Automatic Data Processing Research Articles

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery.

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Towards Automated Detection and Localization of Red Deer Cervus elaphus Using Passive Acoustic Sensors during the Rut

Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of −96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization—the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing.

Breakthrough technologies for mineral exploration

Breakthrough technologies for mineral exploration are discussed from two perspectives. The first perspective is intended to discuss the important factors required for exploration technologies, derived deductively from a review of the role and expectations of exploration in the mining industry and the current situation of the mining industry. The second perspective is intended to discuss the common characteristics of breakthrough technologies for mineral exploration derived inductively from a review of specific examples of technological breakthroughs that actually brought about innovation to exploration: e.g. induced polarization (IP); airborne electromagnetics (EM); airborne gravity gradiometry (AGG); spectroscopic methods; global navigation satellite system (GNSS); unmanned survey platform; and neural networks/deep learning. The specific issues to be solved as breakthroughs in exploration technology in the near future are summarized as follows: Significant improvement in economic efficiency, namely: labor-saving, automated or unmanned operation, e.g. unmanned aerial vehicle (UAV)-based exploration and automatic spectroscopic scanning of drill cores; higher accessing capability, e.g. improvement of various airborne exploration techniques including airborne EM, AGG, and especially airborne IP; higher work efficiency and productivity, e.g. automatic data processing by neural networks/deep learning; and lower cost to implement, e.g. less expensive platforms such as UAVs.To obtain information that is really needed for exploration, specifically: IP effect of sulfide minerals associated with mineralization, i.e. practical spectral IP (SIP); geophysical characterization of the deep underground, e.g. enhancement of superconducting quantum interference device (SQUID)-based time-domain electromagnetic (TDEM); and removal of effects of the surface layer, e.g. very conductive deeply-weathered overburden and younger volcanics rich in magnetic minerals.To obtain information that could not be obtained by the conventional methods, specifically: distribution of endmember minerals related to mineralization, i.e. hyperspectral mapping with high spatial resolution.

The Silent Treatment?: Changes in patient emotional expression after silence.

Psychotherapy can be an emotionally laden conversation, where both verbal and non-verbal interventions may impact the therapeutic process. Prior research has postulated mixed results in how clients emotionally react following a silence after the therapist is finished talking, potentially due to studying a limited range of silences with primarily qualitative and self-report methodologies. A quantitative exploration may illuminate new findings. Utilizing research and automatic data processing from the field of linguistics, we analysed the full range of silence lengths (0.2 to 24.01 seconds), and measures of emotional expression - vocally encoded arousal and emotional valence from the works spoken - of 84 audio recordings Motivational Interviewing sessions. We hypothesized that both the level and the variance of client emotional expression would change as a function of silence length, however, due to the mixed results in the literature the direction of emotional change was unclear. We conducted a multilevel linear regression to examine how the level of client emotional expression changed across silence length, and an ANOVA to examine the variability of client emotional expression across silence lengths. Results indicated in both analyses that as silence length increased, emotional expression largely remained the same. Broadly, we demonstrated a weak connection between silence length and emotional expression, indicating no persuasive evidence that silence leads to client emotional processing and expression.

Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites.

Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8-2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.

“Please understand we cannot provide further information”: evaluating content and transparency of GDPR-mandated AI disclosures

AbstractThe General Data Protection Regulation (GDPR) of the EU confirms the protection of personal data as a fundamental human right and affords data subjects more control over the way their personal information is processed, shared, and analyzed. However, where data are processed by artificial intelligence (AI) algorithms, asserting control and providing adequate explanations is a challenge. Due to massive increases in computing power and big data processing, modern AI algorithms are too complex and opaque to be understood by most data subjects. Articles 15 and 22 of the GDPR provide a modest regulatory framework for automated data processing by, among other things, mandating that data controllers inform data subjects about when it is being used, and its logic and ramifications. Nevertheless, due to the phrasing of the articles and the numerous exceptions they allow, doubts have arisen about their effectiveness. In this paper, we empirically evaluate the quality and effectiveness of AI disclosures as mandated by the GDPR. By means of an online survey (N = 835), we investigated how data subjects expect to be informed about the automated processing of their data. We then conducted a content analysis of the AI disclosures of N = 100 companies and organizations. The combined findings reveal that current GDPR-mandated disclosures do not meet the expectations and needs of data subjects. Explanations drawn up following the guidelines of the generic formulations of the GDPR differ widely and are often vague, incomplete and lack transparency. In our conclusions we identify a path towards standardizing and optimizing AI information notices.

