Main Layer Research Articles

LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

AbstractThe technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.

Investigation of the driving voltage on the high performance flexible ATF-ECDs based on PET/ITO/NiOX/LiTaO3/WO3/ITO

High performance flexible all-thin-film electrochromic devices (ATF-ECDs) have been fabricated and systematically investigated by operating with different driving voltages during the electrochromic processes. The device structure (cross-section) and material properties of some main functional layers were presented and analysed. The electrochromic properties including kinetic and spectral tests were systematically investigated through combining chronoamperometry, cyclic voltammetry measurements and optical measurements. In addition, the open circuit memory measurement was also carried out. A much higher driving voltage might lead to a current leakage inside the device during coloring process. A proper driving voltage is needed for achieving high device performances. More details were widely described and deeply discussed.

Ambient Monitoring Portable Sensor Node for Robot-Based Applications.

The leakage of gases and chemical vapors is a common accident in laboratory processes that requires a rapid response to avoid harmful effects if humans and instruments are exposed to this leakage. In this paper, the performance of a portable sensor node designed for integration with mobile and stationary robots used to transport chemical samples in automated laboratories was tested and evaluated. The sensor node has four main layers for executing several functions, such as power management, control and data preprocessing, sensing gases and environmental parameters, and communication and data transmission. The responses of three metal oxide semiconductor sensors, BME680, ENS160, and SGP41, integrated into the sensing layer have been recorded for various volumes of selected chemicals and volatile organic compounds, including ammonia, pentane, tetrahydrofuran, butanol, phenol, xylene, benzene, ethanol, methanol, acetone, toluene, and isopropanol. For mobile applications, the sensor node was attached to a sample holder on a mobile robot (ASTI ProBOT L). In addition, the sensor nodes were positioned close to automation systems, including stationary robots. The experimental results revealed that the tested sensors have a different response to the tested volumes and can be used efficiently for hazardous gas leakage detection and monitoring.

Exploring the differences of moisture traceability methods based on MixSIAR model under different nitrogen applications of wheat in the Arid Region of Northwest China

The Bayesian stable isotope mixing (MixSIAR) model was widely used in water source tracing. However, double isotope and single isotope calculated by the MixSIAR model led to different results in terms of previous studies. The effect of different nitrogen treatments on the water traceability results of wheat crops is still unclear. This study investigated the wheat root water uptake patterns at different nitrogen application gradients, as well as at different isotopes in the MixSIAR model. The results showed the main soil water uptake layer was relative constant at the treatment of 15% nitrogen reduction, and the water source was mainly from 0–10 cm soil layer from jointing to harvest. The main soil water uptake layer significantly varied at the treatment of 30% nitrogen reduction, which yielded the highest. 45% reduction of nitrogen treatment showed the deepest soil water uptake absorption on average with the whole wheat growth period. The consistency of the results between the two single isotope methods was better in the early stage of wheat and worse in the later stage. The inconsistency of root water uptake distribution results between the two single isotope methods might be influenced by the water transport mechanism of wheat stem and sampling errors. The double isotope method had the lowest uncertainty, but it might amplify the error of the sampling process. The uncertainty of the single hydrogen isotope method was less than single oxygen isotope method. This study provided a new evaluation of hydrogen-oxygen stable isotope traceability methods for wheat under different nitrogen treatments, and gave more ideas and insights for subsequent crop water traceability by the MixSIAR method.

Electrical Properties of Vertical Dominant Charge Structures Observed in Corsican Thunderstorms With a LMA

AbstractLightning characteristics of Corsican storms with different charge structures are investigated in this study. Observations of an LMA network are used to document the total lightning activity. Complementary lightning observations of the lightning detection network Météorage are also used. A clustering algorithm is used to build a database of electrical cells from June to October 2018. A method is also applied to infer the vertical charge structure, as dominant dipoles, per 10‐min period for each electrical cell. As an example, one cell recorded in July 2018 is discussed. The cell database is then presented as well as the main electrical properties according to the dominant charge structures. For instance, the higher in altitude the dominant dipole, the higher the flash rate. Overall, dominant negative dipole are observed for 25% of the 10‐min periods and can be separated into two categories: (a) low altitude negative dipole class dominated by negative cloud‐to‐ground (CG) flashes with a main positive layer located between 2 and 4 km height and (b) high altitude negative dipole class, dominated by negative intracloud (IC) with a main positive layer at 5 km height. Dominant positive dipole can also be separated into two categories with (a) a dominant positive dipole located between 4.5 and 10 km high, −CG dominance, weak flash rate and (b) higher altitude dominant positive dipole, +IC dominance and a larger +CG fraction. The synergistic use of LMA and Météorage observations independently gives a rational type and polarity classification with regard to the vertical charge structure.

