Synthetic Maps Research Articles

Gravity Interpretations to Investigate the Deep Geological and Structural Status of Middle Atlas Chain Region, Morocco

The Moroccan Middle Atlas is an intracontinental chain characterized by a combination of tectonic, sedimentary, and magmatic events. These events influenced the Middle Atlas basement structure and its morphology. In order to improve our understanding of the subsurface and deep geological structures, a gravimetric investigation was carried out. A residual gravity map and its horizontal derivative were computed and interpreted. We present in this work the main findings: The residual gravity anomaly map shows fourteen positive anomalies which are well correlated with the undulated Paleozoic basement in the Middle Atlas or with the High Moulouya and the Merhraoua Anticlinorium in the Northeastern part of the Middle Atlas chain.It also exhibits sub-circular negative anomalies related to the thick subsurface Meso-Cenozoic cover of the Middle Atlas chain depocenters located in the center and southeastern parts of this chain. These anomalies are correlated with the Bou Anguer, Bekrit, Tazouta, Enjil, and Almis Mermoucha basins; the Bounacer mount; and the extension of Boumia granite in the High Moulouya. The horizontal gravity gradient map discloses that the study area is mainly affected by the NE-SW trends as major faults, while the N-S, NW-SE, and E-W are minor faults. They’re superposed with the Southern Middle Atlas Accident, the North Middle Atlas Accident the Tizi N’tretten Accident, and their branches. They are due to the alpine neotectonic reactivation phases of the Paleozoic and Jurassic heritaged faults. A synthetic structural map displaying the lateral extension of most Middle Atlas faults was postulated. Consideration is also being given to explore the buried faults or unmapped ones. Our final map represents an efficient and effective tool to help decision-makers during mining exploration.

Simulated non-thermal emission of the supernova remnant G1.9 + 0.3

ABSTRACT Supernova remnants are the nebular leftover of defunct stellar environments, resulting from the interaction between a supernova blastwave and the circumstellar medium shaped by the progenitor throughout its life. They display a large variety of non-spherical morphologies such as ears that shine non-thermally. We have modelled the structure and the non-thermal emission of the supernova remnant G1.9 + 0.3 through 3D magnetohydrodynamic numerical simulations. We propose that the peculiar ear-shaped morphology of this supernova remnant results from the interaction of its blast wave with a magnetized circumstellar medium, which was previously asymmetrically shaped by the past stellar wind emanating from the progenitor star or its stellar companion. We created synthetic non-thermal radio and X-ray maps from our simulated remnant structure, which are in qualitative agreement with observations, forming ears on the polar directions. Our synthetic map study explains the discrepancies between the measured non-thermal radio and X-ray surface brightness distributions assuming that the inverse Compton process produces the observed X-ray emission.

Sea surface reconstruction from marine radar images using deep convolutional neural networks

The sea surface reconstructed from radar images provides valuable information for marine operations and maritime transport. The standard reconstruction method relies on the three-dimensional fast Fourier transform (3D-FFT), which introduces empirical parameters and modulation transfer function (MTF) to correct the modulation effects that may cause errors. In light of the convolutional neural networks’ (CNN) success in computer vision tasks, this paper proposes a novel sea surface reconstruction method from marine radar images based on an end-to-end CNN model with the U-Net architecture. Synthetic radar images and sea surface elevation maps were used for training and testing. Compared to the standard reconstruction method, the CNN-based model achieved higher accuracy on the same data set, with an improved correlation coefficient between reconstructed and actual wave fields of up to 0.96-0.97, and a decreased non-dimensional root mean square error (NDRMSE) of around 0.06. The influence of training data on the deep learning model was also studied. Additionally, the impact of the significant wave height and peak period on the CNN model’s accuracy was investigated. It has been demonstrated that the accuracy will fluctuate as the wave steepness increases, but the correlation coefficient remains above 0.90, and the NDRMSE remains less than 0.11.

Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation

X-shaped radio galaxies (XRGs) develop when certain extragalactic jets deviate from their propagation path. An asymmetric ambient medium (backflow model) or complex active galactic nucleus activity (jet-reorientation model) enforcing the jet direction to deviate may cause these structures. In this context, the present investigation focuses on the modeling of XRGs by performing 3D relativistic magnetohydrodynamic simulations. We implement different jet-propagation models applying an initially identical jet-ambient medium configuration to understand distinctive features. This study, the first of its kind, demonstrates that all adopted models produce XRGs with notable properties, thereby challenging the notion of a universal model. Jet reorientation naturally explains several contentious properties of XRGs, including wing alignment along the ambient medium’s primary axis, development of collimated lobes, and the formation of noticeably longer wings than active lobes. These XRGs disrupt the cluster medium by generating isotropic shocks and channeling more energy than in the backflow scenario. Our synthetic thermal X-ray maps of the cluster medium reveal four clear elongated cavities associated with the wing-lobe alignment, regardless of projection effects, but they affect their age estimation. We show that the depth and geometric alignment of the evolved cavities may qualify as promising characteristics of XRGs, which may be used to disentangle different formation scenarios.

A database of restriction maps to expand the utility of bacterial artificial chromosomes.

While Bacterial Artificial Chromosomes libraries were once a key resource for the genomic community, they have been obviated, for sequencing purposes, by long-read technologies. Such libraries may now serve as a valuable resource for manipulating and assembling large genomic constructs. To enhance accessibility and comparison, we have developed a BAC restriction map database. Using information from the National Center for Biotechnology Information's cloneDB FTP site, we constructed a database containing the restriction maps for both uniquely placed and insert-sequenced BACs from 11 libraries covering the recognition sequences of the available restriction enzymes. Along with the database, we generated a set of Python functions to reconstruct the database and more easily access the information within. This data is valuable for researchers simply using BACs, as well as those working with larger sections of the genome in terms of synthetic genes, large-scale editing, and mapping.

CO and [C ii] line emission of molecular clouds: the impact of stellar feedback and non-equilibrium chemistry

ABSTRACT We analyse synthetic 12CO, 13CO, and [C ii] emission maps of molecular cloud (MC) simulations from the SILCC-Zoom project. We present radiation, magnetohydrodynamic zoom-in simulations of individual clouds, both with and without radiative stellar feedback, forming in a turbulent multiphase interstellar medium following on-the-fly the evolution of e.g. H2, CO, and C+. We introduce a novel post-processing routine based on cloudy which accounts for higher ionization states of carbon due to stellar radiation in H ii regions. Synthetic emission maps of [C ii] in and around feedback bubbles show that the bubbles are largely devoid of [C ii], as recently found in observations, which we attribute to the further ionization of C+ into C2+. For both 12CO and 13CO, the cloud-averaged luminosity ratio, $L_\rm {CO}/L_\rm {[C\, \small {II}]}$, can neither be used as a reliable measure of the H2 mass fraction nor of the evolutionary stage of the clouds. We note a relation between the $I_\rm {CO}/I_\rm {[C\, \small {II}]}$ intensity ratio and the H2 mass fraction for individual pixels of our synthetic maps. The scatter, however, is too large to reliably infer the H2 mass fraction. Finally, the assumption of chemical equilibrium overestimates H2 and CO masses by up to 150 and 50 per cent, respectively, and $L_\rm {CO}$ by up to 60 per cent. The masses of H and C+ would be underestimated by 65 and 30 per cent, respectively, and $L_\rm {[C\, \small {II}]}$ by up to 35 per cent. Hence, the assumption of chemical equilibrium in MC simulations introduces intrinsic errors of a factor of 2 in chemical abundances, luminosities, and luminosity ratios.

