Consecutive Layers Research Articles

Rolled-up gastroretentive oral dosages for controlled release of riboflavin and propranolol

We present a simple approach to make gelatin gastroretentive oral dosages for controlled release of riboflavin and propranolol. The approach is based on rolling-up of thin self-adhesive gelatin films, on which the reservoirs of the drugs are formed by casting. The radial position of the reservoir and, hence, the lag time and the rate of the drugs release are determined by the lateral position of the reservoir on the film before rolling. Gelatin is stabilized against dissolving in the gastric environment by enzymatic cross-linking mediated by transglutaminase (Tgase). The dosages, which have the form of tight scrolls, are stabilized against unrolling by the crosslinking of the consecutive layers. The scrolls swell in the release media, Fasted State Simulated Gastric Fluid (FaSSGF), to the dimensions which make difficult their passage by the human pyloric sphincter, but remain mechanically robust to resist the stomach peristaltic contractions during at least 24 h, sufficient for the drug release. Eventually the scrolls are degraded by the release media and can be evacuated from the stomach.

VCSEL-based photonic spiking neural networks for ultrafast detection and tracking

Inspired by efficient biological spike-based neural networks, we demonstrate for the first time the detection and tracking of target patterns in image and video inputs at high-speed rates with networks of multiple artificial spiking optical neurons. Using photonic systems of in-parallel spiking vertical cavity surface emitting lasers (VCSELs), we demonstrate the implementation of multiple convolutional kernel operators which, in combination with optical spike signalling, enable the detection and tracking of target features in images/video feeds at an ultrafast photonic operation speed of 1 ns per pixel. Alongside a single layer optical spiking neural network (SNN) demonstration, a multi-layer network of photonic (GHz-rate) spike-firing neurons is reported where the photonic system successfully tracks a large complex feature (Handwritten Digit 3). The consecutive photonic layers perform spike-enabled image reduction and convolution operations, and interact with a software-implemented SNN, that learns the feature patterns that best identify the target to provide a high detection efficiency even in the presence of a distractor feature. This work therefore highlights the effectiveness of combining neuromorphic photonic hardware and software SNNs, for efficient learning and ultrafast operation, thanks to the use of spiking light signals, towards tackling complex AI and computer vision problems.

Fabrication of architecturally designed steel for improving isotropy of mechanical properties using directed energy deposition

As an excellent illustration of architectural alloy designs, multilayered structures, which incorporates high-strength and high-ductility alloys in their layered composition, can accomplish unusual combinations of strength and ductility. This is possible because the continuous microstructure consisting of a single phase without a phase boundary in each layer results in a uniform distribution of strain. However, the multilayered structure shows anisotropic mechanical properties due to discontinuity from the phase boundary in the perpendicular direction (z-direction). In this work, an alloy system with a 3D continuous microstructure with two distinct steels was manufactured using laser-based directed energy deposition. The alternately deposited lines of two distinct alloys, along with the rotated consecutive layers, contribute to creating a woven-like structure. This architectural structure features continuous microstructures in multiple directions. Successfully manufactured both multilayered and woven-like systems demonstrated advanced yield strength and uniform elongation compared to each constituting alloy in the xy plane. In the z-direction, however, only the woven-like system exhibited improved properties as in the xy plane; the multilayered system did not. This isotropy in the mechanical properties of a woven-like system results from a 3D continuous microstructure in the z-direction. The strain analyses confirmed the deformation of the brittle body-centered cubic phase and partitioning to the ductile phase which are key mechanisms of mechanical property enhancement with continuous microstructure.

New nanoscale multilayer magnetron sputtered Ti-DLC/DLC coatings with improved mechanical properties

Two sets of nanoscale multilayer Ti-DLC/DLC coatings, with similar chemical composition but different periods between consecutive layers (0.4 and 5.5 nm), were deposited by non-reactive direct current magnetron sputtering using different rotational speeds of the substrate holder (12 and 1 rpm, respectively) in this work. The results show that the overall Ti content in the coatings varied from ≈ 2.9 to ≈ 8.2 at. % in both cases. Significant fluctuations in the Ti concentration between the middle points of the DLC and Ti-DLC layers were observed for the coatings deposited at 1 rpm. All the coatings deposited were mainly amorphous, presenting a columnar structure and a cauliflower-like granular surface morphology. The sp2/sp3 ratio increased as the concentration of Ti rose. The coatings deposited at 1 rpm demonstrated better mechanical properties (up to 33 % improvement in hardness and up to 25 % improvement in the elastic modulus) when compared to the equivalent coatings deposited at 12 rpm. The multilayer titanium doped coating (deposited at 1 rpm) with 5.9 at. % Ti presented the most significant improvement in mechanical properties when compared to pure DLC and monolayer coatings deposited at 12 rpm. The results confirmed that it is possible to tune the performance of this type of coatings by depositing alternating layers of pure DLC and Ti-doped DLC.