Influence of energy density on the microstructure and property regulation of additive manufactured pure nickel

In this work, laser powder bed fusion (LPBF) technology was used to fabricate pure nickel components, and the densification behavior and microstructure of pure nickel with different energy densities were investigated. The results indicate that for LPBF-fabricated pure nickel components, the relative density reaches a peak of 98.76% at an energy density of 101 J/mm3. With the increase of energy density, a large number of pores appear inside the grains, and the grains grow epitaxially along the building direction within multiple molten pools, pores gradually disappear after undergoing remelting at the edges of the melting tracks. Among these, competitive inward growth of columnar crystals may be the main cause of dislocations and new grain generation. The grains are primarily distributed along the Ni (111) or Ni (110) orientations, and with the increase of energy density, the grains with these two orientations increase. The surface energy follows the sequence of Ni (220) > Ni (200) > Ni (111). Due to the stacking of the <101> oriented main layer and the <001> oriented sub-layer in the building direction, the sample with higher energy density exhibits a strong Ni {110} texture, accompanied by increased tensile properties.

Research on Measurement Technology of Coefficient of Thermal Expansion of Honeycomb Structure

Honeycomb structure is widely used as the main sandwich layer of load-bearing structure in aviation, aerospace and other fields because of its light weight and high strength. Under some service conditions, the deformation of the honeycomb structure affected by high and low temperature will affect the overall mechanical performance of the structure. Therefore, accurate evaluation of the thermal expansion performance of the honeycomb structure becomes the key to structural design. However, the existing measurement methods of thermal expansion coefficient mainly have problems such as the hollow structure cannot be effectively contacted, the area of the grid optical target is limited and difficult to detect. In view of the above conditions, in this paper, a method for measuring the thermal expansion coefficient of honeycomb structure is proposed, and the measurement system is built, and the thermal expansion performance of honeycomb structure is effectively obtained by testing the honeycomb structure. At the same time, the measurement accuracy of the system is verified by using standard test pieces. The experimental results show that the method has high measurement accuracy.

Ultrastructural diversity in the shell of Artemia resting eggs.

The shell of Artemia resting egg, which is a delicate multilayered envelope surrounding the inside diapause embryo, plays an important role in the survival strategy of Artemia. To date, the ultrastructure of resting eggshell has been studied for only handful populations, and knowledge about the diversity of shell structure is still limited. In this paper, resting eggs from 13 Artemia populations were studied by transmission electron microscopy. Results show that the basic configuration of resting eggshell is quite conservative, but variations are not uncommon in the fine ultrastructure of each main layer of the shell (e.g., the shape and distribution of the radially oriented pores in the cortical layer; the size, number and arrangement of chambers in the alveolar layer; and the development state of outer cuticular membrane [OCM]). The ultrastructural variation of eggshell seems not to be linked with species and reproductive mode of Artemia. Resting eggs from very high habitats (4300+ m above sea level [a.s.l.]) on Qinghai-Tibet Plateau and certain tropical salterns have a hypoplastic OCM, which may be related to the adaptation to habitat conditions such as low oxygen concentration. RESEARCH HIGHLIGHTS: Comparative study on resting eggs from 13 Artemia populations reveals high diversity in the fine structure of eggshell. Resting eggs from very high (4300+ m a.s.l.) habitats commonly have a hypoplastic OCM.

Prediction of Heavy Metal Pollution in Soil Based on SSA-XGBoost Model and 3D Geological Model

ABSTRACT The problem of soil heavy metal pollution in decommissioned sites has become an environmental threat and challenge faced by countries around the world. Establishing a high-precision 3D model of contaminants is essential for pollution risk assessment and accurate monitoring of contaminated sites. In this study, 3D geological model and SSA-XGBoost model are proposed to predict heavy metal concentration in a site. These models can effectively improve the prediction accuracy of soil heavy metals, and the RMSE of the XGBoost model optimized by the SSA algorithm is reduced by 24.3%-34.3%. Compared with other machine learning models, the SSA-XGBoost model has optimal performance in improving the prediction accuracy of soil heavy metals. It is suitable for the areas with significant spatial heterogeneity of soil heavy metals. Using the SSA-XGBoost model and 3D geological model, the 3D spatial distribution characteristics of heavy metals in contaminated soil are determined. The concentration of soil pollutants ranked as As>Pb>Mo, and the overall pollution degree decreases gradually from top to bottom. The pollutants are mainly distributed in the production workshop area in the southwest of the site, and the miscellaneous fill layer is the main soil layer that needs to be remediated.