The value of T2 mapping for evaluating the pathological type, grade and depth of myometrial invasion in endometrial carcinoma

Objective: To investigate the value of T2 map and synthetic T2WI generated by T2 mapping in evaluating the histological type, pathological classification and depth of myometrial invasion of endometrial carcinoma (EC). Methods: Seventy-three patients with pathologically proven EC diagnosed at the First Affiliated Hospital of Zhengzhou University from December 2019 to December 2021 and 42 healthy volunteers were enrolled in the study. All subjects underwent conventional MRI, diffusion weighted imaging (DWI) and T2 mapping sequence for the pelvic cavity to test the T2 values and the apparent diffusion coefficient (ADC) of the focus nidus of the patients and the normal endometrium of the volunteers. The T2 and ADC values of EC vs normal endometrium, and those of different histological types and pathological grades were compared. The receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of T2 and ADC values in determining the pathological type and classification of EC. In addition, two radiologists used synthetic T2WI combined with T2 map and conventional T2WI combined with DWI, respectively, to evaluate the depth of myometrial invasion, and compared the imaging results with the results of pathological diagnosis to evaluate the diagnostic efficacy of the two methods in determining the depth of myometrial invasion. Results: The T2 and ADC values of endometrial carcinoma were 85.0 (80.8, 92.5) ms and 0.71 (0.64, 0.77) ×10(-3) mm(2)/s, respectively, which were significantly lower than those of normal endometrium [147.4 (123.4, 176.7) ms and 1.46 (1.26, 1.76)×10(-3) mm(2)/s, respectively; both P<0.05]. The T2 values of endometrioid carcinoma (EA) [84.1 (79.5, 88.7) ms] were significantly lower than those of non-EA [98.8 (92.1, 102.8) ms; P<0.05]. There was no significant difference in ADC values between EA and non-EA (P=0.075). The T2 values of G1, G2 and G3 groups in EA were 89.1 (84.4, 94.4) ms, 83.6 (80.9, 86.2) ms, and 76.5 (71.4, 80.3) ms, respectively. There were significant differences in the T2 values between G1 vs G2, G1 vs G3, and G2 vs G3 groups, respectively (all P<0.017). Significant difference was also found in the ADC values between the G1 and G3 groups (P<0.017). The area under the ROC curve (AUC) of T2 values in distinguishing EA from non-EA was 0.867. The AUC of T2 values, ADC values and their combination in predicting high-grade EA was 0.888, 0.730 and 0.895, respectively. The accuracy of synthetic T2WI+ T2 map and conventional T2WI+ DWI in the diagnosis of deep myometrial invasion was 78.1% and 79.5%, respectively, with no significant difference (P>0.05). Conclusions: T2 mapping has great potential in preoperative evaluation of EC. The quantitative T2 value can be used in the diagnosis, pathological classification and grading of EC. The combination of synthetic T2WI and T2 map may be helpful to determine the depth of myometrial invasion.

Divergent Total Syntheses of Illicium Sesquiterpenes through Late-Stage Skeletal Reorganization.

We disclose unified, protecting-group-free, bioinspired divergent total syntheses of eight allo-cedrane and seco-prezizaane Illicium sesquiterpenes and formal syntheses of five anislactone sesquiterpenes. The efficiency of our approach derives from rapid access to the 15-carbon tricyclic carboxylic acid through cationic epoxide-ene cyclization and HAT oxygenation, transformation of this intermediate into three distinct tricyclic precursors via Lewis acid-mediated skeletal reorganizations, subsequent programmed oxidation level enhancement, and a biomimetic oxidation-initiated skeletal rearrangement cascade. Consequently, we created a synthetic correlation map of the three most prevalent Illicium sesquiterpene families.