Interlayer mechanical performance of 3D-printed cementitious systems: A comprehensive study on operational and material parameters

This study delves into the interlayer mechanical performance of 3D-printed cementitious materials, exploring a variety of operational and material parameters to understand the practical effects on the performance of printed structures. To achieve this, a comprehensive battery of tests, encompassing compression, triplet shear, direct tensile, and diagonal tension tests, was conducted. Within the scope of this investigation, the anisotropic performance in perpendicular, lateral, and parallel directions was examined, along with varying printing time intervals (0, 15, 30, and 60 min between consecutive layers), material aging times (0, 30, and 60 min), and different manufacturing methodologies, including cast, horizontal-printed, and vertical-printed specimens. The research findings indicate that well-established mechanical tests, commonly utilized for evaluating masonry structures, can be effectively transferred and applied to assess 3D printed structures. A noteworthy discovery is the anisotropic behavior observed in compressive strength, characterized by diminishing results from perpendicular to parallel and parallel to lateral loading directions. Extended printing time intervals have an adverse impact on the interlayer mechanical performance of cementitious systems. Material aging time also significantly influences bond strength, particularly in mixtures aged for 60 min. In conclusion, it is evident that material aging exerts a more substantial influence on bond strength compared to printing time intervals in cementitious systems. Additionally, it was observed that vertically printed specimens replicate the mechanical performance and fracture mechanism of cast specimens, while horizontally printed specimens exhibit slightly lower performance with distinct fracture patterns.

A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks

Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect and do not provide appropriate interpretation as a guideline to explain and promote superior performance in the target task. Therefore, we provide an interpretable approach for our presented deep transfer learning (DTL) models to overcome such drawbacks, working as follows. We utilize several pre-trained models including SEResNet152, and SEResNeXT101. Then, we transfer knowledge from pre-trained models via keeping the weights in the convolutional base (i.e., feature extraction part) while modifying the classification part with the use of Adam optimizer to deal with classifying healthy controls and T2D based on single-cell gene regulatory network (SCGRN) images. Another DTL models work in a similar manner but just with keeping weights of the bottom layers in the feature extraction unaltered while updating weights of consecutive layers through training from scratch. Experimental results on the whole 224 SCGRN images using five-fold cross-validation show that our model (TFeSEResNeXT101) achieving the highest average balanced accuracy (BAC) of 0.97 and thereby significantly outperforming the baseline that resulted in an average BAC of 0.86. Moreover, the simulation study demonstrated that the superiority is attributed to the distributional conformance of model weight parameters obtained with Adam optimizer when coupled with weights from a pre-trained model.

Evaluating 3D printed mesh geometries in ceramic LiB electrodes

Additive manufacturing techniques have the potential to promote a paradigmatic change in the electrode fabrication processes for lithium-ion batteries (LiBs) as they may offer alternative component designs to boost their performance or to customise the application. The present research work explores the use of low-cost fused filament fabrication (FFF) 3D printing to fabricate Li4Ti5O12 (LTO) mesh electrodes in the search for enlarged electrochemically active areas. Using different nozzle diameters (ND), we have 3D printed several mesh electrodes that after sintering allow an increase in the surface to volume ratio by up to ≈290% compared to conventional flat cylindrical geometries. As the conventional route to produce 3D printed meshes, i.e. stacking of consecutive layers with a 90° rotation, leads to problems of vertical misalignment that may affect the electrical contact, we have developed a new compact design that maximises the contact between layers. All the 3D printed mesh electrodes with thicknesses of 400 and 800 μm, exhibit electrochemical performance very close to those of thin (70 μm) electrodes, e.g. 175 mAh g−1 at C/2 in the case of ND = 100 μm, which is the theoretical capacity value for LTO. At higher C-rates, 800 μm-thick mesh electrodes with larger ND exhibit a marked drop in the reversible capacity (28 mAh g−1 at 8 C), although the values obtained improve notably those of the equivalent thick solid electrode (almost null at 8 C). The compact design demonstrated superior performance at high C-rates, improving by ≈70% the results of the best conventional mesh electrode at 8 C for 800 μm electrodes. These results highlight the potential of FFF-3D printing to generate novel high aspect ratio geometries and the impact of design and printing parameters on the performance of LiB electrode materials. Exploring alternative efficient geometries may facilitate the integration of thick electrodes in high energy density LiBs.

