Dataset Of Measurements Research Articles

Pore pressure-minimum horizontal stress coupling estimation in Tunu Field, Lower Kutai Basin, Indonesia

Quantifying the change of minimum horizontal stress (Shmin) in response to pore pressure is vital, particularly at the development stage of a hydrocarbon field. However, estimates of the coupling ratio between these parameters, either at basin or reservoir scales, are not well defined in Indonesia’s sedimentary basin, such as Lower Kutai Basin. This study investigates the coupling ratio between Shmin and pore pressure in one of the gas fields within the basin, Tunu Field, using available evidence from an extensive data set of direct pressure measurement, leak-off test, extended LOT, Mini-Frac, and lost circulation. Previous studies have reported overpressured and depleted reservoirs. Nevertheless, the evolution of Shmin due to the change in pore pressure has yet to be well established. This study reveals a Shmin and vertical stress ratio of 0.87 which may relate to the stiffening of the shales via clay diagenesis/cementation. Coupling ratios between Shmin and pore pressure are 0.3 for the overpressured zone at the basin scale and 0.42 for the depletion at the reservoir scale. The approach used has been shown to produce similar coupling ratio (or stress path) results to other basins globally, thus providing a useful estimate of the Shmin and pore pressure relationship for the Tunu Field, and potentially other fields in the Lower Kutai Basin. For the application to infill drillings within the field, this study indicates Shmin decreases in response to pore pressure depletion due to production, ranging from 1.7 to 2.3 ppg within the hydrostatic zone and from 2.5 to 3.7 within the overpressured zone, equivalent to a Pore Pressure (PP)-Shmin window decrease of approximately 28% to 37% and approximately 40% to 88%, respectively. In addition, strike-slip faulting is suggested as the likely in situ stress regime in the Tunu Field.

The Unsteady Shock-Boundary Layer Interaction in a Compressor Cascade – Part 3: Mechanisms of Shock Oscillation

Abstract The shock-boundary layer interaction in transonic flows is known to cause strong unsteady flow effects that negatively affect the performance and operability of blade and cascade designs. Despite decades of research on the subject, little is still known about the physical mechanisms that drive the different oscillation frequencies observed with different designs. In the conclusion of this three-part series, the experimental and numerical data obtained with the Transonic Cascade TEAMAero are analyzed together in detail in order to test the main theories of continuous shock oscillation. This analysis exposes a main mechanism of shock oscillation, where pressure waves generated inside the passage of the cascade propagate upstream and interact strongly with the main shock when the latter is also in the passage. The interaction of these features causes a breakdown of the flow that is shown to propagate upstream, inevitably causing strong variations in the inflow angle and therefore on the operating conditions of the cascade. The high frequency content of these pressure waves is also shown to be responsible for weaker high-frequency variations of the shock movement throughout the cycle. Parallels are also drawn with previous experimental campaigns in order to search for a global understanding of the different observations made. Although various parts of the described interaction are not fully understood yet, and the dataset of experimental measurements compiled is still rather small, a good basis is provided on which to further study the underlying mechanisms of unsteady flows in transonic cascades.

Implications of Water Quality Index and Multivariate Statistics for Improved Environmental Regulation in the Irtysh River Basin (Kazakhstan)

