Dataset Of Measurements Research Articles

Polarization of active galactic nuclei with significant VLBI-Gaia displacements

Context. Numerous studies have reported significant displacements in the coordinates of active galactic nuclei (AGNs) between measurements using the very-long-baseline-interferometry (VLBI) technique and those obtained by the Gaia space observatory. There is consensus that these discrepancies do indeed manifest astrometrically resolved sub-components of AGNs rather than random measurement noise. Among other evidence, it has been reported that AGNs with VLBI-to-Gaia displacements (VGDs) pointing downstream of their parsec-scale radio jets exhibit higher optical polarization compared to sources with the opposite (upstream) VGD orientation. Aims. We aim to verify the previously reported connection between optical polarization and a VGD-jet angle using a larger dataset of polarimetric measurements and updated Gaia DR3 positions. We also seek further evidence supporting the disk-jet dichotomy as an explanation of such a connection by using millimeter-wave polarization and multiband optical polarization measurements. Methods. We performed optical polarimetric observations of 152 AGNs using three telescopes. These data are complemented by other publicly available polarimetric measurements of AGNs. We cross-matched public astrometric data from VLBI and Gaia experiments, obtained corresponding positional displacements, and combined this catalog with the polarimetric and jet direction data. Results. Active galactic nuclei with downstream VGDs are confirmed to have significantly higher optical fractional polarization than the upstream sample. At the same time, the millimeter-wavelength polarization of the two samples shows very similar distributions. Conclusions. Our results support the hypothesis that the VGDs pointing down the radio jet are likely caused by a component in the jet emitting highly polarized synchrotron radiation and dominating in the overall optical emission. The upstream-oriented VGDs are likely to be produced by the low-polarization emission of the central engine’s subcomponents, which dominate in the optical.

Consistency evaluation of tropospheric ozone from ozonesonde and IAGOS (In-service Aircraft for a Global Observing System) observations: vertical distribution, ozonesonde types, and station–airport distance

Abstract. The vertical distribution of tropospheric O3 from ozonesondes is compared with that from In-service Aircraft for a Global Observing System (IAGOS) measurements collected at 23 pairs of sites between about 30° S and 55° N from 1995 to 2021. Profiles of tropospheric O3 from IAGOS are generally in good agreement with ozonesonde observations from electrochemical concentration cells (ECCs), Brewer–Mast sondes, and carbon–iodine sensors, with average biases of 2.58, −0.28, and 0.67 ppb and correlation coefficients (R) of 0.72, 0.82, and 0.66, respectively. Agreement between aircraft and Indian-sonde observations is poor, with an average bias of 15.32 ppb and an R value of 0.44. The O3 concentration observed by ECC sondes is, on average, 5 %–10 % higher than that observed by IAGOS, and the relative bias increases modestly with altitude. For other sonde types, there are some seasonal and altitudinal variations in the relative bias with respect to the IAGOS measurements, but these appear to be caused by local differences. The distance between the station and airport, when within 4° (latitude and longitude), has little effect on the comparison results. For the ECC ozonesondes, the overall bias with respect to the IAGOS measurements varies from 5.7 to 9.8 ppb when the station pairs are grouped by station–airport distances of <1° (latitude and longitude), 1–2°, and 2–4°. Correlations for these groups correspond to R=0.8, 0.9, and 0.7. These comparison results provide important information for merging ozonesonde and IAGOS measurement datasets. They can also be used to evaluate the relative biases of different sonde types in the troposphere, using the aircraft as a transfer standard.

Aluminum dust concentration detection based on LSTM-Kalman filter

Industrial dust emissions present serious hazards, including respiratory issues and explosion risks. Traditional dust concentration detection methods are often compromised by environmental factors. This study introduces a novel FFT-KF-LSTM-NET model to predict high-concentration aluminum powder levels, utilizing 128 sets of electrostatic induction measurement data. By applying the Fast Fourier Transform (FFT) to eliminate low-frequency interference, integrating Long Short-Term Memory (LSTM) networks for advanced time series analysis, and using the Kalman Filter (KF) for rapid model convergence, this approach significantly enhances prediction accuracy. The model achieves an 82% improvement in Mean Squared Error (MSE), reducing it to 0.1336, outperforming traditional methods. Furthermore, by modeling the voltage signal generated by charged dust particles in the electrostatic induction sensor's sensing area and using the first derivative of the voltage signal as a learning feature, the model's prediction speed is increased from 1.5 s-2 s–0.5 s, with improved anti-interference capabilities. These advancements position the FFT-KF-LSTM-NET model as a crucial tool for real-time, reliable dust concentration detection in industrial environments.

Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks

Monitoring railway tracks through drive-by vibration data collected by in-service trains offers a cost-effective and adaptable solution for inspecting multiple railway lines. However, numerous existing drive-by monitoring methods rely on supervised learning models, necessitating extensive labelled data for each line. In this paper, a novel framework is proposed based on Unsupervised Domain Adaptation (UDA) concept which facilitates the transfer of a geometric defects diagnosis model learned from one line to a new line without the need for any labelled data from the new line. The proposed framework learns the dynamic-based features that are sensitive to damage and also invariant to different railway tracks. It comprises three components: data pre-processing, UDA implementation, and damage diagnosis. The framework uses the data from the source domain, including corresponding labels, as well as the unlabelled data from the target domain as input. The outputs of the framework consist of the predicted labels for the target domain. The performance of the proposed framework is evaluated using a comprehensive dataset of field measurements of a high-speed train passing 4 different lines within the French high-speed rail network. The proposed UDA framework is implemented using four common UDA algorithms including Information-Theoretical Learning (ITL), Geodesic Flow Kernel (GFK), Transfer Component Analysis (TCA), and Subspace Alignment (SA). The results show that the proposed framework has a 14% increase in the anomaly detection accuracy compared to traditional unsupervised learning methods in which UDA is not used. Furthermore, this study investigates the impact of incorporating a percentage of target data labels during training (semi-supervised domain adaptation), along with various sensor layouts and different tuning parameters, on the accuracy of the proposed approach. The results show that the proposed framework can significantly facilitate the monitoring of railway track conditions using the data collected by in-service trains which could be great interest of railway owners.

Segmentation of multi-view video speed extraction method that can cross lane dividing lines

Aiming at the problem that the existing video traffic parameter extraction methods are overly dependent on manual annotation and a single perspective cannot effectively correct the dynamic driving deviation of vehicles on site, a multi-perspective video traffic parameter extraction method that can segment and cross lane dividing lines is proposed. The method consists of an automatic annotation point generation module and a multi-perspective correction module. The automatic annotation point generation module realizes the process of automatically generating annotation points by constructing a reference block based on equal-length lane dividing lines. The multi-perspective correction module proposes multiple mapping methods between vehicles and lane dividing lines and a correction speed measurement method based on the average speed probability density function to correct the two types of deviations generated by vehicles during dynamic driving. Experimental results on public data sets and measured data sets show that the vehicle speed extraction accuracy of the proposed method is better than other speed measurement methods, and it has a certain degree of universality.

Image Stitching of Low-Resolution Retinography Using Fundus Blur Filter and Homography Convolutional Neural Network

Great advances in stitching high-quality retinal images have been made in recent years. On the other hand, very few studies have been carried out on low-resolution retinal imaging. This work investigates the challenges of low-resolution retinal images obtained by the D-EYE smartphone-based fundus camera. The proposed method uses homography estimation to register and stitch low-quality retinal images into a cohesive mosaic. First, a Siamese neural network extracts features from a pair of images, after which the correlation of their feature maps is computed. This correlation map is fed through four independent CNNs to estimate the homography parameters, each specializing in different corner coordinates. Our model was trained on a synthetic dataset generated from the Microsoft Common Objects in Context (MSCOCO) dataset; this work added an important data augmentation phase to improve the quality of the model. Then, the same is evaluated on the FIRE retina and D-EYE datasets for performance measurement using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The obtained results are promising: the average PSNR was 26.14 dB, with an SSIM of 0.96 on the D-EYE dataset. Compared to the method that uses a single neural network for homography calculations, our approach improves the PSNR by 7.96 dB and achieves a 7.86% higher SSIM score.