Evaluation of biophysical alterations in the epithelial and endothelial layer of patients with Bullous Keratopathy

The cornea is a fundamental ocular tissue for the sense of sight. Thanks to it, the refraction of two-thirds of light manages to participate in the visual process and protect against mechanical damage. Because it is transparent, avascular, and innervated, the cornea comprises five main layers: Epithelium, Bowman's layer, stroma, Descemet's membrane, and endothelium. Each layer plays a key role in the functionality and maintenance of ocular tissue, providing unique ultrastructural and biomechanical properties. Bullous Keratopathy (BK) is an endothelial dysfunction that leads to corneal edema, loss of visual acuity, epithelial blisters, and severe pain, among other symptoms. The corneal layers are subject to changes in their biophysical properties promoted by Keratopathy. In this context, the Atomic Force Microscopy (AFM) technique in air was used to investigate the anterior epithelial surface and the posterior endothelial surface, healthy and with BK, using a triangular silicone tip with a nominal spring constant of 0.4 N/m. Six human corneas (n = 6) samples were used for each analyzed group. Roughness data, calculated by third-order polynomial adjustment, adhesion, and Young's modulus, were obtained to serve as a comparison and identification of morphological and biomechanical changes possibly associated with the pathology, such as craters and in the epithelial layer and exposure of a fibrotic layer due to loss of the endothelial cell wall. Endothelial cell membrane area and volume data were calculated, obtaining a relevant comparison between the control and patient. Such results may provide new data on the physical properties of the ocular tissue to understand the physiology of the cornea when it has pathology.

Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort

PurposePeriodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration, in individuals with no eye disease. DesignCross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank. ParticipantsSixty-seven thousand three hundred eleven UK residents aged 40-70 years recruited between 2006-2010 underwent retinal imaging. MethodsMacular-centered optical coherence tomography images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery. Main Outcome MeasuresPhotoreceptor layer (PRL) and retinal pigment epithelium-Bruch’s membrane (RPE-BM) thicknesses. ResultsAmong 36,897 participants included in the analysis, 1,571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation and were more likely to be hypertensive, diabetic and current smokers (all p<0.001). On average, those with very severe periodontitis were myopic (-0.29 ± 2.40 diopters) while those unaffected were hyperopic (0.05 ± 2.27 diopters, p<0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (-0.55 μm, 95% CI: -0.97, -0.12, p=0.022) but there was no difference in RPE-BM thickness (0.00 μm, 95% CI: -0.12, 0.13, p=0.97). The association between PRL thickness and very severe periodontitis was modified by age (p<0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60-69 years with disease (-1.19 μm, 95% CI: -1.85, -0.53, p<0.001) but not among those under 60 years. ConclusionsAmong those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient age-related macular degeneration. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease.

A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms

During geomagnetic storms, which are a result of solar wind’s interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth’s surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its effects on transformers, a GIC monitoring system was created in 2011. The system consists of devices that are installed in the neutrals of autotransformers at five substations of the Kola–Karelian power transit in northwestern Russia. Considering the significant amount of data accumulated over 12 years of operating the GIC monitoring system, manual analysis becomes impractical. To analyze the constantly growing volume of recorded data effectively, a method for the automatic classification of GICs in autotransformer neutrals was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. The classifier’s performance is evaluated using accuracy and binary cross-entropy loss metrics. As the result of comparing four CNN architectures, a model that showed high GIC classification performance on the validation set was chosen as the final model. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer.