Extended magic phase in twisted graphene multilayers

Theoretical and experimental studies have verified the existence of “magic angles” in twisted bilayer graphene, where the rotation angle between layers gives rise to flat bands and consequently exotic correlated phases. Recently, magic-angle phenomena have been predicted and reported in other graphene systems, for instance, multilayers with alternating twist angles and trilayers with identical twist angles between consecutive layers. In this paper, we present a comprehensive theoretical study on flat bands in general twisted graphene systems. Using the continuum model in the chiral limit, we demonstrate the existence of flat bands in a variety of multilayers where the ratios between twist angles are rational and develop a framework for predicting magic-angle sets in trilayer configurations with arbitrary ratio of rotation angles. Our results are corroborated by tight-binding calculations. Remarkably, the technique we developed can be extended to systems with many layers of graphene. Our results suggest that flat bands can exist in graphene multilayers with angle disorder, that is, narrow samples of turbostatic graphite, point to the existence of a continuous, connected magic surface in trilayer configuration space, and compare favourably with contemporary experiments on trilayer moiré quasicrystals. Published by the American Physical Society 2024

Investigation of the thermal gradients formed during the process of cooling of WAAM built Al99.7 specimen

This work presents a preliminary experiment used to determine the possibility of developing a thermal model used to predict the thermal gradients formed during the process of wire and arc additive manufacturing (WAAM). For this purpose, an Al99.7 specimen was built using gas metal arc welding (GMAW) in the cold arc pulse mode. The temperature of both the substrate and each consecutive layer was measured using thermocouples and infrared thermometers and the experimental data was used for the calibration of the developed thermal model. The obtained results can be used for the further development of the thermal model.

A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography.

Polysomnography (PSG) is unique in diagnosing sleep disorders, notably obstructive sleep apnea (OSA). Despite its advantages, manual PSG data grading is time-consuming and laborious. Thus, this research evaluated a deep learning-based automated scoring system for respiratory events in sleep-disordered breathing patients. A total of 1000 case PSG data were enrolled to develop a deep learning algorithm. Of the 1000 data, 700 were distributed for training, 200 for validation, and 100 for testing. The respiratory events scoring deep learning model is composed of five sequential layers: an initial layer of perceptrons, followed by three consecutive layers of long short-term memory cells, and ultimately, an additional two layers of perceptrons. The PSG data of 100 patients (simple snoring, mild, moderate, and severe OSA; n = 25 in each group) were selected for validation and testing of the deep learning model. The algorithm demonstrated high sensitivity (95% CI: 98.06-98.51) and specificity (95% CI: 95.46-97.79) across all OSA severities in detecting apnea/hypopnea events, compared to manual PSG analysis. The deep learning model's area under the curve values for predicting OSA in apnea-hypopnea index ≥ 5, 15, and 30 groups were 0.9402, 0.9388, and 0.9442, respectively, showing no significant differences between each group. The deep learning algorithm employed in our study showed high accuracy in identifying apnea/hypopnea episodes and assessing the severity of OSA, suggesting the potential for enhancing both the efficiency and accuracy of automated respiratory event scoring in PSG through advanced deep learning techniques.

