, current, speed, power, and so on, have been collected. A 125 kW three-phase asynchronous motor was tested with the designed system, and the parameters obtained from the system were compared with those from the motor labels. Results: The three-phase AC asynchronous motor test system was designed based on LabVIEW and AC dynamometer operating on an industrial computer with a precision measuring instrument. The most advanced virtual instrument technology was used to combine the powerful data computing and processing ability with the measurement and control ability of instrument hardware. The software proved to be able to acquire data display, control, storage, and analysis simultaneously. In addition, a high degree of intelligence, an automatic motor start and stop control, automatic synchronous acquisition of test data, automatic data processing and calculation, and automatic test report generation and printing function were covered in this system. The simulated results of the system agreed well with the actual performance of the three-phase asynchronous motor, and helped the motor to operate well. Conclusion: The designed testing system exhibited a high automation ability, reliability, and accuracy. It proved to be a time and manpower-saving technical method, improving the actual test efficiency, and helped to reduce labor intensity dramatically.

Daily Hospital Emerging Infectious Disease(EID) Workflow & Data Processing Automation.

Following the onset of the covid pandemic two years ago, the Ministry of Health(MOH)'s required all health care cluster groups to provide daily reporting of the emergency department as well as the inpatient situation in the respective healthcare institutions for oversight of the covid situation. In view of the improvements in the data availability and relief of the tedious manual collation, DAO was entrusted with the task to help enable and setup the standardized report and generation process moving forward.

Amine vapor-responsive ratiometric sensing tag based on HPTS/TPB-PVA fluorescent film for visual determination of fish freshness

In this study, amine vapor-sensitive films with ratiometric fluorescence attributes were developed. The pH-sensitive fluorescein 8-hydroxypyrene-1,3,6-trisulfonic acid trisodium salt (HPTS) and its tetraphenylethylene derivative (TPB) were selected as ratiometric indicators and incorporated into a polyvinyl alcohol (PVA) matrix to produce HPTS/TPB-PVA films. The films responded well to amine vapors, and the interference of aromatic vapors did not substantially affect the fluorescence signals of the films. Under UV light at a wavelength of 365 nm, the fluorescence of the films changed from dark pink to light pink and finally to yellow when the freshness of the fish was visually checked during storage. In addition, the color difference values of the films showed a positive correlation with the total volatile basic nitrogen (TVB-N), ranging from 12.7 to 24.8 mg/100 g at 25 °C and 8.4 to 25.6 mg/100 g at 4 °C, respectively. This indicates that fluorescent films have good potential for quantifying fish freshness in the near future when connected to an automatic data processing system based on color differences.

RSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment

Abstract. Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn, last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis.

IEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.

Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations. iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

ИТОГИ ВНЕДРЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ЗДРАВООХРАНЕНИИ РОССИИ

Introduction. The introduction of artificial intelligence (AI) technologies in Russian healthcare is an important step to improve the efficiency and quality of medical care. The development of AI technologies helps automate data processing, support physician decision-making and improve predictive analytics. Aim. Analysis of the results of creation and implementation of software solutions using AI technologies in Russian healthcare. Materials and Methods: A systematic search for data in the State Register of Medical Devices and on the official website of the Unified Information System in the field of procurement was conducted. The main research methods were analysis of registered AI-based medical devices and monitoring of their use in medical institutions. Results. Quantitative indicators of implementation of solutions using AI technologies in Russian healthcare have been determined. The factors facilitating and hindering the introduction of innovations were formulated. The list of components of the methodology of implementation and operation of medical solutions based on AI technologies, related changes in the organization of medical care, personnel training, and patients' involvement in the development of their health has been defined. Conclusions. Significant progress in the use of AI in Russian healthcare requires further disclosure of methodological, organizational, technological and economic issues. Continued sharing of regional practices and knowledge will be key to building trust in AI technologies.

Digital Forensics in the Age of AI and ML: Evidence Handling, Validation and Reporting

In the age of artificial intelligence (AI) and machine learning (ML), digital forensics has undergone substantial transformations increasing the efficiency and accuracy of evidence management, validation and reporting. This article investigates the incorporation of AI and machine learning technology into digital forensic techniques, with a focus on automating data processing, discovering trends and enhancing the overall dependability of forensic investigations. Key areas of evidence processing, such as collection, preservation and chain of custody, are evaluated in light of AI-powered technologies. The validation process is explained, with an emphasis on using ML algorithms to assure data integrity and authenticity. Furthermore, the article goes into new reporting methodologies made possible by AI, which provide complete and understandable information for legal and investigative purposes.

Visual digital data, ethical challenges, and psychological science.

