Post-processing Data Research Articles

ApoNecV: A macro for cell death type differentiation.

The evaluation of large experimental datasets is a fundamental aspect of research in every scientific field. Streamlining this process can improve the reliability of results while making data analysis more efficient and faster to execute. In biomedical research it is often very important to determine the type of cell death after various treatments. Thus, differentiating between viable, apoptotic, and necrotic cells provide critical insights into the treatment efficacy, a key aspect in the field of drug development. Fluorescent microscopy is perceived as a widely used technique for cell metabolism assessment and can therefore be used to investigate treatment outcomes after staining samples with cell death detection kit. However, accurate evaluation of therapeutic results requires quantitative analysis, often necessitating extensive postprocessing of imaging data. In this study, we introduce a complementary tool designed as a macro for the Fiji platform, enabling the automated postprocessing of fluorescent microscopy images to accurately distinguish and quantify viable, apoptotic, and necrotic cells.

A program for modeling the RF wave propagation of ICRF antennas utilizing the finite element method

Abstract Controlled nuclear fusion represents a significant solution for future clean energy, with Ion Cyclotron Range of Frequency (ICRF) heating emerging as one of the most promising technologies for heating the fusion plasma. This study primarily presents a self-developed 2D Ion Cyclotron Resonance Antenna Electromagnetic Field Solver (ICRAEMS) code implemented on the MATLAB platform, which solves the electric field wave equation by using the finite element method, establishing Perfectly Matched Layer (PML) boundary conditions, and post-processing electromagnetic field data. This code can be utilized to facilitate the design and optimization processes of antennas for ICRF heating technology. Furthermore, this study examines the electric field distribution and power spectrum associated with various antenna phases to investigate how different antenna configurations affect electromagnetic field propagation and coupling characteristics.

Correction of ERA5 temperature and relative humidity biases by bivariate quantile mapping for contrail formation analysis

Abstract. Aviation contributes to global emissions of carbon dioxide, aerosol particles, water vapor (WV), and other compounds. WV promotes the formation of condensation trails (contrails), which are known for their net warming effect on the climate. Contrail formation is often estimated using the Schmidt–Appleman criterion (SAc) together with meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric reanalysis model. We compare ERA5 output of temperature and relative humidity in the upper troposphere and lower stratosphere with 5 years of In-service Aircraft for a Global Observing System (IAGOS) observations over the North Atlantic. Good agreement was found for the temperature fields, with a maximum bias of −0.4 K (200 hPa level), while larger biases were found for relative humidity of up to −5.5 % (250 hPa level). Using original ERA5 data, conditions prone to contrail formation occurred 50.3 % and 7.9 % of the time for non-persistent and persistent contrails, respectively, while 44.0 % and 12.1 % were flagged in the IAGOS data. We propose a multivariate quantile mapping (QM) correction to remove systematic biases by post-processing ERA5 temperature and relative humidity fields with respect to contrail formation. The QM correction was applied to post-process ERA5 data, reducing the temperature bias to less than 0.1 K and the relative humidity bias to less than −1.5 %, resulting in 44 % and 10.9 % of the data points now being flagged for non-persistent and persistent contrail formation, respectively. Our bias correction generalizes well compared to the IAGOS observations. How it generalizes outside the IAGOS regions remains to be investigated.

АВТОМАТИЧЕСКИЕ СИСТЕМЫ УПРАВЛЕНИЯ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ ПЕРЕВОЗОК С РАСШИРЕННЫМ КОНТУРОМ АНАЛИТИКИ ДАННЫХ

A new hybrid approach has been proposed to automate the management of complex technological processes at railway stations of industrial transport using intelligent monitoring technologies. This approach is based on the concept of predictive modeling combined with methods of statistical analysis, including a modification of the principal components analysis method for multivariate statistical analysis and the identification of violations in technological processes using a combination of well-known methods such as contribution analysis and fuzzy dynamic analysis. The principal feature of the hybrid approach is mapping the initial space of numerical parameters of the technological process onto a new space formed by fuzzy rules of an evolving system model. Applying multivariate analysis to new system variables using the principal component method allows for the formation of a few intermediate variables with different degrees of granularity and interpretability, describing the behavior of the controlled process, which makes it possible to develop mathematical models and algorithms for solving various monitoring tasks An example of using this approach for post-processing monitoring data to identify performance discrepancies in a marshalling yard and anomalies in the controlled process is considered.

