Mapping Method Research Articles

Pulp grade monitoring using binocular image through multi-scale feature cross-attention fusion network and saliency map constraint

Pulp grade is an important indicator for performance monitoring in froth flotation process. Previous studies have shown that the pulp grade can be predicted more accurately by using the stereo vision information of froth images. However, due to the low intra-class variation among froth images under the identical working conditions and the similarity in shape and texture among bubbles, it is challenging for current binocular image-based methods for grade prediction. Therefore, to accurately predict the key performance indicators in flotation process, a prediction model based on binocular image fusion is proposed in this paper. First, a calculation method of froth image saliency map is proposed, and the saliency map is used as a priori knowledge to guide the prediction model to learn the characteristics of the region of interest in the image. This measure aims to solve the problem of difficulty in grade prediction caused by low intra-class differences in froth images. Then, a multi-scale feature cross-attention fusion network is introduced, wherein the multi-scale features of the left and right views serve as attention mechanism gating signals to extract common feature from binocular image. After that, a pulp grade prediction model is developed based on Video Transformer Network. The results on actual industrial flotation datasets show that, compared with other pulp grade prediction methods, our proposed approach reduces the mean absolute error and root mean square error by 22.84% and 23.55%, respectively.

Grazing-sliding bifurcation in a dry-friction oscillator on a moving belt under periodic excitation.

In this paper, we consider the grazing-sliding bifurcations in a dry-friction oscillator on a moving belt under periodic excitation. The system is a nonlinear piecewise smooth system defined in two zones whose analytical expressions of the solutions are not available. Thus, we obtain conditions of the existence of grazing-sliding orbits numerically by the shooting method. Then, we compute the lower and higher order approximations of the stroboscopic Poincaré map, respectively, near the grazing-sliding bifurcation point by the method of local zero-time discontinuity mapping. The results of computing the bifurcation diagrams obtained by the lower and higher order maps, respectively, are compared with those from direct simulations of the original system. We find that there are big differences between the lower order map and the original system, while the higher order map can effectively reduce such disagreements. By using the higher order map and numerical simulations, we find that the system undergoes very complicated dynamical behaviors near the grazing-sliding bifurcation point, such as period-adding cascades and chaos.

The application of explainable artificial intelligence methods to models for automatic creativity assessment.

The study is devoted to comparing various models based on Artificial Intelligence to determine the level of creativity based on drawings performed using the Urban test, as well as analyzing the results of applying explainable artificial intelligence methods to a trained model to identify the most relevant features in drawings that influence the model's prediction. The dataset is represented by a set of 1,823 scanned forms of drawings of participants performed according to the Urban test. The test results of each participant were assessed by an expert. Preprocessed images were used for fine-tuning pre-trained models such as MobileNet, ResNet18, AlexNet, DenseNet, ResNext, EfficientNet, ViT with additional linear layers to predict the participant's score. Visualization of the areas that are of greatest importance from the point of view of the model was carried out using the Gradient-weighted Class Activation Mapping (Grad-CAM) method. Trained models based on MobileNet showed the highest prediction accuracy rate of 76%. The results of the application of explainable artificial intelligence demonstrated areas of interest that correlated with the criteria for expert assessment according to the Urban test. Analysis of erroneous predictions of the model in terms of interpretation of areas of interest made it possible to clarify the features of the drawing on which the model relies, contrary to the expert. The study demonstrated the possibility of using neural network methods for automated diagnosis of the level of creativity according to the Urban test based on the respondents' drawings. The application of explainable artificial intelligence methods to the trained model demonstrated the compliance of the identified activation zones with the rules of expert assessment according to the Urban test.

A Rose by Any Other Name: Mapping Taxonomic and Thematic Naming Errors Poststroke.

Understanding the neurobiology of semantic knowledge is a major goal of cognitive neuroscience. Taxonomic and thematic semantic knowledge are represented differently within the brain's conceptual networks, but the specific neural mechanisms remain unclear. Some neurobiological models propose that the anterior temporal lobe is an important hub for taxonomic knowledge, whereas the TPJ is especially involved in the representation of thematic knowledge. However, recent studies have provided divergent evidence. In this context, we investigated the neural correlates of taxonomic and thematic confrontation naming errors in 79 people with aphasia. We used three complementary lesion-symptom mapping (LSM) methods to investigate how structure and function in both spared and impaired brain regions relate to taxonomic and thematic naming errors. Voxel-based LSM mapped brain damage, activation-based LSM mapped BOLD signal in surviving tissue, and network-based LSM mapped white matter subnetwork integrity to error type. Voxel- and network-based lesion symptom mapping provided converging evidence that damage/disruption of the left mid-to-anterior temporal lobe was associated with a greater proportion of thematic naming errors. Activation-based lesion symptom mapping revealed that higher BOLD signal in the left anterior temporal lobe during an in-house naming task was associated with a greater proportion of taxonomic errors on the Philadelphia Naming Test administered outside of the scanner. A lower BOLD signal in the bilateral angular gyrus, precuneus, and right inferior frontal cortex was associated with a greater proportion of taxonomic errors. These findings provide novel evidence that damage to the anterior temporal lobe is especially related to thematic naming errors.

Deciphering the growth of digital payment adoption: an extensive SPAR-4 bibliometric exploration

Purpose This study aims to perform a detailed bibliometric analysis of digital payment adoption and map the trends and growth in this field by examining seminal works, conceptual foundations and intellectual networks. Design/methodology/approach The Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) technique was applied to retrieve 1,016 journal articles from Scopus and Web of Science (2003–2023). Diverse performance analyses and science mapping methods were performed using the Biblioshiny package in R to gain a comprehensive understanding of the field’s knowledge base. Findings The findings reveal a significant increase in scholarly interest, marked by a surge in publications and citations in recent years. Co-citation and factorial analysis identified distinct research clusters, delineating core themes and suggesting future research avenues relevant to theoretical exploration in digital payment technologies. Originality/value This study is a groundbreaking effort to explore the adoption of digital payment through bibliometric analysis. It uniquely addresses key domains for science mapping, including intellectual and conceptual structures. These innovative findings provide valuable insights for future practitioners, academics and aspiring researchers in the field.