Geochemical Behavior of Shallow Buried Nodules from Clarion–Clipperton Fracture Zone in the East Pacific: A LA-ICP-MS Mapping Analysis Perspective

The Clarion–Clipperton Fracture Zone of the east Pacific contains numerous shallow buried nodules that are in direct contact with pore water in sediment, providing a direct reflection of the interaction between nodules and sediment. However, research on the geochemical behavior of these shallow-buried nodules is limited. This study used laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), high-resolution transmission electron microscopy (HRTEM), and X-ray diffraction (XRD) to compare mineral and element distribution in shallow buried nodules with surface nodules. The shallow buried nodules are products of nodules entering the burial stage. In comparison to surface nodules, shallowly buried nodules develop a fourth oxidized-suboxic diagenetic growth layer after entering the burial stage, in addition to the three main growth inner layers (L1, L2, L3). We suggest that L4 is not influenced by the bottom water source and that the presence of todorokite and the high flux of Mn2+ in the sediment pore water compete with other metal elements to enter the lattice of manganate, resulting in significantly higher Mn, W, and Li contents in L4 compared to L2. However, the content of Ni, Mg, and other hydrogenetic elements is much lower in L4 compared to L2. We suggest that the instantaneous change in surface primary productivity results in a sudden shift in the redox environment of the upper sediment layer. This reaction leads to the reduction of solid-phase Mn, providing growth opportunities for the buried nodules. Simultaneously, this may also be the reason why the growth layer of the nodules is jointly controlled by the sedimentary processes of hydrogenetic, oxic diagenetic, and suboxic diagenetic processes.

A deep learning approach based on multi-view consensus for SQL injection detection

SQL injection (SQLi) attacks are one of the oldest and most serious security threats, consistently ranking among the top ten critical web security risks. Traditional defense mechanisms against SQL injection predominantly use blacklists to disallow common injection characters or terms. However, the major challenge for these systems is to create a comprehensive list of potential SQLi characters, terms, and multi-terms that encompass various types of SQLi attacks (time-based, error-based, etc.), taking into account various SQL datasets (such as MySQL, Oracle, and NoSQL). Recently, some research studies have concentrated on feature learning from SQL queries by applying some well-known deep architectures to detect SQLi attacks. Motivated by a similar objective, this research introduces a novel deep learning-based SQLi detection system named “Bidirectional LSTM-CNN based on Multi-View Consensus” (MVC-BiCNN). The proposed method implements a pre-processing step that generates multiple views from SQL data by semantically encoding SQL statements into their corresponding SQL tags. By utilizing two different main layers, which are bidirectional long short-term memory (LSTM) and convolutional neural network (CNN), the proposed method learns a joint latent space from multi-view representations. In the detection phase, the proposed method yields separate predictions for each representation and assesses whether the query constitutes an SQLi attack based on a consensus function’s output. Moreover, Interpretable Model-Agnostic Annotations (LIME), one of the methods of Explainable Artificial Intelligence (XAI), is employed for the purpose of interpreting the model’s results and analyzing the SQL injection (SQLi) inputs. The experimental results demonstrate that MVC-BiCNN outperforms the baseline methods, yielding 99.96% detection rate.

FPGA-friendly compact and efficient AES-like 8 × 8 S-box

One of the main layers in the Advanced Encryption Standard (AES) is the substitution layer, where an 8 × 8 S-Box is used 16 times. The substitution layer provides confusion and makes the algorithm resistant to cryptanalysis techniques. Therefore, the security of the algorithm is also highly dependent on this layer. However, the cost of implementing 8 × 8 S-Box on FPGA platforms is considerably higher than other layers of the algorithm. Since S-Boxes are repeatedly used in the algorithm, the cost of the algorithm highly comes from the substitution layer. In 2005, Canright used different extension fields to represent AES S-Box to get FPGA-friendly compact designs. The best optimization proposed by Canright reduced the gate-area of the AES S-Box implementation by 20%.In this study, we use the same optimization methods that Canright used to optimize AES S-Box on hardware platforms. Our purpose is not to optimize AES S-Box; we aim to create another 8 × 8 S-Box which is strong and compact enough for FPGA platforms. We create an 8 × 8 S-Box using the inverse field operation as in the case of AES S-Box. We use another irreducible polynomial to represent the finite field and get an FPGA-friendly compact and efficient 8 × 8 S-Box. The finite field we propose provides the same level of security against cryptanalysis techniques with a 3.125% less gate-area on Virtex-7 and Artix-7 FPGAs compared to Canright’s results. Moreover, our proposed S-Box requires 11.76% less gate on Virtex-4 FPGAs. These gate-area improvements are beneficial for resource-constraint IoT devices and allow more copies of the S-Box for algorithm parallelism. Therefore, we claim that our proposed S-Box is more compact and efficient than AES S-Box. Cryptographers who need an 8 × 8 S-Box can use our proposed S-Box in their designs instead of AES S-Box with the same level of security but better efficiency.