Ultrathin nitrogen-doped carbon Ti3C2Tx-TiN heterostructure derived from ZIF-8 nanoparticles sandwiched MXene for high-performance capacitive deionization

Rational design of high-performance electrode materials is crucial for enhancing desalination performance of capacitive deionization (CDI). Here, ultrathin nitrogen-doped carbon/Ti3C2Tx-TiN (NC/MX-TiN) heterostructure was developed by pyrolyzing zeolite imidazolate framework-8 (ZIF-8) nanoparticles sandwiched MXene (ZSM), which were formed by assembling ultrafine ZIF-8 nanoparticles with size of 20 nm on both sides of MXene nanosheets. The introduction of ultrasmall ZIF-8 particles allowed for in situ nitridation of the MXene during pyrolysis, forming consecutive TiN layers tightly connected to the internal MXene. The two-dimensional (2D) heterostructure exhibited remarkable properties, including high specific surface area and excellent conductivity. Additionally, the resulting TiN demonstrated exceptional redox capability, which significantly enhanced the performance of CDI and ensured cycling stability. Benefiting from these advantages, the NC/MX-TiN exhibited a maximum adsorption capacity of 45.6 mg g−1 and a steady cycling performance in oxygenated saline water over 50 cycles. This work explores the rational design and construction of MXene-based 2D heterostructure and broadens new horizons for the development of novel CDI electrode materials.

Dilated Spectral–Spatial Gaussian Transformer Net for Hyperspectral Image Classification

In recent years, deep learning-based classification methods for hyperspectral images (HSIs) have gained widespread popularity in fields such as agriculture, environmental monitoring, and geological exploration. This is owing to their ability to automatically extract features and deliver outstanding performance. This study provides a new Dilated Spectral–Spatial Gaussian Transformer Net (DSSGT) model. The DSSGT model incorporates dilated convolutions as shallow feature extraction units, which allows for an expanded receptive field while maintaining computational efficiency. We integrated transformer architecture to effectively capture feature relationships and generate deep fusion features, thereby enhancing classification accuracy. We used consecutive dilated convolutional layers to extract joint low-level spectral–spatial features. We then introduced Gaussian Weighted Pixel Embedding blocks, which leverage Gaussian weight matrices to transform the joint features into pixel-level vectors. By combining the features of each pixel with its neighbouring pixels, we obtained pixel-level representations that are more expressive and context-aware. The transformed vector matrix was fed into the transformer encoder module, enabling the capture of global dependencies within the input data and generating higher-level fusion features with improved expressiveness and discriminability. We evaluated the proposed DSSGT model using five hyperspectral image datasets through comparative experiments. Our results demonstrate the superior performance of our approach compared to those of current state-of-the-art methods, providing compelling evidence of the DSSGT model’s effectiveness.

Residual Stresses in a Wire and Arc-Directed Energy-Deposited Al–6Cu–Mn (ER2319) Alloy Determined by Energy-Dispersive High-Energy X-ray Diffraction

In order to enable and promote the adoption of novel material processing technologies, a comprehensive understanding of the residual stresses present in structural components is required. The intrinsically high energy input and complex thermal cycle during arc-based additive manufacturing typically translate into non-negligible residual stresses. This study focuses on the quantitative evaluation of residual stresses in an Al–6Cu–Mn alloy fabricated by wire and arc-directed energy deposition. Thin, single-track aluminum specimens that differ in their respective height are investigated by means of energy-dispersive high-energy X-ray diffraction. The aim is to assess the build-up of stresses upon consecutive layer deposition. Stresses are evaluated along the specimen build direction as well as with respect to the lateral position within the component. The residual stress evolution suggests that the most critical region of the specimen is close to the substrate, where high tensile stresses close to the material’s yield strength prevail. The presence of these stresses is due to the most pronounced thermal gradients and mechanical constraints in this region.

Enhanced thermoelectric performance of a wide-bandgap twisted heterostructure of graphene and boron nitride.

The manipulation of the relative twist angle between consecutive layers in two-dimensional (2D) materials dramatically modulates their electronic characteristics. Twisted bilayer graphene (tblg) and twisted boron nitride (tBN) exhibit Moiré patterns that have the potential to revolutionize various fields, from electronics to quantum materials. Here, the electronic and thermoelectric properties of 21.8° tblg and 21.8° tBN and a 21.8° twisted graphene/boron nitride (Gr/BN) heterostructure were investigated using density functional theory and Boltzmann transport theory. The twisted Gr/BN heterostructure possesses a wide band gap of 1.95 eV, which overcomes the limitations of the absence of a band gap of graphene and boron nitride's extremely wide band gap. A significant increase in thermoelectric power factor was obtained for the heterostructure compared to its parent materials, 21.8° tblg and 21.8° tBN. It has a thermal conductivity of 5.88 W m-1 K-1 at 300 K, which is much lower than those of 21.8° tblg and 21.8° tBN. It is observed that graphene plays an important role in electron transport or power factor enhancement, whereas BN helps in reducing the thermal conductivity in twisted Gr/BN systems. A strong role of boundary scattering in thermal transport compared to electrical transport was observed. A high figure of merit (ZT) of 1.28 for the twisted Gr/BN heterostructure at a ribbon width of L = 10 nm and T = 900 K was obtained. This suggests its suitability as an effective material for thermoelectric applications.