The selection of sites for permanent environmental monitoring of natural water bodies should rely on corresponding source apportionment studies. Tools like the water quality index (WQI) assessment may support this objective. This study aims to analyze a decade-long dataset of measurements of 26 chemical components at 26 observation points within the Irtysh River Basin, aiming to identify priority zones for stricter environmental regulations. It was achieved through the WQI tool integrated with geoinformation systems (GISs) and multivariate statistical techniques. The findings highlighted that both upstream sections of tributaries (Oba and Bukhtarma rivers) and the mainstream of the basin are generally in good condition, with slight fluctuations observed during flooding periods. Areas in the basin experiencing significant impacts from mining and domestic wastewater treatment activities were identified. The rivers Glubochanka (GL) and Krasnoyarka (KR) consistently experienced marginal water quality throughout the observation period. Various contaminant sources were found to influence water quality. The impact of domestic wastewater treatment facilities was represented by twofold elevated concentrations of chemical oxygen demand, reaching 22.6 and 27.1 mg/L for the KR and GL rivers, respectively. Natural factors were indicated by consistent slight exceedings of recommended calcium levels at the KR and GL rivers. These exceedances were most pronounced during the cold seasons, with an average value equal to 96 mg/L. Mining operations introduced extremal concentrations of trace elements like copper, reaching 0.046–0.051 mg/L, which is higher than the threshold by 12–13 times; zinc, which peaked at 1.57–2.96 mg/L, exceeding the set limit by almost 50–100 times; and cadmium, peaking at levels surpassing 1000 times the safe limit, reaching 0.8 mg/L. The adverse impact of mining activities was evident in the Tikhaya, Ulba, and Breksa rivers, showing similar trends in trace element concentrations. Seasonal effects were also investigated. Ice cover formation during cold seasons led to oxygen depletion and the exclusion of pollutants into the stream when ice melted, worsening water quality. Conversely, flooding events led to contaminant dilution, partially improving the WQI during flood seasons. Principal component analysis and hierarchical cluster analysis indicated that local natural processes, mining activities, and domestic wastewater discharge were the predominant influences on water quality within the study area. These findings can serve as a basis for enhanced environmental regulation in light of updated ecological legislation in Kazakhstan, advocating for the establishment of a comprehensive monitoring network and the reinforcement of requirements governing contaminating activities.

A Framework for Developing Tools to Predict PFAS Physical–Chemical Properties and Mass-Partitioning Parameters

A framework for developing predictive models for PFAS physical–chemical properties and mass-partitioning parameters is presented. The framework is based on the objective of developing tools that are of sufficient simplicity to be used rapidly and routinely for initial site investigations and risk assessments. This is accomplished by the use of bespoke PFAS-specific QSPR models. The development of these models entails aggregation and curation of measured data sets for a target property or parameter, supplemented by estimates produced with quantum–chemical ab initio predictions. The application of bespoke QSPR models for PFAS is illustrated with several examples, including partitioning to different interfaces, uptake by several fish species, and partitioning to four different biological materials. Reasonable correlations to molar volume were observed for all systems. One notable observation is that the slopes of all of the regression functions are similar. This suggests that the partitioning processes in all of these systems are to some degree mediated by the same mechanism, namely hydrophobic interaction. Special factors and elements requiring consideration in the development of predictive models are discussed, including differences in bulk-phase versus interface partitioning processes.

Feature extraction and pattern recognition in time-lapse pressure transient responses

Monitoring of modern wells equipped with permanent downhole gauges and flowmeters provides large datasets of pressure, temperature and flowrate measurements. On a limited scale, selected events from these datasets are traditionally used in well performance analysis and monitoring, reservoir simulation, and many other tasks employing physics-based models. New methods that combine model- and data-driven approaches for full-scale analysis of these large datasets have recently attracted interest, suggesting new metrics for knowledge extraction. Most recent studies have used a combination of Pressure Transient Analysis (PTA), which is a key reservoir engineering tool, with hybrid methods, such as PTA-metrics and AI-powered models. The emergence of hybrid methodologies combining model- and data-driven approaches has opened new perspectives for comprehensive analysis and knowledge extraction from big well monitoring data sets, accumulated in the industry. Despite their potential, these methodologies often face challenges in reliability, effective data processing, interpretation and feature engineering.This paper introduces a novel PTA-feature extraction and pattern recognition methods for analysis of time-lapse pressure transient responses and their Bourdet derivatives. The pattern recognition method builds on an unsupervised classification method to extract PTA-features, associated with different flow regimes commonly used in PTA. Each pressure transient in a time-lapse series is first processed by an autonomously fine-tuned algorithm that measures the signal's distance to an ensemble of physically meaningful responses defined by PTA-feature library, thus breaking the transient into a series of likely PTA-features. A set of hyperparameters is used in the unsupervised classification, where an optimization procedure is employed for automated tuning of the hyperparameters to a particular transient. Subsequently, recognition of underlying patterns governed by sequences of these PTA-features in the time-lapse transient responses is performed. Testing of the combination of these new methods through synthetic and field cases is further carried out with verification via comparison with expert’ interpretation results. Added value to the previously introduced PTA metrics, which provide on-the-fly well and reservoir performance analysis, is finally demonstrated. The proposed pattern recognition method improves reliability and automates the calculation of the PTA metrics.The developed methodology serves as a new tool for knowledge extraction from big well monitoring datasets, available in the companies operating in the oil and gas industry, as well as the emerging industries such as carbon capture and storage and geothermal energy production. The article concludes with a discussion of the main advantages and limitations of the suggested feature extraction and pattern recognition methods. Besides the combined use of the methods described in this article, these methods may also be integrated with conventional physics-based approaches widely used in the industry for well data interpretation and reservoir simulations, improving their performance and efficiency for big data sets.

Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation

Modeling streamflow is essential for understanding flow inundation. Traditionally, this involves hydrologic and numerical models. This research introduces a framework using agent-based modeling (ABM) combined with data-driven modeling (DDM) and Artificial Intelligence (AI). An agent-driven model simulates streamflow and its interactions with river courses and surroundings, considering hydrologic phenomena related to precipitation, water level, and discharge as well as channel and basin characteristics causing increased water levels in the Medio River. A five-year dataset of hourly precipitation, water level, and discharge measurements was used to simulate streamflow. The model’s accuracy was evaluated using statistical metrics like correlation coefficient (r), coefficient of determination (R2), root mean squared error (RMSE), and percentage error in peak discharge (Qpk). The ABM’s simulated peak discharge (Qpk) was compared with the measured peak discharge across four experimental scenarios. The best simulations occurred in scenario 3, using only rainfall and streamflow data. Data management and visualization facilitated input, output, and analysis. This study’s ABM combined with DDM and AI offers a novel approach for simulating streamflow and predicting floods. Future studies could extend this framework to other river basins and incorporate advanced sensor data to enhance the accuracy and responsiveness of flood forecasting.

Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

Identification of an Unknown Stationary Emission Source in Urban Geometry Using Bayesian Inference

Estimating the parameters of an unidentified toxic pollutant source is crucial for public safety, especially in densely populated urban areas. Implementing source term estimation methods in real-world urban environments is challenging due to complex phenomena and the absence of concentration observational data. This work combines a computational fluid dynamics numerical simulation with the Metropolis–Hastings MCMC algorithm to identify the location and quantify the release rate of an unknown source within the geometry of Augsburg city center. To address the lack of concentration measurements, synthetic observations are generated by a forward dispersion model. The methodology is tested using these datasets, both as directly calculated by the forward model and with added Gaussian noise under different source release and wind flow scenarios. The results indicate that in most cases, both the source location and the release rate are estimated accurately. Although a higher performance is achieved using synthetic datasets without additional noise, high accuracy predictions are also obtained in many applications of noisy measurement datasets. In general, the outcomes demonstrate that the presented methodology can be a useful tool for estimating unknown source parameters in real-world urban applications.

Using Expert Participation to Evaluate the Accuracy of Hand-Drawn Water-Table Maps.

Water-table maps are fundamental to hydrogeological studies and a manual, hand-drawn method is still commonly used to produce them. Despite this, the accuracy and variability of such maps have received little attention in international literature. In a unique experiment, 63 groundwater professionals drew water-table equipotential contours based on the same dataset of point measurements and were asked to infer flow directions and predict groundwater elevations at predefined locations. The root mean squared error (RMSE) for the average map calibration data was 10.5 m, which is accuracy comparable to numerical groundwater models. This study confirmed that to produce hand-drawn water-table maps, practitioners seek to not only fit the spatial data, but also to conform to their own cognitive model of hydrogeological concepts and processes. The calibration accuracy increased with experience; from a RMSE of 13.3 m for practitioners with 0-3 years of experience to a RMSE of 9.2 m for those with four or more years. Despite considerable variability in the style of the hand-drawn water-table maps, the maps were consistent in their representation of the dominant regional groundwater flow directions. There was less consensus, however, in predicting the direction of surface water-groundwater interaction for a stream reach. Hand-drawn water-table mapping remains useful and valid, especially as a starting point for hydrogeological conceptualization, yet further work is required to resolve issues around transparency, repeatability, and reproducibility.

A framework for studying environmental statistics in developmental science.