PrimitivePose: Generic Model and Representation for 3D Bounding Box Prediction of Unseen Objects

A considerable amount of research is concerned with the challenging task of estimating three-dimensional (3D) pose and size for multi-object indoor scene configurations. Many existing models rely on a priori known object models, such as 3D CAD models and are therefore limited to a predefined set of object categories. This closed-set constraint limits the range of applications for robots interacting in dynamic environments where previously unseen objects may appear. This paper addresses this problem with a highly generic 3D bounding box detection method that relies entirely on geometric cues obtained from depth data percepts. While the generation of synthetic data, e.g. synthetic depth maps, is commonly used for this task, the well-known synth-to-real gap often emerges, which prohibits transition of models trained solely on synthetic data to the real world. To ameliorate this problem, we use stereo depth computation on synthetic data to obtain pseudo-realistic disparity maps. We then propose an intermediate representation, namely disparity-scaled surface normal (SN) images, which encodes geometry and at the same time preserves depth/scale information unlike the commonly used standard SNs. In a series of experiments, we demonstrate the usefulness of our approach, detecting everyday objects on a captured data set of tabletop scenes, and compare it to the popular PoseCNN model. We quantitatively show that standard SNs are less adequate for challenging 3D detection tasks by comparing predictions from the model trained on disparity alone, SNs and disparity-scaled SNs. Additionally, in an ablation study we investigate the minimal number of training samples required for such a learning task. Lastly, we make the tool used for 3D object annotation publicly available at: https://preview.tinyurl.com/3ycn8v5k . A video showcasing our results can be found at: https://preview.tinyurl.com/dzdzabek .

Analyzing spatial vulnerability in cities by combining the analytic hierarchy process and geographic information systems: The case of Vitoria-Gasteiz, Spain

ABSTRACT Vulnerability in urban areas has been on the rise following the global political and economic restructuring processes that have taken place since the 1980s. This increase in vulnerability has sparked a growing awareness of the need to understand the impact of these processes on the most vulnerable neighborhoods and areas of cities so as to develop effective policy responses. Although this vulnerability has become more evident in larger cities, this casuistry can also be seen in medium-sized cities. This paper examines the level of vulnerability of different neighborhoods in the medium-sized European city of Vitoria-Gasteiz (Basque Country, Spain), using a methodological approach which combines the analytic hierarchy process and geographic information systems, based on a number of indicators for different economic, demographic and migratory status-related dimensions. The results reveal a synthetic map of the vulnerability of neighborhoods in Vitoria-Gasteiz that shows an uneven impact of structural processes and policy initiatives implemented over decades. The paper concludes that synthetic maps of vulnerability make it possible to show the different realities in the urban framework and can contribute to defining public interventions tailored to these specific problems.

Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks.

Acquisition and pre-processing pipelines for diffusion-weighted imaging (DWI) volumes are resource- and time-consuming. Generating synthetic DWI scalar maps from commonly acquired brain MRI sequences such as fluid-attenuated inversion recovery (FLAIR) could be useful for supplementing datasets. In this work we design and compare GAN-based image translation models for generating DWI scalar maps from FLAIR MRI for the first time. We evaluate a pix2pix model, two modified CycleGANs using paired and unpaired data, and a convolutional autoencoder in synthesizing DWI fractional anisotropy (FA) and mean diffusivity (MD) from whole FLAIR volumes. In total, 420 FLAIR and DWI volumes (11,957 images) from multi-center dementia and vascular disease cohorts were used for training/testing. Generated images were evaluated using two groups of metrics: (1) human perception metrics including peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), (2) structural metrics including a newly proposed histogram similarity (Hist-KL) metric and mean squared error (MSE). Pix2pix demonstrated the best performance both quantitatively and qualitatively with mean PSNR, SSIM, and MSE metrics of 23.41 dB, 0.8, 0.004, respectively for MD generation, and 24.05 dB, 0.78, 0.004, respectively for FA generation. The new histogram similarity metric demonstrated sensitivity to differences in fine details between generated and real images with mean pix2pix MD and FA Hist-KL metrics of 11.73 and 3.74, respectively. Detailed analysis of clinically relevant regions of white matter (WM) and gray matter (GM) in the pix2pix images also showed strong significant (p < 0.001) correlations between real and synthetic FA values in both tissue types (R = 0.714 for GM, R = 0.877 for WM). Our results show that pix2pix's FA and MD models had significantly better structural similarity of tissue structures and fine details than other models, including WM tracts and CSF spaces, between real and generated images. Regional analysis of synthetic volumes showed that synthetic DWI images can not only be used to supplement clinical datasets, but demonstrates potential utility in bypassing or correcting registration in data pre-processing.