Simultaneous Temperature and Relative Humidity Measurement Using Machine Learning in Rayleigh-Based Optical Frequency Domain Reflectometry.

This paper presents, for the first time to the best of our knowledge, simultaneous temperature and relative humidity (RH) measurement using a machine learning (ML) model in Rayleigh-based Optical Frequency Domain Reflectometry (OFDR). The sensor unit consists of two segments: bare and polyimide-coated fibers, each with different sensitivities to temperature. The polyimide-coated fiber is RH-sensitive, unlike the bare fiber. We propose the ML approach to avoid manual post-processing data and maintain relatively high accuracy of the sensor. The root mean square error (RMSE) values for the 3 cm length of the sensor unit were 0.36 °C and 1.73% RH for temperature and RH, respectively. Furthermore, we investigated the impact of sensor unit lengths and number of data points on RMSE values. This approach eliminates the need for manual data processing, reduces analysis time, and enables accurate, simultaneous measurement of temperature and RH in Rayleigh-based OFDR.

Investigating the potential of ICEYE-SAR data in storm damage detection in a coniferous forest with rugged terrain

ABSTRACT Adaptive forest management strategies require accurate detection of forest disturbance, in various spatial scales. Synthetic Aperture Radar (SAR) data can provide information about the forest attributes, penetrating the canopy at different levels under any weather and lighting conditions. ICEYE consists of the largest constellation of SAR satellites, enabling very high spatial and temporal resolution. In this study, ICEYE data were investigated in the detection of storm damage, in a fir forest with complex topography which was recently hit by the Daniel storm, resulting in severe damage to the forest structure (FS) and alluvium depositions (AD). To identify the best potential for storm damage detection using ICEYE data, an unsupervised change detection approach was employed combining wavelet transform and adaptive thresholds at spatial scales of 0.5 m (R05), 1 m (R1), 2 m (R2) and 3 m (R3). Additionally, two morphological filters were applied in best-performing resolutions to assess the impact of post-processing on the detection accuracy. Finally, FS and AD damage were investigated separately in order to provide detailed information about the detection capabilities of ICEYE. The results showcased that R1 (UA = 32.35, PA = 18.34 and K = 0.31) provided the best detection performance, followed by R05 (UA = 20.62, PA = 20.12 and K = 0.19). Furthermore, the employment of morphological filters slightly increased UA and Kappa metrics in both R1(UA = 33.40 and K = 0.32) and R05 (UA = 25.60 and K = 0.24), suggesting that post-processing is necessary to mitigate false detections. Regarding the investigation of AD and FS damage, it was revealed that post-processed ICEYE data in both R05 and R1 are capable of identifying AD with satisfying accuracy (UA = 47.41, PA = 22.36, K = 0.47), while FS damage detection is more challenging (UA = 10.15%-14.40%, PA = 14.55%-15.11%, and Kappa = 0.10-0.17). Overall, this study demonstrated that ICEYE can be used to detect storm-affected areas in mountainous forest ecosystems, especially in cases where detection with other methods is not feasible.

Workflow for high-quality visualisation of large-scale CFD simulations by volume rendering

High-fidelity CFD simulations can easily generate terabytes to petabytes of resulting data. Post-processing of such data is not an easy task. It holds especially for volume rendering, one of the most illustrative but computationally intensive post-processing techniques.This paper presents an HPC-ready workflow for post-processing large-scale CFD data computed on unstructured meshes by volume rendering using matured visual effects tools. The workflow consists of five steps: (1) parallel loading of unstructured data into memory, (2) data load-balancing among available resources, (3) re-sampling unstructured data into a regular grid (voxelisation), (4) storing data to OpenVDB format, and (5) final high-quality volume rendering of the (possibly sparse) regular grid in Blender. The workflow is based on open-source libraries, where we have improved all these steps to build an effective and robust approach. Due to parallel loading and appropriate load balancing, our workflow (a) allows loading sequential databases that do not fit into the memory of a single node and (b) significantly outperforms current scientific visualisation tools in voxelisation scalability. Moreover, due to the connection to professional visual effects tools such as Blender, interactive or photo-realistic volume rendering by path tracing, which includes global illumination effects, is allowed.With the workflow, it is possible to re-sample hundreds of time steps on an unstructured mesh with 1 billion cells (tens of TB of data) to a sparse regular grid with a density of 11 billion voxels and prepare data for interactive visualisation in just a few minutes using thousands of CPU cores.