GHOST: a globally harmonised dataset of surface atmospheric composition measurements

Abstract. GHOST (Globally Harmonised Observations in Space and Time) represents one of the biggest collections of harmonised measurements of atmospheric composition at the surface. In total, 7 275 148 646 measurements from 1970 to 2023, of 227 different components from 38 reporting networks, are compiled, parsed, and standardised. The components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of the information provided by major public reporting networks, which is saved in 21 data variables and 163 metadata variables. Extensive effort in particular is made towards the standardisation of measurement process information and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use) and temporal classifications per measurement (e.g. day or night). A range of standardised network quality assurance flags is associated with each individual measurement. GHOST's own quality assurance is also performed and associated with measurements. Measurements pre-filtered by the default GHOST quality assurance are also provided. In this paper, we outline all steps undertaken to create the GHOST dataset and give insights and recommendations for data providers based on the experiences gleaned through our efforts. The GHOST dataset is made freely available via the following repository: https://doi.org/10.5281/zenodo.10637449 (Bowdalo, 2024a).

Bivariate Log-Symmetric Regression Models Applied to Newborn Data

This paper introduces bivariate log-symmetric models for analyzing the relationship between two variables, assuming a family of log-symmetric distributions. These models offer greater flexibility than the bivariate lognormal distribution, allowing for better representation of diverse distribution shapes and behaviors in the data. The log-symmetric distribution family is widely used in various scientific fields and includes distributions such as log-normal, log-Student-t, and log-Laplace, among others, providing several options for modeling different data types. However, there are few approaches to jointly model continuous positive and explanatory variables in regression analysis. Therefore, we propose a class of generalized linear model (GLM) regression models based on bivariate log-symmetric distributions, aiming to fill this gap. Furthermore, in the proposed model, covariates are used to describe its dispersion and correlation parameters. This study uses a dataset of anthropometric measurements of newborns to correlate them with various biological factors, proposing bivariate regression models to account for the relationships observed in the data. Such models are crucial for preventing and controlling public health issues.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57µg/m3 and PM10 at 39.58µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty. Introduction In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions. A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics. In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set. Data Acquisition and Generation Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development. An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells. Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level. These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.

Machine learning-based layer-wise detection of overheating anomaly in LPBF using photodiode data

Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies which can lead to various defects in the part including geometric distortion, and poor surface roughness, among others, using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics and thermal history. Hence, the proposed method offers a machine learning (ML) framework to utilize photodiode sensor data for layer-wise detection of overheating anomalies. In doing so, three sets of features are extracted from the raw photodiode data: MSMM (mean, standard deviation, median, maximum), MSQ (mean, standard deviation, quartiles), and MSD (mean, standard deviation, deciles). These three datasets are used to train several ML classifiers. Cost-sensitive learning is used to handle the class imbalance between the “anomalous” layers (affected by overheating) and “nominal” layers in the benchmark dataset. To boost detection accuracy, our proposed ML framework involves utilizing the majority voting ensemble (MVE) approach. First, the top three ML classifiers are identified from an initial pool of classifiers, based on their performance in k-fold cross-validation. Next, final predictions are generated using majority voting from the individual predictions of the top three classifiers. We performed 100 iterations to generate statistically reliable results. This proposed method is demonstrated using a case study including an open benchmark dataset of photodiode measurements from an LPBF specimen with deliberate overheating anomalies at some layers. The results from the case study demonstrate that the MSD features yield the best performance for all classifiers, and the MVE classifier (with a mean F1-score of 0.8654) surpasses the individual ML classifiers. Moreover, our machine learning methodology achieves superior results (9.66% improvement in mean F1-score) in detecting layer-wise overheating anomalies, surpassing the existing methods in the literature that use the same benchmark dataset. Finally, based on our results, we provide useful insights and recommendations for future research on applying machine learning techniques for defect detection in AM.

Dataset of dendrometer and environmental parameter measurements of two different species of the group of genera known as eucalypts in South Africa and Portugal.