Towards MR contrast independent synthetic CT generation

The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics.To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose.On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model.Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.

Learning from single-defect wafer maps to classify mixed-defect wafer maps

Automation of wafer map pattern classification has been an active research topic for identifying the root causes of semiconductor manufacturing processes. Building a classification model is a primary challenge owing to the high cost of labeling wafer maps with their defect categories to create a training dataset. Single-defect wafer maps are relatively easy to label because their patterns are simple and clear. However, labeling mixed-defect wafer maps is difficult and time-consuming because they often have complex and ambiguous patterns. This study aims to address the problem of data insufficiency in which only normal and single-defect wafer maps are present in the training dataset, whereas mixed-defect wafer maps are also present in the test dataset. We propose a method for training a convolutional neural network (CNN) by using a training dataset of normal and single-defect wafer maps to accurately classify mixed-defect wafer maps. The proposed method utilizes three components to generate synthetic wafer maps from the training dataset: mixup, random rotation, and noise filtering. Mixup combines single-defect wafer maps to create synthetic mixed-defect wafer maps. Random rotation increases the diversity of the synthetic wafer maps. Noise filtering prevents the accumulation of random noise in synthetic wafer maps. The CNN is trained with the synthetic wafer maps generated through the proposed method, which include normal, single-defect, and mixed-defect variations. Through experimental investigation using the MixedWM38 dataset, we demonstrate that the proposed method significantly outperforms the existing methods.

Modelling planet-induced gaps and rings in ALMA discs: the role of in-plane radiative diffusion

ABSTRACT ALMA observations of protoplanetary discs in dust continuum emission reveal a variety of annular structures. Attributing the existence of such features to embedded planets is a popular scenario, supported by studies using hydrodynamical models. Recent work has shown that radiative cooling greatly influences the capability of planet-driven spiral density waves to transport angular momentum, ultimately deciding the number, position, and depth of rings and gaps that a planet can carve in a disc. However, radiation transport has only been treated via local thermal relaxation, not taking into account radiative diffusion along the disc plane. We compare the previous state-of-the-art models of planet–disc interaction with local cooling prescriptions to our new models that include cooling in the vertical direction and radiative diffusion in the plane of the disc, and show that the response of the disc to the induced spiral waves can differ significantly when comparing these two treatments of the disc thermodynamics. We follow up with synthetic emission maps of ALMA systems, and show that our new models reproduce the observations found in the literature better than models with local cooling. We conclude that appropriate treatment of radiation transport is key to constraining the parameter space when interpreting ALMA observations using the planet–disc interaction scenario.

Distillation of non-locality in porous solids

Non-local interactions are intrinsic to multiscale heterogeneous solids. The strength of the interactions exhibits a position-dependent character whose spatial distribution strongly correlates with the underlying microstructure. In this work, a variable-order fractional calculus-based framework to distill the position-dependent non-local effects is developed. By considering the example of porous plates, theoretical and numerical analyses will illustrate the ability of variable-order mechanics to enable parsimonious and causal models via a synthetic variable-order map that naturally measures the heterogeneous non-locality induced by the microstructure; in other terms, the non-classical role of the microstructure in determining the macroscopic response. The characteristic measure for non-locality, different from the local entanglement resulting from the porosity map, allows distillation of the macroscopic non-locality, analogous to quantum non-locality distillation. The macroscopic distillation also enables the additivity of the order map, that is the ability of assembled order maps to capture the response of combined porous plates.