SinGAN-Seg: Synthetic training data generation for medical image segmentation.

Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.

Automated Design and Rapid Manufacturing of Low-Cost Customized Upper Limb Prostheses

The paper presents results of studies on an innovative approach to mass customization of prostheses, namely automated design and rapid manufacturing (3D printing), on the basis of 3D scanning of upper limb of a given patient. Two different products are presented – a simple mechanical prosthesis and a cosmetic prosthesis. In both cases, intelligent CAD models, with adaptive geometry – changing automatically to the data from 3D scanning – were prepared. The digital models were built and tested using data of real patients. A special algorithm of 3D scanning data processing was prepared and implemented, as well as a set of algorithms of automated data processing for 3D printing processes. The test models were manufactured using low-cost 3D printing technology. The verification was successful – possibility of preparing customized prostheses was reached, using automated design.

A novel UHPLC-ESI-MS/MS method and automatic calculation software for regiospecific analysis of triacylglycerols in natural fats and oils

Regioisomeric analysis of triacylglycerols (TAGs) in natural oils and fats is a highly challenging task in analytical chemistry. Here we present a software (TAG Analyzer) for automatic calculation of regioisomeric composition of TAGs based on the mass spectral data from recently reported ultra-high performance liquid chromatography electrospray ionization tandem mass spectrometry (UHPLC−ESI−MS/MS) method for analyzing TAG regioisomers. The software enables fast and accurate processing of complex product ion spectra containing structurally informative diacylglycerol [M+NH4−RCO2H–NH3]+ and fatty acid ketene [RCO]+ fragment ions. Compared to manual processing, the developed software offers higher throughput with faster calculation as well as more accurate interpretation of chromatographically overlapping isobaric TAGs. The software determines results by constructing a synthetic spectrum to match the measured fragment ion spectrum, and by reporting the optimal concentrations of TAGs used to create the synthetic spectrum. This type of calculation is often extremely challenging for manual interpretation of the fragment ion spectra of isobaric TAGs with shared fragments, hence the need for automated data processing. The developed software was validated by analyzing a wide range of mixtures of regiopure TAG reference compounds of known composition and a commercial olive oil sample. Additionally, the method was also applied for regiospecific analysis of TAGs in human milk as an example of natural fats and oils with a highly complex TAG profile. The results indicate that the software is capable of resolving regioisomeric composition of natural TAGs even of the most complex composition. This novel calculation software combined with our existing UHPLC-ESI-MS/MS method form a highly efficient tool for regioisomeric analysis of TAGs in natural fats and oils.

Analysis of Ti- and Pb-based particles in the aqueous environment of Melbourne (Australia) via single particle ICP-MS

The analysis of natural and anthropogenic nanomaterials (NMs) in the environment is challenging and requires methods capable to identify and characterise structures on the nanoscale regarding particle number concentrations (PNCs), elemental composition, size, and mass distributions. In this study, we employed single particle inductively coupled plasma-mass spectrometry (SP ICP-MS) to investigate the occurrence of NMs in the Melbourne area (Australia) across 63 locations. Poisson statistics were used to discriminate between signals from nanoparticulate matter and ionic background. TiO2-based NMs were frequently detected and corresponding NM signals were calibated with an automated data processing platform. Additionally, a method utilising a larger mass bandpass was developed to screen for particulate high-mass elements. This procedure identified Pb-based NMs in various samples. The effects of different environmental matrices consisting of fresh, brackish, or seawater were mitigated with an aerosol dilution method reducing the introduction of salt into the plasma and avoiding signal drift. Signals from TiO2- and Pb-based NMs were counted, integrated, and subsequently calibrated to determine PNCs as well as mass and size distributions. PNCs, mean sizes, particulate masses, and ionic background levels were compared across different locations and environments.Graphical abstract

Multi-pronged approach for monitoring sedimentation processes at multi-barrel culverts