Impact of Nitrogen Fertilization on Soil Nitrate Concentrations in an Onion Field in the Saiss Basin

High nitrogen (N) fertilizer rates can result in excessive nitrate (NO3-N) in soils and groundwater. The Saiss basin is an agricultural area in Morocco, where substantial increases in nonpoint source pollution from excessive N fertilization is commonly observed. The purpose of this study was to determine the short-term effect of N fertilizer application rates on soil NO3-N and soil water contents (SWC) in an onion (Allium cepa L.) field over two consecutive years (2021 and 2022). The field experiment was conducted in a randomized complete block design. Six N rates, namely 0, 90, 135, 180, 225, and 270 Kg N/ha were considered in drip-irrigated onion crops. Soil samples were collected from five consecutive soil layers, namely 0-20, 20-40, 40-60, 60-80, and 80-100 cm, and analyzed for NO3-N contents and SWC. SWC were lower before irrigation and increased significantly after irrigation for both seasons. There was no significant effect of N rates on SWC. Soil NO3-N decreased significantly with time. In the first season, lowest NO3-N levels was observed after harvest for all N rates. Visually, a marked accumulation of nitrate in soil was observed in the three highest N rates after harvest. Statistically, N rates did not show significant effect on soil NO3-N (p>0,05) in 2021 and 2022. This finding may be explained by the predominance of the denitrification process at Douyet station.

СИСТЕМА ТРАДИЦИОННОГО ПИТАНИЯ НАГАЙБАКОВ: СТРУКТУРНЫЙ АНАЛИЗ

The ethnic processes that took place in the Southern Urals since the mid-18th century had been accompanied by Slavic-Turkic contacts and the emergence of new identities. One of these was the Nagaybaks, baptized Tatars who were transferred to the Cossacks. Their ethno-cultural image was formed during contacts with unbaptized groups — the Chuvash people, the Transkama Udmurts and the Eastern Mari. From the mid-19th century, Russian influences increased, and relations with the Kazakhs appeared. As a result, by the beginning of the 20th century, the Nagaybaks had developed their own original complex of economy and culture, which allowed them to be considered as a new ethnic phenomenon. Despite numerous innovations, the Nagaybaks culture retains archaic features, obviously reflecting deep historical ties with the Tatars and the Turkic world as a whole. In this regard, nutrition system seems to be a convenient material for studying. The article offers its structural-chronological and structural-functional analysis. Three chronologically consecutive layers in the genesis are distinguished — substrate, main and adstrate-superstrate. Each of them reflects the stages of the Nagaybaks’ ethnic history and their ancestors, the Tatars and other Turks. A significant part of the vocabulary related to agriculture, cattle breeding and the raw material base of the nutrition system reveals similarities with such phenomena among most Turkic peoples and defines its substrate. The main one reflects the Kazan-Tatar period. The later superstrate phenomena are Russian, Kazakh and other influences.

Exploring the potential of an Aloe vera and honey extract loaded bi-layered nanofibrous scaffold of PCL-Col and PCL-SBMA mimicking the skin architecture for the treatment of diabetic wounds.