A Comparative Analysis of Lineaments Extracted from Landsat 8 OLI &amp; ASTER-GDEM Data, in Talmakent Area, Western High Atlas, Morocco

Structural mapping by remote sensing is an essential component to completing the geological mapping of the Talmaknet region. It allows us to understand the regional tectonics of the southernmost segment of the Hercynian of Western Morocco, exploiting Geographic Information Systems (GIS) and satellite imagery processing techniques in order to obtain a good lineament discrimination. To this end, the approach adopted was based on preliminary processing of Landsat 8 OLI satellite imagery combined with a morpho-structural shading analysis technique, using the Digital Elevation Model (DEM), and applying several techniques of image enhancement and directional filters to the different treatments to improve the visibility of the linear structures in the images.Given the interesting number of fractions collected, a statistical and directional study was carried out on the synthetic maps to show the distribution and orientation of the lineaments. This analysis indicates a dominant NE-SW oriented geological fracturing, which agrees with the existing geological data in the area, confirming the effectiveness of GIS and remote sensing techniques in structural geology studies, especially to have a detailed mapping of the linear structures in the study area.

Synthetic Attenuation Correction Maps for SPECT Imaging Using Deep Learning: A Study on Myocardial Perfusion Imaging.

(1) Background: The CT-based attenuation correction of SPECT images is essential for obtaining accurate quantitative images in cardiovascular imaging. However, there are still many SPECT cameras without associated CT scanners throughout the world, especially in developing countries. Performing additional CT scans implies troublesome planning logistics and larger radiation doses for patients, making it a suboptimal solution. Deep learning (DL) offers a revolutionary way to generate complementary images for individual patients at a large scale. Hence, we aimed to generate linear attenuation coefficient maps from SPECT emission images reconstructed without attenuation correction using deep learning. (2) Methods: A total of 384 SPECT myocardial perfusion studies that used 99mTc-sestamibi were included. A DL model based on a 2D U-Net architecture was trained using information from 312 patients. The quality of the generated synthetic attenuation correction maps (ACMs) and reconstructed emission values were evaluated using three metrics and compared to standard-of-care data using Bland-Altman plots. Finally, a quantitative evaluation of myocardial uptake was performed, followed by a semi-quantitative evaluation of myocardial perfusion. (3) Results: In a test set of 66 test patients, the ACM quality metrics were MSSIM = 0.97 ± 0.001 and NMAE = 3.08 ± 1.26 (%), and the reconstructed emission quality metrics were MSSIM = 0.99 ± 0.003 and NMAE = 0.23 ± 0.13 (%). The 95% limits of agreement (LoAs) at the voxel level for reconstructed SPECT images were: [-9.04; 9.00]%, and for the segment level, they were [-11; 10]%. The 95% LoAs for the Summed Stress Score values between the images reconstructed were [-2.8, 3.0]. When global perfusion scores were assessed, only 2 out of 66 patients showed changes in perfusion categories. (4) Conclusion: Deep learning can generate accurate attenuation correction maps from non-attenuation-corrected cardiac SPECT images. These high-quality attenuation maps are suitable for attenuation correction in myocardial perfusion SPECT imaging and could obviate the need for additional imaging in standalone SPECT scanners.