This paper describes a case study that employs an in-situ methodology for monitoring sedimentation processes at a multi-barrel culvert site. This area is seldom discussed in the scientific and engineering literature. The methodology integrates ground and aerial surveys from Real-Time Kinematic (RTK) and drone-based photogrammetry, with traditional hydro-geomorphological monitoring techniques (e.g., on-site photography, webcams, soil coring, and in-stream measurements) to acquire data continuously or for specific weather events. The paper focuses on presenting how experimental methods and their associated protocols can be assembled to complement each other to enable a comprehensive and detailed investigation of culvert sedimentation processes. The investigation entails the inception of the sediment deposits in the culvert vicinity and the progression of the depositional pattern over time (e.g., individual storms, seasons, and annual cycles). The data and information produced by our combined observational system lead to practical insights regarding the interplay between local hydraulic and geomorphological variables, which can be otherwise difficult to obtain. A automated data processing technique is applied in this study to automatically connect aerial photogrammetry results with the RTK-based ground survey to improve the accuracy of the monitoring.

A Database of Teleseismic Shear-Wave Splitting Measurements for the Ordos Block and Adjacent Areas

Abstract The Ordos block of the north China craton and its surrounding regions are affected by the India–Eurasia collision to the southwest and the subduction of the Pacific beneath the Eurasian plates to the east. To provide the foundation for delineating lithospheric deformation and asthenospheric flow beneath this tectonically diverse region, we have created a database of individual shear-wave splitting (SWS) parameters by applying a uniform set of data processing procedures. After automatic data processing and manual checking, a total of 16,228 pairs of well-defined PKS, SKKS, and SKS splitting parameters (fast orientations and splitting times) are obtained from 1023 broadband seismic stations that recorded data between 2007 and 2019. Along the western and southern margins of the Ordos block, the observed seismic anisotropy is attributable to mantle flow deflected by the relatively thick lithospheric root of the Ordos block. A clear back-azimuthal dependence of the fast orientations is observed at some of the stations located in the Alxa block, Ordos block, and Sichuan basin, indicating possible existence of complex anisotropic structures. The new SWS database can be employed by researchers in various fields to study lithospheric deformation and asthenospheric flow beneath the Ordos block and surrounding regions.

Evaluation of IMERG and ERA5 Precipitation-Phase Partitioning on the Global Scale

The precipitation phase (i.e., rain and snow) is important for the global hydrologic cycle and climate system. The objective of this study is to evaluate the precipitation-phase partitioning capabilities of remote sensing and reanalysis modeling methods on the global scale. Specifically, observation data from the National Centers for Environmental Prediction (NCEP) Automated Data Processing (ADP), from 2000 to 2007, are used to evaluate the rain–snow discrimination accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth-generation reanalysis product of the European Centre for Medium Range Weather Forecasts (ERA5). The results show that: (1) the ERA5 performs better than the IMERG at distinguishing rainfall and snowfall events, overall. (2) The ERA5 has high accuracy in all continents except for South America, while the IMERG performs well only in Antarctica and North America. (3) Compared with the IMERG, the ERA5 can more effectively capture snowfall events at high latitudes but shows worse performance at mid-low latitude regions. Both the IMERG and ERA5 have lower accuracy for rain–snow partitioning under heavy precipitation. Overall, the results of this study provide references for the application and improvement of global rain–snow partitioning products.

Sistem Informasi Pengolahan Data Pembelian Dan Penjualan Produk Usaha Pada Toko Mentari Berbasis Website

The development of technology is growing rapidly and there is many oportunity people could gain from that development, which also been implemented in the small and micro business development in various field to achieve its goals. Mentari Store is one of them. To ensure that data processing and report making of goods could be carried out properly, they need to change their management method from conventional into computerized and automated data processing methods. The data for this program is collected through interviews with the store owner, obvervation at the store, and through library studies. Using the Waterfall methodology and the Blackbox testing, this research is focused on software functionally to ensure that all parts already tested and successfully executed.

PERANCANGAN MEDIA PEMBELAJARAN INTERAKTIF MATA PELAJARAN IPA KELAS VI SD BERBASIS WEBSITE

The library is a center of knowledge that must be managed properly and correctly in an educational institution. SLBN 1 Linggo Sari Baganti is a school where all kinds of activities get more attention from the government, one of which is through the library. various assistance, facilities and infrastructure in the form of a collection of books are always provided. However, the operation of the library at SLBN 1 Linggo Sari Baganti is currently still managed in a conventional way. Starting from the data collection process to borrowing books is still done manually. Causing activities to be slow, inefficient and errors tend to be repeated frequently. So the Design of a Library Information System at SLBN 1 Linggo Sari Baganti Using Visual Basic 2010 Programming Language and Supported by Mysql Database is the right solution to overcome these problems. The results of the evaluation of the library system that has been designed show that the library system has features of computerized automation of data processing, transactions and the creation of important reports that have made it easier for librarians to manage the library.