Synthetic Wind Estimation for Small Fixed-Wing Drones

Wind estimation is crucial for studying the atmospheric boundary layer. Traditional methods such as weather balloons offer limited in situ capabilities; besides an Air Data System (ADS) combined with inertial measurements and satellite positioning is required to estimate the wind on fixed-wing drones. As pressure probes are an important constituent of an ADS, they are susceptible to malfunctioning or failure due to blockages, thus affecting the capability of wind sensing and possibly the safety of the drone. This paper presents a novel approach, using low-fidelity aerodynamic models of drones to estimate wind synthetically. In our work, the aerodynamic model parameters are derived from post-processed flight data, in contrast to existing approaches that use expensive wind tunnel calibration for identifying the same. In sum, our method integrates aerodynamic force and moment models into a Vehicle Dynamic Model (VDM)-based navigation filter to yield a synthetic wind estimate without relying on an airspeed sensor. We validate our approach using two geometrically distinct drones, each characterized by a unique aerodynamic model and different quality of inertial sensors, altogether tested across several flights. Experimental results demonstrate that the proposed cross-platform method provides a synthetic wind velocity estimate, thus offering a practical backup to traditional techniques.

Research on filling missing GNSS precipitable water vapor time series data using the PSORF model combined with reanalysis datasets

Abstract The inversion of precipitable water vapor (PWV) using the Global Navigation Satellite System (GNSS) has advantages such as all-weather observation, high precision, low cost, and high temporal resolution. Currently, long-term GNSS-PWV data has become an important data source for studying climate change. However, due to factors such as equipment failures, observation technology limitations, and estimation model errors, missing data and outliers often occur in real-time or post-processed PWV time series data. Furthermore, the main sources of GNSS-PWV errors are influenced by the atmospheric weighted mean temperature and surface meteorological data (pressure and temperature). The results indicate that the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset exhibits high accuracy in the Chinese region, making it suitable for GNSS-PWV inversion. By utilizing ERA5 meteorological data to calculate hourly GNSS-PWV and conducting accuracy assessments, it is demonstrated that the PWV inverted based on GNSS and ERA5 meteorological parameters possesses high precision. Based on this, this study selects GNSS stations from the Crustal Movement Observation Network of China where the proportion of missing measured data is less than 8%. By combining ERA5, random forest (RF), and particle swarm optimization (PSO) algorithms, a new model called PSORF is proposed to fill in missing values in GNSS-PWV time series data. The research findings reveal that the R 2 and root mean square error (RMSE) of PSORF-PWV are 0.98 and 2.16 mm, respectively. Additionally, GNSS stations with more than 8% missing measured data are utilized to validate the accuracy of the PSORF model. A comparative analysis is conducted between the results obtained through the PSORF model and the ERA5-PWV acquired via traditional interpolation methods. The MAE and RMSE of PSORF-PWV are reduced by 21% and 17%, respectively, indicating that the PSORF model excels in filling missing data and effectively enhances the accuracy and reliability of PWV time series analysis. This study not only presents an effective approach for processing missing PWV data but also evaluates the applicability and accuracy of the ERA5 dataset in PWV inversion. This provides crucial technical support and data security for climate change research, short-term humidity field forecasting, and studies in related fields.

Development of A deep Learning-based algorithm for High-Pitch helical computed tomography imaging

High-pitch X-ray helical computed tomography (HCT) imaging has been recently drawing considerable attention in biomedical fields due to its ability to reduce the scanning time and thus lower the radiation dose that objects (being imagined) may receive. However, the issue of compromised reconstruction quality caused by incomplete data in these high-pitch CT scans remains, thus limiting its applications. By addressing the aforementioned issue, this paper presents our study on the development of a novel deep leaning (DL)-based algorithm, ViT-U, for high-pitch X-ray propagation-based imaging HCT (PBI-HCT) reconstruction. ViT-U consists of two key process modules of a vision transformer (ViT) and a convolutional neural network (i.e., U-Net), where ViT addresses the missing information in the data domain and U-Net enhances the post data-processing in the reconstruction domain. For verification, we designed and conducted simulations and experiments with both low-density-biomaterial samples and biological-tissue samples to exemplify the biomedical applications, and then examined the ViT-U performance with varying pitches of 3, 3.5, 4, and 4.5, respectively, for comparison in term of radiation does and reconstruction quality. Our results showed that the high-pitch PBI-HCT allowed for the dose reduction from 72% to 93%. Importantly, our results demonstrated that the ViT-U exhibited outstanding performance by effectively removing the missing wedge artifacts thus enhancing the reconstruction quality of high-pitch PBI-HCT imaging. Also, our results showed the superior capability of ViT-U to achieve high quality of reconstruction from the high-pitch images with the helical pitch value up to 4 (which allowed for the substantial reduction of radiation doses). Taken together, our DL-based ViT-U algorithm not only enables high-speed imaging with low radiation dose, but also maintains the high quality of imaging reconstruction, thereby offering significant potentials for biomedical imaging applications.