3D physico-chemical model of a pre-stellar core

Context. Pre-stellar cores represent the earliest stage of the formation process of stars and planets. By characterizing the physical and chemical structure of these cores, we can establish the initial conditions for star and planet formation and determine to what degree the chemical composition of pre-stellar cores is inherited by the later stages. Aims. We aim to determine the underlying causes of spatial chemical segregation observed in pre-stellar cores and study the effects of the core structure and external environment on the chemical structure of pre-stellar cores. Methods. A three-dimensional (3D) magnetohydrodynamic model of a pre-stellar core embedded in a dynamic star-forming cloud was post-processed with a sequentially continuum radiative transfer, a gas-grain chemical model, and a line-radiative transfer model. The results were analyzed and compared to observations of CH3OH and c-C3H2 in L1544. We compared nine different chemical models to the observations to determine which initial conditions are compatible with the observed chemical segregation in the prototypical pre-stellar core L1544. Results. The model is able to reproduce several aspects of the observed chemical differentiation in L1544. Extended methanol emission is shifted towards colder and more shielded regions of the core envelope, while c-C3H2 emission overlaps with the dust continuum, which is consistent with the observed chemical structure. Furthermore, these results are consistent across a broad spectrum of chemical models. Increasing the strength of the interstellar radiation field or the cosmic-ray ionization rate with respect to the typical values assumed in nearby star-forming regions leads to synthetic maps that are inconsistent with the observed chemical structure. Conclusions. Our model shows that the observed chemical dichotomy in L1544 can arise as a result of uneven illumination due to the asymmetrical structure of the 3D core and the environment within which the core has formed. This highlights the importance of the 3D structure at the core-cloud transition on the chemistry of pre-stellar cores. The reported effect is likely to affect later stages of the formation process of stars and planets through chemical inheritance.

Modelling Local Bubble analogs: synthetic dust polarization maps

ABSTRACT We present a study of synthetic observations of polarized dust emission at 353 GHz as seen by an observer within a cavity in the interstellar medium (ISM). The cavity is selected from a magnetohydrodynamic simulation of the local ISM with time-dependent chemistry, star formation, and stellar feedback in form of supernova explosions with physical properties comparable to the Local Bubble ones. We find that the local density enhancement together with the coherent magnetic field in the cavity walls makes the selected candidate a translucent polarization filter to the emission coming from beyond its domains. This underlines the importance of studying the Local Bubble in further detail. The magnetic field lines inferred from synthetic dust polarization data are qualitatively in agreement with the all-sky maps of polarized emission at 353 GHz from the Planck satellite in the latitudes interval 15° ≲ |b| ≲ 65°. As our numerical simulation allows us to track the galactic mid-plane only out to distances of $250\,$ pc, we exclude the region |b| ≲ 15° from our analysis. At large galactic latitudes, our model exhibits a high degree of small-scale structures. On the contrary, the observed polarization pattern around the Galactic Poles is relatively coherent and regular, and we argue that the global toroidal magnetic field of the Milky Way is important for explaining the data at |b| ≳ 65°. We show that from our synthetic polarization maps, it is difficult to distinguish between an open and a closed galactic cap using the inferred magnetic field morphology alone.

The role of structural inheritance in the tectonic configuration of the Moroccan Meseta Coastal Block: Insights from morpho-structural and aeromagnetic data

The Coastal Block (CB) of the Western Meseta (Morocco) is located between the Atlantic passive margin and the Middle and High Atlas Mountains. In this contribution, a morpho-structural analysis using topographic data and an aeromagnetic data interpretation have been integrated to map tectonically related structures and to unravel the surface-subsurface faults interaction and their geodynamic implications in the CB. The morpho-structural analysis allowed the structural lineaments mapping through the processing of the topographic and hydrographic data. The application of derivatives operators on the magnetic anomaly map reduced to the pole allowed the assessment of the tectonic framework. The visual integrated analysis of surface and subsurface structures allowed to establish a synthetic structural map of the CB. Several N–S to NNE-SSW, NW-SE and NE-SW oriented fault systems were identified, some of which were confirmed and others were newly outlined. Four geometrically horst and graben type blocks were well distinguished, including the Triassic Basins of Berrechid to the north and of Doukkala to the south separated by the Casablanca Paleozoic block. Meanwhile, the Sahel represents an uplifted zone separating the Doukkala Basin from the continental shelf to the west. Rooted faults bounding these blocks, which are heavily inherited from the pre-Mesozoic tectonic events, record variable reactivation indicators related to the Neogene-Quaternary Atlasic shortening.