Psychologists tend to rely on verbal descriptions of the environment over time, using terms like "unpredictable," "variable," and "unstable." These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that organizes descriptions of the environment over time in clear terms: as statistical definitions. Here, we first present such a framework, drawing on theory developed in other disciplines, such as biology, anthropology, ecology, and economics. Then we apply our framework by quantifying "unpredictability" in a publicly available, longitudinal data set of crime rates in New York City (NYC) across 15 years. This case study shows that the correlations between different "unpredictability statistics" across regions are only moderate. This means that regions within NYC rank differently on unpredictability depending on which definition is used and at which spatial scale the statistics are computed. Additionally, we explore associations between unpredictability statistics and measures of unemployment, poverty, and educational attainment derived from publicly available NYC survey data. In our case study, these measures are associated with mean levels in crime rates but hardly with unpredictability in crime rates. Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. To facilitate the use of our framework, we provide a friendly, step-by-step guide for identifying the structure of the environment in repeated measures data sets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Eye morphology contributes to the ecology and evolution of the aquatic avifauna.

Aquatic birds are notable among the global avifauna for living in environments exposed to large amounts of light. Despite growing evidence that visual adaptations to light underly the ecology and evolution of the avian tree of life, no comprehensive comparative analysis of visual acuity as approximated by eyes size exists for the global aquatic avifauna. Here, I use Stanley Ritland's unpublished dataset of measurements for axial length collected from museum specimens to explore the ecology and evolution of eye size variation for half of the aquatic avifauna (N = 464 species). After correcting for body mass allometry and incorporating phylogenetic relationships, aquatic species had significantly smaller eyes compared to terrestrial species. Furthermore, species using hyperopic foraging manoeuvres, exhibiting carnivorous and insectivorous diets, and displaying nocturnal behaviour had larger eyes. Plunge-divers (e.g. boobies and tropic birds) and stalkers (e.g. herons) had the largest relative eye sizes, especially species identifying prey at higher altitudes or longer distances. Underwater pursuit-divers foraging at greater depths had larger eyes, likely due to the dramatic attenuation of light in the deep ocean. Overall, residual eye size was phylogenetically conserved (l = 0.94), with phylogeny alone explaining 62% of residual eye size variation. Collectively, these results suggest that the relatively bright environments found in aquatic ecosystems negate the adaptive benefits of costly metabolic investments associated with developing and maintaining larger eyes, while also reducing the potential occurrence of disability glare. Strong correlations between eye size and foraging ecology in different aquatic environments corroborate similar comparative studies of terrestrial birds and underscore the central role that vision has played in driving the ecology and evolution of the global avifauna.

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds

The Additive Manufacturing Benchmark Test Series (AM Bench) is a broad effort to produce rigorous measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many additive manufacturing (AM) build methods and material classes. Here, the microstructures of nickel alloy 718 AM Bench 2022 test artifacts produced using laser-based powder bed fusion (PBF-LB), in both as-built and fully heat-treated conditions, are examined. Cross sections are primarily characterized using large area scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and example analyses of the crystallographic textures are described. These data are part of a large set of in situ and ex situ measurements from both three-dimensional builds and laser tracks on bare plates. All the measurement data are available online with download links at www.nist.gov/ambench.

Assessing wayside traffic noise levels using long-term noise monitoring and available Department of Transportation traffic data - worst-case noise traffic conditions, effects of repaving, and methods for comparing traffic noise data sets

The availability of automated continuous traffic data from State Departments of Transportation (DOTs), such as the California Department of Transportation (Caltrans) Performance Measurement System (PeMS), provides increased opportunities for the analysis of highway noise. Traffic data that is free to access and downloadable via the internet can be matched with unattended long-term noise measurement data to provide large datasets for analysis, without requiring field staff to be continuously on site. A 2018 ICF/Caltrans paper documented how this approach was used on an operating freeway to determine the volume of traffic that results in the highest one-hour energy average traffic noise level. At the time of those measurements, the subject freeway had Portland cement concrete pavement. The freeway has since been repaved with an open-graded rubberized asphalt concrete pavement overlay. The current study uses new (2023) traffic noise measurements and PeMS data to investigate how worst-noise conditions have changed and the effect of the repaving on wayside noise levels. Changes in traffic patterns since 2018 required the authors to normalize the measurement data for comparison. Opportunities were identified to take advantage of the large measurement data sets to match before-and-after data, and those methods are also described in this paper.

Does migration constrain glucocorticoid phenotypes? Testing corticosterone levels during breeding in migratory versus resident birds.