Dataset of dendrometer and environmental parameter measurements of two different species of the group of genera known as eucalypts in South Africa and Portugal.

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.

Open-set Communication Scene Recognition Based on Metric Learning

Abstract In this paper, we study the problem of recognizing communication scenes under open set protocols, i.e., making the extracted features of communication scenes sufficiently separable under a suitable metric space. To achieve it, we design a feature fusion model based on a convolutional neural network to extract the features of communication scenes, and we take the real and imaginary parts of the channel frequency response as model inputs to extract the scene features sufficiently. Furthermore, in order to optimize the distribution of scene features in angular space, a Linear-Softmax loss function is designed to restrict the inter-class angles of extracted features to be larger than the intra-class angles by two hyperparameters. Meanwhile, a cosine distance-based communication scene recognition algorithm is designed to complete the communication scene recognition by calculating the cosine distance between features. Finally, it is tested on actual measurement data sets, and the experimental results show that compared with the state-of-the-art loss functions, the proposed Linear-Softmax loss function enables the learning of the maximum angular margin. The Linear-Softmax loss function combined with the proposed communication scenes recognition algorithm can complete the recognition of both closed-set and open-set test data, with a recognition accuracy as high as 97.28% on the closed-set test set and 85.65% on the open-set test set.

The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning

AbstractHydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US.

Changes in cerebrospinal fluid proteins across the spectrum of untreated and treated chronic HIV-1 infection.

Using the Olink Explore 1536 platform, we measured 1,463 unique proteins in 303 cerebrospinal fluid (CSF) specimens from four clinical centers contributed by uninfected controls and 12 groups of people living with HIV-1 infection representing the spectrum of progressive untreated and treated chronic infection. We present three initial analyses of these measurements: an overview of the CSF protein features of the sample; correlations of the CSF proteins with CSF HIV-1 RNA and neurofilament light chain protein (NfL) concentrations; and comparison of CSF proteins in HIV-associated dementia (HAD) and neurosymptomatic CSF escape (NSE). These reveal a complex but coherent picture of CSF protein changes with highest concentrations of many proteins during CNS injury in the HAD and NSE groups and variable protein changes across the course of systemic HIV-1 progression that included two common patterns, designated as lymphoid and myeloid patterns, related to principal involvement of their underlying inflammatory cell lineages. Antiretroviral therapy reduced CSF protein perturbations, though not always to control levels. The dataset of these CSF protein measurements, along with background clinical information, is posted online. Extended studies of this unique dataset will supplement this report to provide more detailed characterization of the dynamic impact of HIV-1 infection on the CSF proteome across the spectrum of HIV-1 infection, advancing the mechanistic understanding of HIV-1-related CNS pathobiology.

Prepackaged foods healthiness ranking index: developing front-of-pack labeling to facilitate the assessment and validation of healthiness rankings of prepackaged foods in China

BackgroundThe Medium and Long-Term Plan for the Prevention and Treatment of Chronic Diseases (2017–2025) in China has highlighted the importance of reducing fat, sodium, and sugar in foods. However, front-of-pack labeling, which enables consumers to assess the health levels of prepackaged foods, is lacking in China. In response to the Zhejiang Provincial Health Commission’s request, we sought to develop a method for efficiently evaluating the health level of prepackaged foods.MethodsThrough a comprehensive literature review, we established a systematic framework: the Prepackaged Foods Healthiness Ranking Index (PHRI). We determined specific threshold values and grading criteria and conducted two rounds of Delphi survey to refine the index. Using a measurement dataset, we aimed to determine the optimal limit values for assessing the health level of prepackaged foods in real-world settings.ResultsAfter two rounds of Delphi surveys, the PHRI underwent multiple revisions until consensus among experts was reached. The final decision regarding the upper limit values set 30% Nutrient Reference Values as the limit for solid foods and 15% Nutrient Reference Values for liquid foods. The calculation of PHRI values was successfully implemented with the dataset, in accord with current nutrition awareness and real-world conditions.ConclusionsThe PHRI provides a valuable tool for assessing the healthiness of prepackaged foods. This front-of-pack labeling system provides a convenient method for evaluating the nutritional quality of prepackaged foods. Ultimately, the PHRI has the potential to contribute to advancements in health policy, practice, and education in China.

