Post-processing Data Research Articles

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.

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.

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 (Tm) 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 (CMONOC) 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 R2 and 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.

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.

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.

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.

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 to 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.

ARCHITECTURE DESIGN OF HEALTH ASSET DETECTION SYSTEM IN HOSPITAL

Efficient management of hospital assets is essential to ensure that operations can run optimally and the quality of health services is good. However, the recording and management of assets in hospitals carried out manually often causes data errors, information mismatches, and also assets are only known by the manager without being explicitly recorded. In overcoming this problem, the researcher aims to develop a hospital asset detection system architecture using an iterative and incremental methodology approach. The stages of this system development include identification of needs and conceptual models, logical architecture design, conceptual architecture design, logical architecture, physical architecture, technology selection, and evaluation. This system utilizes YOLO model reading technology for asset detection and identification, storing detection results into a local database using SQLite3, sending data to a central server via API, and post-processing data by selecting the highest confidence score stored in a MySQL database and then using the data to manage asset management and asset visualization. The implementation of this system successfully reduces manual recording time, improves asset visibility, and optimizes resource usage, thus contributing to the improvement of efficiency and quality of health services.

Exploring the dynamic compression behavior of glass/epoxy composite through cutting-edge numerical modeling techniques

Experimental tests are essential for obtaining reliable results. However, fabricating specimens, conducting tests, and post-processing experimental data are both costly and time-consuming. A reliable numerical model that matches experimental results can help avoid these issues. Using commercial ABAQUS explicit finite element software, we have developed numerical models that accurately replicate experimental outcomes while significantly reducing computation time. In this paper, we examine mesh convergence to ensure the precision and reliability of our numerical models. We detail the progression from a full model (FM) to a reduced model with infinite elements (RM-WIE) and its quarter version (QM-WIE). For non-reflective boundary conditions in the Split Hopkinson Pressure Bar (SHPB) tests, we used CIN3D8 infinite elements in the RM-WIE and QM-WIE models. This approach yields substantial time savings without sacrificing accuracy, reducing computation time from over 22 minutes for the full model to 15 minutes for RM-WIE and 11 minutes for QM-WIE. Moreover, our validation efforts demonstrate a strong numerical-experimental correlation of SHPB global parameters for E-Glass/Epoxy composites across four distinct lay-up configurations: [0°]40, [±30°]20, [±45°]20, and [90°]40, with an error margin of less than 10 %. This validation not only highlights the effectiveness of our numerical models but also underscores their utility in expediting the design process of E-Glass/Epoxy composites, offering a reliable and cost-effective alternative to traditional experimental testing methodologies.

An open-source tool for automated human-level circling behavior detection

Quantitatively relating behavior to underlying biology is crucial in life science. Although progress in keypoint tracking tools has reduced barriers to recording postural data, identifying specific behaviors from this data remains challenging. Manual behavior coding is labor-intensive and inconsistent, while automatic methods struggle to explicitly define complex behaviors, even when they seem obvious to the human eye. Here, we demonstrate an effective technique for detecting circling in mice, a form of locomotion characterized by stereotyped spinning. Despite circling's extensive history as a behavioral marker, there currently exists no standard automated detection method. We developed a circling detection technique using simple postprocessing of keypoint data obtained from videos of freely-exploring (Cib2−/−;Cib3−/−) mutant mice, a strain previously found to exhibit circling behavior. Our technique achieves statistical parity with independent human observers in matching occurrence times based on human consensus, and it accurately distinguishes between videos of wild type mice and mutants. Our pipeline provides a convenient, noninvasive, quantitative tool for analyzing circling mouse models without the need for software engineering experience. Additionally, as the concepts underlying our approach are agnostic to the behavior being analyzed, and indeed to the modality of the recorded data, our results support the feasibility of algorithmically detecting specific research-relevant behaviors using readily-interpretable parameters tuned on the basis of human consensus.

Automatic Detection of Anomalies in Post-Processed Data Applied to UTC Time Transfer Links.

In this article, we present a method for identifying anomalies, particularly time steps, which can affect data. Recognition of these anomalies is essential for understanding the intrinsic nature of problems that may occasionally affect the data, and for guaranteeing system reliability and accuracy. The tool presented, based on the Kalman filter, is optimized to work with post-processed data which means that the dataset is available at the time the algorithm is run. The main aim is to retain as much data as possible, while detecting anomalies, and avoid deleting valuable data. The originality of this tool with respect to the already existing Kalman-filter-based tools for detecting anomalies is substantial, because its objective is not only to enable the system to run but also to avoid unnecessary deletion of valuable data. This tool is designed to accurately determine the date of occurrence and magnitude of these anomalies, focusing on time steps. The tool presented will be applied, by way of example, to the time links used in Coordinated Universal Time (UTC) as calculated by Bureau International des Poids et Measures (BIPM), Paris, France. In addition, the algorithm developed will enable the BIPM's Time Department to be rapidly alerted to unexpected behavior that may compromise UTC performance. To guarantee the reliability and accuracy of UTC, rigorous data validation and rapid problem identification are essential.