Corticosterone, the main glucocorticoid in birds, is a major mediator of the incredible physiological feat of migration. Corticosterone plays important roles in migration, from preparation to in-flight energy mobilization to refueling, and corticosterone levels often show distinct elevations or depressions during certain stages of the migratory process. Here, we ask whether corticosterone's role in migration shapes its modulation during other life history stages, as is the case with some other phenotypically flexible traits involved in migration. Specifically, we use a global dataset of corticosterone measures to test whether birds' migratory status (migrant versus resident) predicts corticosterone levels during breeding. Our results indicate that migratory status predicts neither baseline nor stress-induced corticosterone levels in breeding birds; despite corticosterone's role in migration, we find no evidence that migratory corticosterone phenotypes carry over to breeding. We encourage future studies to continue to explore corticosterone in migrants versus residents across the annual cycle. Additionally, future efforts should aim to disentangle the possible effects of environmental conditions and migratory status on corticosterone phenotypes; potentially fruitful avenues include focusing on regions where migrants and residents overlap during breeding. Overall, insights from work in this area could demonstrate whether migration shapes traits during other important life stages, identify tradeoffs or limitations associated with the migratory lifestyle, and ultimately shed light on the evolution of flexible traits and migration.

Six years of high-resolution climatic data collected along an elevation gradient in the Italian Alps

The complex meso- and microclimatic heterogeneity inherent to mountainous regions, driven by both topographic and biotic factors, and the lack of observations, poses significant challenges to using climate models to predict and understand impacts at various scales. We present here a six-year dataset (2017–2022) of continuous climatic measurements collected at five elevations from 983 m to 2705 m above sea level in the Val Mazia - Matschertal valley in the Italian Alps. The measurements include the air temperature, relative humidity, wind speed and direction, solar radiation, soil properties, precipitation, and snow height. Collected within the European Long-Term Ecological Research program (LTER), this dataset is freely available in an open access repository. The time series may be valuable for the validation of regional climate models, atmospheric exchange modelling, and providing support for hydrological models and remote sensing products in mountain environments. Additionally, our data may be useful for research on the influence of elevation on ecological processes such as vegetation growth, plant composition, and soil biology. Beyond its utility in advancing such fundamental research, meteorological monitoring data contribute to informed socio-political decisions on climate adaptation strategies, land management, and water resource planning, enhancing the safety and resilience of mountain communities and biodiversity.

Nanofluidic Detection Platform for Simultaneous Light Absorption and Scattering Measurement of Individual Nanoparticles in Flow.

Characterization and quantification of plasmonic nanoparticles at the single particle level have become increasingly important with the advancements in nanotechnology and their application to various biological analyses including diagnostics, photothermal therapy, and immunoassays. While various nanoparticle detection methodologies have been developed and widely used, simultaneous measurement of light absorption and scattering from individual plasmonic nanoparticles in flow is still challenging. Herein, we describe a novel nanofluidic detection platform that enables simultaneous measurement of absorption and scattering signals from individual nanoparticles within a nanochannel. Our detection platform utilized optical diffraction phenomena by a single nanochannel as both a readout signal for photothermal detection and a reference light for interferometric scattering detection. Through the elucidation of the frequency effect on the detection performance and optimization of experimental conditions, we achieved the classification of gold and silver nanoparticles with a diameter of 20-60 nm at an average accuracy score of 82.6 ± 2.1% by measured data sets of absorption and scattering signals. Furthermore, we demonstrated the concentration determination of plasmonic nanoparticle mixtures using a trained Support vector machine (SVM) classifier. Our simple yet sensitive nanofluidic detection platform will be a valuable tool for the analysis of nanoparticles and their applications to chemical and biological assays.

Robust Structural Health Monitoring of CFRP Structures under Varying Loads using NSFD and Machine Learning with Combined Usage of Acceleration and Strain Data