An open-source, adaptive solver for particle-resolved simulations with both subcycling and non-subcycling methods

We present the IAMReX (incompressible flow with adaptive mesh refinement for the eXascale), an adaptive and parallel solver for particle-resolved simulations on the multi-level grid. The fluid equations are solved using a finite-volume scheme on the block-structured semi-staggered grids with both subcycling and non-subcycling methods. The particle-fluid interaction is resolved using the multidirect forcing immersed boundary method. The associated Lagrangian markers used to resolve fluid-particle interface only exist on the finest-level grid, which greatly reduces memory usage. The volume integrals are numerically calculated to capture the free motion of particles accurately, and the repulsive potential model is also included to account for the particle–particle collision. We demonstrate the versatility, accuracy, and efficiency of the present multi-level framework by simulating fluid-particle interaction problems with various types of kinematic constraints. The cluster of monodisperse particles case is presented at the end to show the capability of the current solver in handling multiple particles. It is demonstrated that the three-level AMR (Adaptive Mesh Refinement) simulation leads to a 72.46% grid reduction compared with the single-level simulation. The source code and testing cases used in this work can be accessed at https://github.com/ruohai0925/IAMR/tree/development. Input scripts and raw postprocessing data are also available for reproducing all results.

Assessment of offshore wind conditions in coastal areas of Japan using single scanning Doppler LiDAR

Abstract In this study, offshore wind conditions in coastal areas of Japan measured by single scanning Doppler lidar is investigated. The effects of measurement range as well as rainfall and snowfall on data availability of the Doppler scanning lidar are examined. Data filtering criteria are then proposed and verified by data thinning to meet both accuracy and post-processed data availability requirements for offshore wind measurements at multiple altitudes. Finally, one-year offshore wind measurement with three different elevation angles is conducted using a single Doppler scanning lidar to investigate wind conditions in the coastal region of Northern Japan. It is found that the accuracy of 15-second average wind speed measurements by PPI (Plan Position Indicator) scan depends on the sector size. An accurate 10-minute mean wind is measured when the sector size is larger than 39 degrees and proportion of valid data acquisition is more than 10% of the time duration. The vertical and horizontal distributions of offshore wind speed in different wind directions are also analyzed and the effects of onshore topography on offshore wind conditions are clarified.

Advancing elemental analysis by collision/reaction cell technology and micro-droplet calibration for bioimaging applications by LA-ICP-TOFMS

BackgroundBioimaging applications by laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOFMS) require comprehensive elemental analysis spanning the entire mass range. Within biological systems, endogenous elements are critical for maintaining metal homeostasis in cells, a fundamental aspect in disease progression when disrupted. Additionally, elements from the higher mass range may originate from various sources such as metal-tags for immuno-mass spectrometry, nanoparticles, or metal-based anticancer drugs. This study assesses the efficacy of collision/reacton cell (CCT) mode for simultaneously analysing elements across the complete mass spectrum. Furthermore, we demonstrate the accuracy of the LA-ICP-TOFMS methodology through the analysis of serum reference material. ResultsOur findings demonstrate that the CCT mode outperforms the standard/no gas mode for LA-ICP-TOFMS measurements, particularly in quantifying endogenous elements susceptible to interferences. Through the analysis of picolitre-volume micro-droplet standards and serum reference material (deposited as micro-droplets), we observed accurate quantification of elements such as iron and selenium, with isotope ratios closely resembling natural compositions. As key advantage, the utilization of the CCT mode eliminated the need for labor-intensive post-data processing, streamlining analytical procedures. Additionally, the CCT mode also provided enhanced sensitivity (factor of 1.5–2) for elements in the higher mass range compared to the standard mode, without compromising the sensitivity for endogenous elements. SignificanceWe successfully integrated Seronorm serum reference material-containing micro-droplets into LA-ICPMS bioimaging workflows for quality control, enabling validated measurements of key biological elements. The use of the CCT mode is highly recommended for bioimaging applications that address the analysis of elements across the entire mass range.