Towards a momentum potential theory for reacting flows

Mutual coupling of (thermofluiddynamic) modes of perturbations can affect the thermo-acoustic stability of combustors and contribute to combustion noise. For example, vortical or entropic perturbations can be transferred to acoustic perturbations if accelerated by the mean flow. The decomposition of perturbation fields into the respective modes and a linear description of their interactions in terms of fluctuating primitive variables is challenging. In contrast, Doak’s momentum potential theory promises an unambiguous decomposition in terms of momentum fluctuations, which is not limited to the linear regime. Whereas classical momentum potential theory takes into account hydrodynamic, acoustic and entropic modes in unconfined flows, the investigation of noise generation in combustion chambers requires the extension of the momentum potential theory to capture modes linked to the fluctuation of species mass fractions (“species mode”) arising from the change in chemical composition due to the reaction. Furthermore, a rigorous treatment of boundary conditions due to the confinement of the flow inside the combustor is required. The herein presented extension to reactive flows consists of two steps, (i) the formulation of a potential for momentum fluctuations related to species modes and (ii) identification of the total fluctuating enthalpy related to species modes. The extended theory is applied to post-process computational fluid dynamic simulation data of the propagation of entropy and species perturbations through one-dimensional ducts, nozzles and premixed flames. We find that although momentum potential theory offers a complete decomposition of momentum perturbations for reactive flows, the meaningful interpretation of this decomposition is rather challenging, even for non-reactive flows.

Semi-automated minimization of brick-mortar segmentation errors in 3D historical wall reconstruction

In current practices of geometric modeling and building information modeling (BIM) of architectural heritage, point cloud data acquired from site surveys is segmented into parts that correspond to actual building elements. Deep learning methods are used to automate the segmentation processes; however, each historical building presents unique challenges due to distinct construction techniques, irregular applications, deformations, and wear on the surveyed surfaces, and due to these peculiarities, segmented point cloud data may display local errors in the detection of individual architectural elements. This paper presents a method for post-processing segmented point cloud data to semi-automatically detect building elements such as bricks in historical walls. Results show that this approach, which involves testing neighborhoods of points and classified areas to identify and correct misclassified points, diminishes segmentation errors and reconstructs bricks close to their original form.

XCP-D: A Robust Pipeline for the Post-Processing of fMRI data

Abstract Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they may not support output from different pre-processing pipelines, may have limited documentation, and may not follow generally accepted data organization standards (e.g. BIDS). In response, we present XCP-D: a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University of Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Apptainer image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NIfTI or CIFTI files following pre-processing with fMRIPrep, HCP, or ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded &amp;gt;5,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.

A novel multi-fidelity optimization framework for high-altitude propellers

A novel optimization methodology is presented for optimizing high-altitude propellers operating in the lower stratosphere. The proposed methodology combines a Bayesian optimization approach for most design variables and Vortex Theory for the optimization of twist distribution and rotational velocity. For the Bayesian optimization part of the problem, a multi-fidelity approach is employed with three levels of modeling fidelity. The considered fidelity levels, from lower to higher, are: Vortex Theory, and 3D Reynolds-Averaged Navier-Stokes (RANS) with the use of γ−Reθ transition model, converged with first-order upwind, and second-order upwind for the momentum equations. Additionally, the optimization problem involves two objectives: maximizing the cruise efficiency and minimizing the total volume of the propeller. To enhance the performance of multi-fidelity, multi-objective (MFMO) acquisition functions, a novel algorithm is proposed. The proposed optimization methodology demonstrates its effectiveness in generating several high-performing propeller designs and proves to be more efficient than an equivalent problem formulation relying solely on Bayesian optimization. Post-processing of optimization data reveals new design variable bounds that correspond to more efficient blade shapes. These findings provide insights into some important geometric features of high-altitude propellers and establish guidelines for future optimization efforts within the specified thrust and power consumption ranges. Finally, simulations of the best-performing designs across a range of advance ratios and altitudes confirm their high performance in various operating conditions.

Ceramic additive manufacturing and microstructural analysis of tricalcium phosphate implants using X-ray microcomputed tomography

Additive manufacturing (AM) of ceramic bone implants from tricalcium phosphate (TCP) offers several benefits for bone regeneration and defect treatment. TCP scaffolds, e.g. featuring lattice or gyroid geometries, can effectively induce bone ingrowth and integration, showing a high potential in the treatment of large bone defects, e.g. as filler material for large bone defects. A major advantage of TCP is its osteoconductivity making it an effective choice for a broad range of orthopedic and dental applications. In addition, AM allows for the possibility to create precise, patient-specific implants with controllable mechanical properties. Those properties can be controlled by the implants' microstructure, e.g. in relation to bulk density and internal porosity. In this contribution, eleven resorbable bone implants were produced from β-tricalcium phosphate (β-TCP) in order to quantify the internal porosity in three dimensions using microcomputed tomography (μ CT). All components were manufactured using an extrusion-based process and scanned using an industrial μCT system at a voxel size of 10 μm. Two samples were physically prepared to allow a high-resolution μCT analysis at a voxel size of 1 μm. Results show that post-processed image data enables the non-destructive inspection of highly complex ceramic AM implants. Using μCT we were able to quantify internal porosity in β-TCP bone implant and quantify the geometry and distribution of wall thicknesses in the gyroid geometry. However, a detailed microstructural analysis is only possible using high-resolution μCT volume data, e.g. in relation to internal porosity. The findings emphasize that ceramic AM is able to produce complex components. However, NDT using μCT is crucial in the development of new materials and geometries. μCT provides high-resolution insights into the internal and external structure of ceramic AM components. It plays a critical role in detecting internal features, including small-scale porosity and delamination which are crucial for the integrity and functionality of medical implants. Moreover, μCT provides volumetric data that supports the design and manufacturing process at various stages, enabling an iterative approach of continuous improvement in mechanical performance and osseointegration.