Structural health monitoring (SHM) of objects in situ often requires a suitable combination of different methods and measurement techniques. In the field of aerospace applications, the previous research project "Combined acoustic and modal structure monitoring" dealt with the development of a robust SHM system for damage detection in carbon-fibre-reinforced polymer (CFRP) structures under varying realistic loads. The methods combined within the project were based on guided waves, acoustic emission, different vibration monitoring techniques and strain measurement. The present paper deals with the application of the nullspace-based fault detection algorithm (NSFD) combined with a machine learning approach based on strain measurements to classify different load conditions of the structure. Due to the high sensitivity to changes in the statistical properties of the measurement data, the NSFD algorithm reacts very sensitively to structural changes, but also to changes in the environmental conditions, such as changes in the external loads and the resulting stress state of the structure. In the paper, the impact of varying external loads on the NSFD damage indicator is analysed and shown. To prevent the NSFD algorithm from causing false alarms due to changes in the load conditions, but still being sensitive to small structural changes due to impact damages, the strain sensor data is used to predict the external loads on the structure. For this, machine learning is used to create clusters of similar stress states. For every cluster, a baseline for the NSFD algorithm must be made. The resulting approach is robust against changes in external loads and sensitive to small changes in the structure due to damages at the same time. The theory and the algorithms are successfully tested with measured data sets from a CFRP-airplane structure.

Cross-method Analysis of Corotation Radii Data Set for Spiral Galaxies

A corotation radius is a key characteristic of disk galaxies that is essential to determine the angular speed of the spiral structure Ω p , and therefore understand its nature. In the literature, there are plenty of methods to estimate this value, but do these measurements have any consistency? In this work, we collected a data set of corotation radius measurements for 547 galaxies, 300 of which had at least two values. An initial analysis reveals that most objects have rather inconsistent corotation radius positions. Moreover, a significant fraction of galactic disks is distinguished by a large error coverage and almost uniform distribution of measurements. These findings do not have any relation to spiral type, Hubble classification, or presence of a bar. Among other reasons, obtained results could be explained by the transient nature of spirals in a considerable part of galaxies. We have made our collected data sample publicly available, and have demonstrated on one example how it could be useful for future research by investigating a winding time value for a sample of galaxies with possible multiple spiral arm patterns.

Graph-based Data Mining, Pattern Recognition and Anomaly Detection for Intelligent Energy Networks

Intelligent Energy Networks play an important role in the contemporary landscape of energy production and distribution, and the large amount of data produced by their smart sensors recently caused a great deal of interest in Data Analysis and Data Mining strategies to support network operators in optimizing operations, reliability and profitability. This work proposes for the network operator an unified data analytics framework, composed by clustering, anomaly detection and knowledge propagation algorithms, and based on a graph representation of the measurements dataset. The usage of the graph is particularly suited with multivariate and high-dimensional time series characterizing large and complex energy networks, it allows to express additional attributes on the data points and hence incorporate previous expert’s knowledge, and the leveraging of the graph structure and connectivity naturally captures dependencies among data for discovering energy patterns and anomalies in the original energy network. The effectiveness of the presented graph-based framework is validated on a number of real-world cases and private operators of District Networks.

FHIR Implementation Guide for Stroke: A dual focus on the patient’s clinical pathway and value-based healthcare

BackgroundStroke management requires a coordinated strategy, adhering to clinical pathways (CP) and value-based healthcare (VBHC) principles from onset to rehabilitation. However, the discrepancies between these pathways and actual patient experiences highlight the need for ongoing monitoring and addressing interoperability issues across multiple institutions in stroke care. To address this, the Fast Healthcare Interoperability Resource (FHIR) Implementation Guide (IG) standardizes the information exchange among these systems, considering a specific context of use. ObjectiveDevelop an FHIR IG for stroke care rooted in established stroke CP and VBHC principles. MethodWe represented the stroke patient journey by considering the core stroke CP, the International Consortium for Health Outcomes Measurement (ICHOM) dataset for stroke, and a Brazilian case study using the Business Process Model and Notation (BPMN). Next, we developed a data dictionary that aligns variables with existing FHIR resources and adapts profiling from the Brazilian National Health Data Network (BNHDN). ResultsOur BPMN model encompassed three critical phases that represent the entire patient journey from symptom onset to rehabilitation. The stroke data dictionary included 81 variables, which were expressed as questionnaires, profiles, and extensions. The FHIR IG comprised nine pages: Home, Stroke-CP, Data Dictionary, FHIR, ICHOM, Artifacts, Examples, Downloads, and Security. We developed 96 artifacts, including 7 questionnaires, 27 profiles with corresponding example instances, 3 extensions, 18 value sets, and 14 code systems pertinent to ICHOM outcome measures. ConclusionThe FHIR IG for stroke in this study represents a significant advancement in healthcare interoperability, streamlining the tracking of patient outcomes for quality enhancement, facilitating informed treatment choices, and enabling the development of dashboards to promote collaborative excellence in patient care.