Evaluation of urinary volatile organic compounds as a novel metabolomic biomarker to assess chronic kidney disease progression

BackgroundThere is a need to develop accurate and reliable non-invasive methods to evaluate chronic kidney disease (CKD) status and assess disease progression. Given it is recognized that dysregulation in metabolic pathways occur from early CKD, there is a basis in utilizing metabolomic biomarkers to monitor CKD progression. Volatile Organic Compounds (VOCs), a form of metabolomic biomarker, are gaseous products of metabolic processes in organisms which are typically released with greater abundance in disease conditions when there is dysregulation in metabolism. How urinary VOCs reflect the abnormal metabolic profile of patients with CKD status is unknown. Our study aimed to explore this.MethodsIndividuals aged 18–75 years undergoing kidney biopsy were included. Pre-biopsy urine samples were collected. All biopsy samples had an interstitial fibrosis and tubular atrophy (IFTA) grade scored by standardized assessment. Urine supernatant was extracted from residue and sampled for stir bar sorptive extraction followed by Gas chromatography–mass spectrometry (GC-MS) analysis. Post-processing of GC-MS data separated complex mixtures of VOCs based on their volatility and polarity. Mass-to-charge ratios and fragment patterns were measured for individual VOCs identification and quantification. Linear discriminant analysis (LDA) was performed to assess the ability of urinary VOCs in discriminating between IFTA 0 (‘no or minimal IFTA’ i.e. <10%, IFTA), IFTA 1 (‘mild IFTA’ i.e. 10–25% IFTA) and IFTA ≥ 2 (‘moderate or severe IFTA’ i.e. >25% IFTA). Linear regression analysis adjusting for age, sex, estimated glomerular filtration rate, diabetes mellitus (DM) status, and albuminuria was conducted to determine significantly regulated urinary VOCs amongst the groups.Results64 study participants (22 individuals IFTA 0, 15 individuals IFTA 1, 27 individuals IFTA ≥ 2) were included. There were 34 VOCs identified from GC-MS which were statistically associated with correct classification between the IFTA groups, and LDA demonstrated individuals with IFTA 0, IFTA 1 and IFTA ≥ 2 could be significantly separated by their urinary VOCs profile (p < 0.001). Multivariate linear regression analysis reported 4 VOCs significantly upregulated in the IFTA 1 compared to the IFTA 0 group, and 2 VOCs significantly upregulated in the IFTA ≥ 2 compared to the IFTA 1 group (p < 0.05). Significantly upregulated urinary VOCs belonged to one of four functional groups - aldehydes, ketones, hydrocarbons, or alcohols.ConclusionsWe report novel links between urinary VOCs and tubulointerstitial histopathology. Our findings suggest the application of urinary VOCs as a metabolomic biomarker may have a useful clinical role to non-invasively assess CKD status during disease progression.

A waypoint based approach to visibility in performance based fire safety design

In performance based fire safety design, ensuring safe egress, e.g. by visibility of safety signs, is a crucial safety goal. Compliance with the building requirements is often demonstrated by simulations of smoke spread. Numerical models like the Fire Dynamics Simulator generally compute visibility as a local quantity using the light extinction coefficient, without the consideration of the actual light path to a safety sign. Here, visibility maps are introduced, providing an approach for post-processing fire simulation data. They indicate safe areas along egress routes, with respect to visibility. At each location, the available visibility is calculated using Jin’s empirical relation, as an integrated value of the extinction coefficient along the line of sight to the closest exit sign. The required visibility results from the distance between those points. Additional parameters like view angle or visual obstructions are considered. The presented method allows for temporal visibility assessment, e.g. in an ASET-RSET analysis.