On the magnetic field in Earth’s interior

This paper discusses the possible distribution of Earth’s magnetic field in its three main layers. As a first step, using the IGRF-13 model, it was found that the magnetic field energy exterior to the liquid outer core (i.e., inside the mantle and the surrounding Earth’s atmosphere) is about J (for epoch 2010) while the field strength exactly at the Core-Mantle-Boundary (CMB) is about 0.42 mT (RMS value). These findings can be further considered as reliable reference values among the large dispersion of published data for the total magnetic field in Earth’s interior. Therefore, utilizing the layer breakdown reported in a pioneering paper (Glatzmaier and Roberts [8]) according to which the inner core includes about 10% of the total energy content, it was made possible to validate a previously reported figure (year 1949: 9×1020 J) regarding the total magnetic field energy of the Earth. But due to substantial deviation of the latter from a later reported figure (year 1980: 7×1021 J), this paper continues the investigation for the purpose of clarifying the issue. In this context, two inverse-cubic dipole models and the bar magnet (tiny and cylindrical) model, as well as the IGRF-13 standard were applied and compared in the Earth’s interior. Overall, under certain conditions these findings are successfully compared with literature reports, and eventually (after many assumptions) reached an acceptable compromise regarding the spatial distribution of the magnetic field, while a stimulus for further reflection to other researchers was provided. At the end, a question is posed and somewhat answered whether the available magnetic field energy is abundant to feed the motion of futuristic lightweight electromagnetic vehicles revived in recent literature.

Introduction of new attributes: Scaling Exponent and its double derivatives for better land seismic volume interpretation

For the first time, we propose to study the behavior of the correlation (scaling) exponent and its second order with prestack seismic data. For our research, we have chosen Dibrugarh prestack 3D seismic data from the Assam-Arakan Basin, India. We have improved the calculation process so that it is possible to read amplitude values directly from SEG-Y file and collect data from the entire chosen in- or cross-line. As a result, we can now study significant amount of data within less amount of time. After completing the analysis, we could identify the main stratigraphic layers, basement, and the zones of weak and prominent amplitudes from the survey. Strong amplitude zones correlate with low scaling exponent values. Weak amplitudes reflect as high scaling exponent zones. New attributes introduced give a scope to automatize the interpretation of 3D seismic data without learning.

Липидный анализ слезной жидкости в диагностике дисфункции мейбомиевых желез

The tear film lipid layer plays a crucial role in maintaining ocular surface homeostasis by preventing excessive evaporation of the aqueous layer and reducing heat transfer from the corneal and conjunctival surface. The lipid composition of the tear film is unique and consists of two main layers: the outer (nonpolar lipids) and the inner (polar lipids). The thick outer layer of the tear film is composed of non-polar lipids that lack internal distributed charge, while the thin inner layer is formed by polar lipids with positive and negative charges at the ends of the molecule. Both types of lipids are synthesized in the meibomian glands (MG) and contribute to tear film stability. Meibomian gland dysfunction (MGD) alters the composition and physical properties of lipids, leading to pathological changes in both the MG and tear film. In meibomian gland dysfunction (MGD), the meibum thickens and acquires a higher melting point than normal. This leads to a decrease in the amount of nonpolar and amphiphilic lipids in the meibomian gland secretion and tear film. Consequently, the protective effect of the tear lipid layer is impaired, and its stability is compromised. Lipid mapping analyzes the lipid composition of biological samples, such as tear fluid, to study the mechanisms of the development and progression of various ocular surface diseases, including dry eye syndrome, MGD, and recurrent blepharoconjunctivitis. This method provides fundamental data on the properties of the tear film in various ocular surface diseases. It has the potential to establish hallmarks of disease course and develop personalized treatment approaches for specific clinical cases. KEYWORDS: tear, tear film lipids, ocular surface, meibomian gland dysfunction, lipid analysis. FOR CITATION: Takhauova L.R., Guseva A.A., Krylova A.A., Krivosheina O.I. Lipid analysis of tear fluid as a diagnostic tool for meibomian gland dysfunction. Russian Journal of Clinical Ophthalmology. 2024;24(2):83–87 (in Russ.). DOI: 10.32364/2311-7729-2024-24-2-7.