Land objects size approximation informational technology

Despite rapid development of Big Data and data processing technologies, it is still quite a challenge to implement efficient pipelines for obtaining reliable and reasonable results. In re-mote sensing, thousands of gigabytes of data is acquired by satellite and aircraft vehicles, but only the modest amount of data is processed. Among the data processed, even less results re-mains reliable overtime due to the nature of satellite imagery. Nowadays, machine learning area affects nearly every domain of knowledge including remote sensing. A toolset of machine learning suggests ways to automate data processing which makes data pipelines constructure much more effective. Deep neural networks demonstrate the best results so far in image pro-cessing field. However, applying deep learning achievements in Remote Sensing still remain challenging in regard to multi-channel satellite imagery of a very-high resolution. Despite astonishing results deep learning field demonstrates in remote sensing competitions such as Open Street Map, these are applicable on regular RGB images of popular image formats such JPEG and similar. In current paper, a technology is suggested to apply deep neural network toolset in multispectral images of a very high resolution acquired from a commercial WorldView-3 satellite vehicle. The suggested approach covers topics of dataset preparation to fully replicate the results of the suggested neural network training. The proposed neural network solution output are segmentation maps. In addition to replicatable neural network structure, a particular use case is considered in detail which implements a fully automated technology of highly precise approximation of physical sizes of the land objects. The neural network performance is measured and compared to modern similar neural network solutions.

Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign

Abstract. Lidar observations were analysed to characterize atmospheric pollen at four EARLINET (European Aerosol Research Lidar Network) stations (Hohenpeißenberg, Germany; Kuopio, Finland; Leipzig, Germany; and Warsaw, Poland) during the ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) COVID-19 campaign in May 2020. The reanalysis (fully quality-assured) lidar data products, after the centralized and automatic data processing with the Single Calculus Chain (SCC), were used in this study, focusing on particle backscatter coefficients at 355 and 532 nm and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollen depolarization ratio was presented, based on the non-linear least square regression fitting using lidar-derived backscatter-related Ångström exponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and 532 nm should be zero (±0.5) for pure pollen, the pollen depolarization ratios were estimated: for Kuopio and Warsaw stations, the pollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during the birch-dominant pollen periods, whereas for Hohenpeißenberg and Leipzig stations, the pollen depolarization ratios of 0.21 (0.15–0.27) and 0.20 (0.15–0.25) were observed for periods of mixture of birch and grass pollen. The method was also applied for the aerosol classification, using two case examples from the campaign periods; the different pollen types (or pollen mixtures) were identified at Warsaw station, and dust and pollen were classified at Hohenpeißenberg station.

A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC

Paper-based data acquisition and manual transfer between incompatible software or data formats during inspections of bridges, as done currently, are time-consuming, error-prone, cumbersome, and lead to information loss. A fully digitized workflow using open data formats would reduce data loss, efforts, and the costs of future inspections. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information among different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focuses on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing.

Optical Coherence Tomography for 3D Weld Seam Localization in Absorber-Free Laser Transmission Welding

Quality and reliability are of the utmost importance for manufacturing in the optical and medical industries. Absorber-free laser transmission welding enables the precise joining of identical polymers without additives or adhesives and is well-suited to meet the demands of the aforementioned industries. To attain sufficient absorption of laser energy without absorbent additives, thulium fiber lasers, which emit in the polymers’ intrinsic absorption spectrum, are used. Focusing the laser beam with a high numerical aperture provides significant intensity gradients inside the workpiece and enables selective fusing of the internal joining zone without affecting the surface of the device. Because seam size and position are crucial, the high-quality requirements demand internal weld seam monitoring. In this work, we propose a novel method to determine weld seam location and size using optical coherence tomography. Changes in optical material properties because of melting and re-solidification during welding allow for weld seam differentiation from the injection-molded base material. Automatic processing of the optical coherence tomography data enables the identification and measurement of the weld seam geometry. The results from our technique are consistent with microscopic images of microtome sections and demonstrate that weld seam localization in polyamide 6 is possible with an accuracy better than a tenth of a millimeter.