A feasibility study on the use of an intraoral optical coherence tomography system for scanning the subgingival finish line for the fabrication of zirconia crowns: An evaluation of the marginal and internal fit

ObjectivesThis study aimed to evaluate the marginal and internal fit of zirconia crowns were fabricated using scan data from an intraoral optical coherence tomography (OCT) scanner and an intraoral scanner (IOS) for scanning the subgingival finish line. MethodsAn extracted maxillary left central incisor was prepared for a zirconia crown. The prepared tooth was placed in artificial gingiva, created using silicone with a refractive index similar to that of the tooth, ensuring a subgingival depth of 0.50 to 0.70 mm from the labial finish line. Scanning data were obtained from four types of models as follows. (1) CAD reference model (CRM) excluding the gingiva and scanned using a laboratory scanner. (2) IOS group excluding the gingiva (IOS only, IOSO group). (3) IOS group with scanned attached artificial (IOS with gingiva, IOSG group). (4) OCT post-processed data of the subgingival finish line and IOSG data (OCT group). Zirconia crowns were fabricated based on these data, and their marginal and internal fit were evaluated using the silicone replica technique. Statistical analyses were conducted using one-way and two-way ANOVA (α = 0.05). ResultsThe OCT group exhibited a significantly smaller marginal gap than the IOSG group (P < 0.05). The marginal fit of the OCT group did not significantly differ from that of the CRM group (P > 0.05). The IOSG group exhibited a significantly larger chamfer gap, while both the IOSG and OCT groups had significantly larger axial gaps. Furthermore, the OCT group showed a significantly larger incisal gap (P < 0.05). ConclusionsAn intraoral OCT system can enhance the fabrication accuracy of zirconia crowns by achieving superior marginal fit for crowns with subgingival finish lines. Clinical significanceThe use of an IOS for subgingival finish lines without gingival displacement cords may result in a suboptimal marginal fit. However, integrating OCT technology can effectively address this issue, leading to improved clinical outcomes.

Real-Time Monitoring of Seawater Quality Parameters in Ayia Napa, Cyprus

Real-time monitoring systems are crucial for the comprehensive management of operations and processes, as well as for assessing the impacts of coastal infrastructures on the marine environment. These systems not only support environmental protection and data-driven decision-making but also enable the early detection of adverse events and the issuance of timely warnings for prompt responses. Although water quality is a critical parameter in this monitoring framework, there are currently limited permanent systems in place dedicated to maintaining these objectives. Even fewer systems leverage their data for research purposes, leading to a gap in the literature regarding effective processing approaches for real-time water quality data. In this context, this study presents a real-time water quality monitoring system integrated into a broader in-field laboratory installed at a coastal area off the coast of Ayia Napa, Cyprus, as well as an initial measured data set of different qualitative quantities. It proposes a holistic approach for post-processing real-time seawater quality data, employing both time and frequency domain analyses, alongside filtering techniques. The study discusses the advantages of each method and emphasizes the importance of their combined use. Utilizing data collected from a three-month operational period, the study assesses the current state of marine seawater quality and examines both temporal and cyclic variations in various seawater quality parameters. The findings reveal that the examined seawater parameters are within reasonable values, indicating that the construction and operation of a nearby marina and the necessary infrastructures (e.g., breakwater) did not affect the seawater quality in the area. Additionally, the study identifies pronounced daily cyclic responses in different seawater quality parameters, including temperature, density, pH, dissolved oxygen, and turbidity. Finally, notable correlations are observed between temperature and dissolved oxygen, temperature and conductivity, oxidation–reduction potential (ORP) and salinity, ORP and dissolved oxygen, and ORP and TDS.

Integrating geodetic infrastructures for GNSS displacements analysis in Chile: A case study with REDGEOMIN (2019–2022)