ПОБУДОВА ТА ПЕРШІ РЕЗУЛЬТАТИ ІНТЕРПРЕТАЦІЇ ТРИВИМІРНОЇ МОДЕЛІ ЩІЛЬНОСТІ МАНТІЇ ПІД УКРАЇНСЬКИМ ЩИТОМ

Background. Mantle density models are key tools for understanding fundamental geological and physical processes occurring within the Earth. Many parameters used in mantle density models remain poorly understood and undefined. Among others, these include data on the composition and rheology of the mantle, which can vary significantly. Methods. The method of creating density models (density) significantly influences the final result. Modeling with one-dimensional models simplifies the calculation process but generalizes the distribution of mantle density, assuming it is homogeneous in the horizontal direction. This limitation does not allow for the consideration of lateral variations in mantle density, which can be important at the regional level. In this study, we present a quasi-three-dimensional model of mantle density beneath the Ukrainian Shield, obtained on the basis of a set of one-dimensional density curves. Polynomial corrections for heterogeneity were applied during the calculations, compensating for the shortcomings of one-dimensional models. This three-dimensional model was derived by recalculating one-dimensional velocity curves obtained by seismic tomography for 21 mantle domains in the depth range of 50 to 2600 km. The process of transforming P-wave velocity curves into a density model includes the following steps: determining seismic boundaries in the mantle as points of inflection of the first derivative of P-wave velocity curves for each mantle domain; creating a synthetic S-wave mantle model beneath the Ukrainian Shield by recalculating P-wave velocity curves; solving the Adams-Williamson equation using seismic velocities (P, S) for each domain with subsequent polynomial correction to account for heterogeneity; selecting a reference mantle model that would serve as the basis for converting velocity curves into density through the comparison of gravitational potential on the Earth's surface and calculated values from existing reference mantle models (PREM, PREMA, PREMC, IASP91 AK135). The AK135 model was chosen as the reference model based on the comparison of calculated and observed gravitational potential at the central point of the Ukrainian Shield. This study focuses on the final stages of constructing the mantle density model, taking into account mass balancing of the upper and lower mantle for each domain when determining density using the Adams-Williamson equation and introducing polynomial corrections relative to the AK135 reference model; calculating densities for each of the 21 mantle domains and their three-dimensional integration. Results. In this study, we present a quasi-three-dimensional model of mantle density beneath the Ukrainian Shield, obtained on the basis of a set of one-dimensional density curves, with polynomial corrections for inhomogeneity incorporated into the calculations, compensating for the shortcomings of one-dimensional model calculations. This three-dimensional model was obtained by recalculating one-dimensional velocity curves obtained by the seismic tomography method for P-waves, calculated for 21 mantle domains in the depth range from 50 to 2600 km. Conclusions. This study focuses on the final stages of constructing the mantle density model, considering balancing the mass of the upper and lower mantle for each domain in determining density using the Adams-Williamson equation and introducing polynomial corrections relative to the AK135 reference model; calculating densities for each of the 21 mantle domains and their three-dimensional integration. The obtained mantle density model of the Ukrainian Shield is well aligned with the division of the mantle into three main layers: lithosphere, upper mantle, and lower mantle. Each of the structural layers has its own visual pattern of density heterogeneity.

Vulnerability in Inclusive Research: Exploring Co- and Professional Researchers’ Experiences in a Community-Based Participatory Project on the Disability Family

In the transition to a less protectionist research ethics paradigm—in which vulnerable groups are no longer excluded from participating in research—academic researchers need to think differently about vulnerability. By means of a collective autoethnographic investigation of professional and co-researcher’s experiences in a community-based participatory research (CBPR) project, this article explores how vulnerability is experienced and perceived in the work process and how to respond to vulnerability. It finds that vulnerability manifests in two main ways—that of feeling “emotional hurt” and “epistemic self-doubt”—and that it comes from two main layers: the lived life and from working within CBPR. The main argument in the article is that vulnerability is inevitable in qualitative research like CBPR, when involving persons in vulnerable life situations. We propose four key recommendations for future research: (a) accept vulnerability as an inevitable part of CBPR, (b) balance protection with participant autonomy in situ and together as a team, (c) use a processual approach because ethical risks in the research context might alter over time, and (d) accept that placing co-researchers at the center of interpretative authority can increase professional researcher’s vulnerability. The article expands existing understandings of ethical issues and risk in inclusive research through a combined and innovative focus on both professional and co-researcher’s lived experiences.