Diabetic wounds are often chronic in nature, and issues like elevated blood sugar, bacterial infections, oxidative stress and persistent inflammation impede the healing process. To ensure the appropriate healing of wounds, scaffolds should promote complete tissue regeneration in wounds, both functionally and structurally. However, the available scaffolds lack the explicit architecture and functionality that could match those of native skin, thus failing to carry out the scar-free skin regeneration in diabetic wounds. This study deals with the synthesis of a bi-layered nanofibrous scaffold mimicking the native skin architecture in terms of porosity and hydrophobic-hydrophilic gradients. In addition, herbal extracts of Aloe vera and litchi honey were added in consecutive layers to manage the high blood glucose level, inflammation, and increased ROS level associated with diabetic wounds. In vitro studies confirmed that the prepared scaffold with herbal extracts showed enhanced proliferation of skin cells with good mechanical strength, degradability, anti-bacterial and anti-diabetic properties. The scaffold also demonstrated superior wound healing in vivo with quicker scar-free wound recovery and appropriate skin regeneration, compared to conventional treatment. Altogether, the synthesized herbal extract loaded bi-layered nanofibrous scaffold can be used as a regenerative template for hard-to-heal diabetic wounds, offering a new strategy for the management of chronic wounds.

Electrochemical Characterization of Artificial Solid Electrolyte Interphase Developed on Graphite Via ALD

During formation of Li-ion batteries, a ‘natural’ solid electrolyte interphase (SEI) is formed at the anode side by decomposition products of the electrolyte. The properties of the SEI are extremely decisive for the overall battery properties, such as rate capability and cycling stability. However, the SEI formation consumes Li, leading to so called ‘formation losses’ that can make up to 15% of the theoretical energy density of the battery. Several approaches have been presented to overcome formation losses while preserving excellent overall battery properties. Particularly, electrochemical prelithiation and the application of artificial SEIs prior cell assembly are considered to effectively reduced formation losses while improving the interfacial charge transfer properties and increasing cycling stability.Herein, the authors present an innovative approach of applying a multifunctional artificial SEI on anode material powders via consecutive atomic layer deposition (ALD) cycles. As a model system, graphite powder has been chosen to be modified and characterized.The resulting electrodes show substantially improved electrochemical performance in half cells and full cells, regarding initial capacity loss, CE and cycling stability. Furthermore, model electrodes consisting of a single layer of graphite particles were manufactured, which exclude the effects of a typical composite electrode and therefore reveal the intrinsic properties of the active material. Using this approach, the interfacial kinetics and the rate capability are investigated comparatively between pristine and ALD coated electrodes, revealing the impact of the artificial SEI on the materials level.

Possibility of using inexpensive steel protected by deposited vacuum-arc coatings as a basis for parts and tools coating technology

The aim of the work was to identify the possibility of using under extreme conditions parts made of inexpensive steel, protected by sequentially deposited films — Cr and then simultaneously TiN + CrN, micron thickness. Steel grade St3 (Fe ~ 97%) was chosen as the basis. In our country, as in many others, this steel is not only the most common structural material with easy processing and low cost, but also produced in large volumes. The deposition of two consecutive layers of films — Cr and TiN + CrN on steel substrates was carried out using vacuum-arc technology (Arc-PVD). The measurements were carried out after the process of periodic heating of the samples to a temperature of 650 0C, followed by rapid cooling, implemented by immersing the samples in water. The main parameters of the coating were measured — wear resistance, heat resistance, microhardness. Each thermal effect lasted 20 hours, and the entire heating-cooling process was carried out 5 times. The results obtained in this work can be considered very promising for the protection of inexpensive steels.

Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods

Abstract. This paper develops a calibration methodology of the artificial absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. Specifically, we consider the damped wave equation (DWE) that results from adding a dissipation term to the original wave equation, the sponge boundary layer (SBL) that applies an exponentially decaying factor directly to the wavefields, and finally, a classical split formulation of the perfectly matched layer (PML). These three techniques belong to the same family of absorbing boundary layers (ABLs), where outgoing waves and edge reflections are progressively damped across a grid zone of NABL consecutive layers. The ABLs used are compatible with a pure Fourier formulation of PSTD. We first characterize the three ABLs with respect to multiple sets of NABL and their respective absorption parameters for homogeneous media. Next, we validate our findings in heterogeneous media of increasing complexity, starting with a layered medium and finishing with the SEG/EAGE 3D salt model. Finally, we algorithmically compare the three PSTD–ABL methods in terms of memory demands and computational cost. An interesting result is that PML, despite outperforming the absorption of the other two ABLs for a given NABL value, requires approximately double the computational time. The parameter configurations reported in this paper can be readily used for PSTD simulations in other test cases, and the ABL calibration methodology can be applied to other wave propagation schemes.