Splitting intensity tomography to image depth-dependent seismic anisotropy patterns beneath the Italian Peninsula and surrounding regions

The region between central Europe and the centre of the Mediterranean is characterised by complex tectonics and kinematics. Here, the interaction between thickened crust, subducting lithosphere and surrounding asthenosphere produces strong and pervasive anisotropy in the upper mantle. Shear wave splitting measurements, the most adopted method to image seismic anisotropy so far, when interpreted in a ray-based framework result in little or no depth resolution, hampering a correct image of the anisotropy distribution with depth. In this study, we aim to better constrain the depth-dependent seismic anisotropy beneath Italy and surrounding regions, by isolating for the first time the source region of anisotropy at different depths. To do that, we perform an anisotropy tomography, adopting the splitting intensity inversion method. It is entirely based on the finite-frequency effect in the splitting of SKS waves. We first computed the splitting intensity using SKS waves recorded at all available permanent and temporary stations over the region, obtaining a huge dataset of measurements used as an input for the tomographic inversion. The large-scale 3D model of seismic anisotropy obtained with the inversion shows a clear change of anisotropy properties in terms of fast polarisation direction and intensity for different depths, thus improving the characterization of the main sources of anisotropy in the mantle as a function of depth. Shallower layers (70–100 km depth) are characterised by a complex and variable oriented pattern of anisotropy fast direction and intensity, which becomes progressively more organised with depth (100–300 km). This pattern suggests a strong control exerted by the geometry and motion of the different slab segments and the large-scale asthenospheric flow generated by subduction and roll-back processes. The strength of anisotropy increases with depth, with high values affecting the bulge of the Alps and Apennines chains and the southern Tyrrhenian subduction system. On the contrary, weaker anisotropy characterises the transition zone from the Apennines to Alpine domains beneath the Po plain, and both the Adriatic and European domains.

Quantifying gender differences in orbital morphology with large MRI datasets

PurposeTo investigate gender differences in orbital morphology using large MRI datasets. MethodsUsing a deep learning-based approach, the orbit and eyeball were automatically segmented from high resolution 3D MRI images of the IXI and OASIS3 datasets. Orbital and eyeball morphological parameters, including orbital volume, eyeball volume, effective orbital volume (EOV, defined as the orbital cavity volume excluding the eyeball), and coronal orbital dimensions and shape, were quantitatively assessed. The volume index was defined as the ratio of orbital volume to eyeball volume. ResultsThis study included 1926 subjects with a mean age of 63.9 ​± ​15.3 years. The mean volumes of the eyeball and orbit were 7.1 ​± ​1.0 ​ml and 25.9 ​± ​3.5 ​ml, respectively. Significant gender differences (all P ​< ​0.001) were observed in the following parameters (males versus females): orbital volume (28.3 ​± ​3.0 ​ml versus 24.0 ​± ​2.7 ​ml), EOV (25.1 ​± ​3.0 ​ml versus 21.1 ​± ​2.6 ​ml), eyeball volume (7.3 ​± ​1.0 ​ml versus 6.9 ​± ​1.0 ​ml), volume index (3.9 ​± ​0.6 versus 3.5 ​± ​0.5), orbital depth (40.0 ​± ​3.1 ​mm versus 37.4 ​± ​2.9 ​mm), coronal orbital height (40.8 ​± ​3.0 ​mm versus 38.4 ​± ​2.4 ​mm), coronal orbital width (38.0 ​± ​1.9 ​mm versus 36.6 ​± ​1.7 ​mm) and coronal orbital area (1292.5 ​± ​97.1 ​mm2 versus 1177.9 ​± ​89.7 ​mm2). ConclusionsThis study employed deep learning to analyze a large dataset of 3D head MRI scans, achieving accurate and objective measurements of orbital morphology. We identified significant gender differences in orbital parameters, with males generally having larger structures. Additionally, we established a normative database for orbital dimensions, providing a valuable resource for future research on orbital disorders and potentially improving clinical diagnosis and treatment.