Digital visual data afford psychologists with exciting research possibilities. It becomes possible to see real-life interactions in real time and to be able to analyze this behavior in a fine-grained and systematic manner. However, the fact that faces (and other personally identifying physical characteristics) are captured as part of these data sets means that this kind of data is at the highest level of sensitivity by default. When this is combined with the possibility of automatic collection and processing, then the sensitivity risks are compounded. Here we explore the ethical challenges that face psychologists wishing to take advantage of digital visual data. Specifically, we discuss ethical considerations around data acquisition, data analysis, data storage, and data sharing. We begin by considering the challenges of securing visual data from both public space security systems and social media sources. We then explore the dangers of bias and discrimination in automatic data processing, as well as the dangers to human analysts. We set out the ethical requirements for secure data storage, the dangers of "function creep," and the challenges of the right of the individual to withdraw from databases. Finally, we consider the tensions that exist between sensitive visual data that require extra protections and the recent open science movement, which advocates data transparency and sharing. We conclude by offering a practical route map for tackling these complex ethical issues in the form of a Privacy and Data Protection Impact Assessment template for researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Two psychologies: Specifics of research models

A pressing problem in psychology is the quality of the application of mathematical methods in the practice of psychological research, which was one of the reasons for the emergence of a replication crisis. Also, recently many publications have appeared criticizing mathematical statistics in psychological research within the framework of critical psychology. In our opinion, the following factors play a key role in this problem: 1) the contradiction between the requirement for comprehensive research and the multiplicity of hypothesis testing; 2) the ability to put forward or adjust post-hoc hypotheses; 3) the training of psychologists in the field of mathematical statistics is significantly dependent on the exponential growth in the number of methods for the study of which limited time is given. Consequently, it is necessary to form lists of mathematical methods that are preferable in certain areas from the point of view of statistical practice, the origins of which lie in general methodological principles. The paper examines the problems of the methodology of applying mathematical methods in psychological articles from the point of view of the frequency and dynamics of their use, the dependence of the level of citations and other phenomena on them. It also examines a sample of articles in eight leading APA journals over the past 110 years that use the most current mathematical methods in psychology — 24,657 publications. The sample was obtained using queries to the Psyc- ARTICLES database. Using machine learning methods and automatic data processing, the raw results of search queries were corrected and validated. The work carried out on standardization and validation of data of this kind is the first example of work of this nature carried out in psychology. Based on the validated results obtained using structural equation modeling, two main factors in the use of mathematical methods were identified. Several alternative models are considered and compared.

ANALYSIS OF THE CURRENT STATE AND DIAGNOSIS OF KEY PROBLEMS OF PUBLIC ADMINISTRATION IN THE HEALTHCARE SECTOR IN THE REPUBLIC OF KAZAKHSTAN

Healthcare is one of the most important areas of activity of any state. One of the goals of sustainable development, the United Nations (UN) proclaims “Ensuring a healthy lifestyle and promoting well-being for all at any age.” The efforts of all countries of the world are directed towards the realization of this goal, since the health of the nation and the high standard of living of the country’s population are important components of the sustainable development of each country. In this regard, the article emphasizes that healthcare is one of the most important areas of activity of any state, and effective public administration in this area is critical, so the goal was to develop proposals and recommendations to ensure high-quality and affordable medical care for all segments of the population. For this purpose, various research meth-ods were used, such as the method of scientific abstraction, the method of induction and deduction, literature analysis and statistical analysis of modern data in dynamics over the past 5 years, the method of generalization and comparative analysis, which were used to identify problems and develop recom-mendations to improve the availability and quality of medical care, ensure public health and improve the quality of life of the population. In light of the challenges Kazakhstan has faced in public health management, including pandemics, demographic changes and technological innovations, we have pro-posed: introducing an automated data processing system in all regions of the country, improving the system of monitoring and analyzing health management, optimizing the financing system, developing a mechanism for monitoring and overseeing the provision of high-quality and affordable medical care to all segments of the population.

TQL: a Case Study of Integrating DSL in a Product

Domain-specific languages power numerous modern applications and libraries, including but not limited to: Wolfram Alpha, Microsoft Excel, Graphviz. This work aims to share the experience gathered from developing TQL (Talisman Query Language) – a domain-specific language used in Talisman platform. Talisman platform is a set of tools to automate data processing tasks, developed by Ivannikov Institute for System Programming of RAS. TQL implementation, discussed in this article, supports error-recovery, can be run directly inside a browser as well as on a server, and it also has an interactive playground that visualizes the parse tree while typing. This article describes several techniques and technologies that were used to make these qualities possible while keeping a single, maintainable codebase.

High Throughput Tomography (HiTT) on EMBL beamline P14 on PETRA III.

Here, high-throughput tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DESY in Hamburg, Germany, is presented. A high-photon-flux undulator beamline is used to perform tomographic phase-contrast acquisition in about two minutes which is linked to an automated data processing pipeline that delivers a 3D reconstructed data set less than a minute and a half after the completion of the X-ray scan. Combining this workflow with a sophisticated robotic sample changer enables the streamlined collection and reconstruction of X-ray imaging data from potentially hundreds of samples during a beam-time shift. HiTT permits optimal data collection for many different samples and makes possible the imaging of large sample cohorts thus allowing population studies to be attempted. The successful application of HiTT on various soft tissue samples in both liquid (hydrated and also dehydrated) and paraffin-embedded preparations is demonstrated. Furthermore, the feasibility of HiTT to be used as a targeting tool for volume electron microscopy, as well as using HiTT to study plant morphology, is demonstrated. It is also shown how the high-throughput nature of the work has allowed large numbers of `identical' samples to be imaged to enable statistically relevant sample volumes to be studied.