This article investigates crustal displacements in Chile between 2019 and 2022, a region marked by the interaction between three Plates. Utilizing the Red Geodésica para la Minería (Geodetic Network for Mining, REDGEOMIN), which incorporates GNSS stations within Chile and fiduciary stations from the International GNSS Service (IGS) and the Sistema de Referencia Geodésico para las Américas (Geodetic Reference System for the Americas, SIRGAS) outside the country, our solutions are aligned with the International Terrestrial Reference Frame (ITRF). This approach ensures our research has significant local relevance and contributes to the global scientific community. This work thoroughly analyses GNSS geodetic infrastructure and covers equipment specifications, post-processing data availability, and quality control measures. REDGEOMIN's methodology aligns with IGb14 and the SIRGAS-Continuously Operating Network (SIRGAS-CON), ensuring precision and reliability. The primary objective of this research is to identify regions with significant displacement and areas requiring densification to improve vector variation. The study achieved high precision in aligning station coordinates to the IGb14 frame, with deviations of less than 1 mm, and to SIRGAS-CON, with deviations of 1 mm in planimetry and 2 mm in altimetry. The temporal evolution from 2019 to 2022, analyzed through the Analysis Deformation Beyond Los Andes (ADELA) project, reveals 3–5 cm/year displacements during interseismic periods, with contrasting directions in the northern and south-central regions. Metric displacements during co-seismic periods underscore the compelling necessity for kinematic reference frames in Chile and the incorporation of co-seismic displacement into scientific processing software. Therefore, this study ultimately emphasizes the need to establish a robust geodetic infrastructure in Chile, aiming for a comprehensive characterization and monitoring of the existing geodynamic processes in this region. These results, with their significant practical applications for future geodetic research and mining in Chile and internationally, enhance the precision and safety of mining operations in seismic zones, thereby demonstrating the relevance and applicability of the findings.

Enhanced hybrid LSTM and SLAR modeling for in-depth analysis of temporal and spatial patterns in compositional data for environmental monitoring

A novel methodology for analyzing compositional data (CoDa) integrates long short-term memory (LSTM) networks with spatial lag autoregressive (SLAR) models to simultaneously capture temporal and spatial patterns. The proposed approach leverages LSTM’s strengths in handling temporal relationships and SLAR’s ability to capture spatial correlations, employing the centered log-ratio (CLR) transformation to manage CoDa’s characteristics, making it suitable for advanced analysis. The Adam optimizer ensures efficient and stable model training, enhancing convergence speed and precision. The combined approach involves preprocessing CoDa with CLR, initializing LSTM layers, performing forward passes through LSTM, integrating the SLAR model, generating outputs, post-processing data, calculating loss, and performing backpropagation. The methodology’s effectiveness is demonstrated through a case study on pollutant emissions data from a waste incineration plant, focusing on key pollutants such as Dioxins and Furans, Heavy Metals (Hg, Pb, Cd), Nitrogen Oxides (NO), Sulfur Dioxide (SO), Particulate Matter (PM), and Carbon Monoxide (CO). The LSTM network, configured with two hidden layers, dynamically adjusts learning rates for faster convergence. Integrating SLAR improves spatial pattern accounting, significantly enhancing predictive accuracy. Comparative analysis shows substantial performance metric improvements over standard LSTM models, with the LSTM-SLAR model reducing errors and explaining a higher proportion of data variability. In process safety and risk engineering, the proposed methodology enhances pollutant emission monitoring and control, enables proactive risk management, ensures regulatory compliance, and improves risk assessment accuracy, making it invaluable for dynamic risk assessment in environmental monitoring and is broadly applicable to fields requiring detailed temporal and spatial analysis, demonstrating robustness and versatility in handling complex CoDa.

Validation of a Methodology to Assess The Flutter Limit Cycle Oscillation Amplitude of Low-Pressure Turbine Bladed-Disks - Part I: Mach Number Effects

Abstract This paper presents a methodology to estimate the vibration amplitude of fluttering low-pressure turbine blades saturated due to friction effects. The study utilizes an analytical model that balances aerodynamic work and dry-friction work. The analytical predictions are compared against experimental results to validate the model. The first part of this paper focuses on the influence of the Mach number on the work balance between aerodynamic and mechanical components. It is observed that the vibration amplitude of low-pressure turbine rotor blades notably increases with higher Mach numbers. In addition, numerical simulations are employed to assess the influence of the Mach number on the critical damping ratio. The results demonstrate that an appropriate scaling of the critical damping ratio with the exit Mach number collapses all the damping versus inter-blade phase angle curves into a single curve. This finding validates the scaling of the aerodynamic damping for different pressure ratios. Unsteady pressure measurements were acquired, carefully post-processed to extract their flutter-induced peak components, and presented in a nodal diameter by nodal diameter basis. The post-processed data were then used to characterize the vibration amplitude observed in the experiments. The trends of the measured unsteady pressure on the casing of a rotating rig and the proposed model with the Mach number for different shaft speeds are in good agreement. The vibration amplitude and the mean unsteady pressure increase with the Mach number and exhibit a maximum with the shaft speed.