Distinct regional vulnerability to Aβ and iron accumulation in post mortem AD brains.

The paramagnetic iron, diamagnetic amyloid beta (Aβ) plaques and their interaction are crucial in Alzheimer's disease (AD) pathogenesis, complicating non-invasive magnetic resonance imaging for prodromal AD detection. We used a state-of-the-art sub-voxel quantitative susceptibility mapping method to simultaneously measure Aβ and iron levels in post mortem human brains, validated by histology. Further transcriptomic analysis using Allen Human Brain Atlas elucidated the underlying biological processes. Regional increased paramagnetic and diamagnetic susceptibility were observed in medial prefrontal, medial parietal, and para-hippocampal cortices associated with iron deposition (R=0.836, p=0.003) and Aβ accumulation (R=0.853, p=0.002) in AD brains. Higher levels of gene expression relating to cell cycle, post-translational protein modifications, and cellular response to stress were observed. These findings provide quantitative insights into the variable vulnerability of cortical regions to higher levels of Aβ aggregation, iron overload, and subsequent neurodegeneration, indicating changes preceding clinical symptoms. The vulnerability of distinct brain regions to amyloid beta (Aβ) and iron accumulation varies. Histological validation was performed on stained sections of ex-vivo human brains. Regional variations in susceptibility were linked to gene expression profiles. Iron and Aβ levels in ex-vivo brains were simultaneously quantified.

Using Dynamic Stereo X-ray Imaging for In Vivo Acromioclavicular Joint Kinematics Assessment: A Preliminary Investigation.

Acromioclavicular joint (ACJ) disruption occurs frequently in athletes engaged in contact sports. However, the current understanding of ACJ biomechanics during muscle-driven functional activities and the influence of different treatment approaches (eg, reconstruction surgery vs nonoperative methods) on ACJ kinematics and stability remains limited. The absence of precise in vivo biomechanical measurement modalities for scapular and clavicular kinematics contributes significantly to this lack of understanding. The purposes of this study were to determine whether dynamic stereo x-ray (DSX) imaging can be used to evaluate the in vivo kinematics of the ACJ and to provide preliminary comparative data on ACJ kinematics, range of motion, and isometric strength of surgically reconstructed or nonoperatively treated ACJ shoulders and their uninjured contralateral shoulders. It was hypothesized that ACJ kinematics could be measured successfully using DSX and that surgically and nonoperatively treated shoulders would show abnormal 3-dimensional (3-D) ACJ kinematics compared with the uninjured contralateral. Controlled laboratory study. In this cross-sectional study, 11 participants who had undergone unilateral ACJ reconstruction surgery and 3 patients who received nonoperative treatment were enrolled. ACJ kinematics were assessed during active forward flexion, scaption, and abduction through high-speed DSX imaging, complemented by 3-D bone models obtained via computed tomography (CT) scans. To gauge kinematic differences, a 1-dimensional statistical parametric mapping method was employed, which compared outcomes in the index limb to those in the uninjured counterpart. In addition, the range of motion and isometric strength at various abduction angles were analyzed, employing a repeated-measures analysis of variance to compare the affected and uninjured sides. Leveraging a combination of DSX imaging and patient-specific CT bone models, ACJ kinematics was measured successfully during movements along anatomic planes. Preliminary findings from this investigation revealed no detectable differences between the surgically reconstructed and uninjured sides in ACJ biomechanics, shoulder range of motion, and isometric strength outcomes. However, on average, the nonoperatively treated shoulders demonstrated increased internal rotation, upward rotation, and posterior tilting of the scapula relative to the clavicle (no statistical analyses were performed due to the small sample size). DSX imaging is a promising tool for evaluating potential in vivo kinematic abnormalities in the ACJ during muscle-driven activities, laying the groundwork for further investigations in both ACJ-reconstructed and nonreconstructed patients. This study furnished essential data for conducting power analyses and designing future studies with an adequate sample size to investigate the impact of different treatment approaches on shoulder girdle mechanics. With its potential for accurately characterizing shoulder girdle kinematics post-ACJ injury, DSX imaging can offer valuable insights for future clinical studies, facilitating informed decisions regarding the short- and long-term impacts of treatment choices on shoulder health and function.

Identification of Candidate Immunodominant Epitopes and Their HLA-Binding Prediction on BK Polyomavirus Proteins in Healthy Donors.

BK polyomavirus infection is an important cause of graft loss in transplant patients, however, currently available therapies lack effectiveness against this pathogen. Identification of immunological targets for potential treatments is therefore necessary. The aim of this study was to predict candidates of immunodominant epitopes within four BK virus proteins (VP1, VP2, VP3 and LTA) using PBMCs from 44 healthy donors. We used the ELISpot epitope mapping method to evaluate the T-cell response, and HLA-peptide binding was predicted using the NetMHCpan algorithm. A total of 11 potential peptides were selected for VP1, 3 for VP2/VP3 and 13 for LTA. Greater reactivity was observed for VP1 and LTA proteins compared with VP2/VP3. Most of the peptides selected as potential immunodominant candidates were restricted towards several HLA class I and II alleles, with predominant HLA class I binding by computational predictions. Based on these findings, the sequences of the selected immunodominant epitopes candidates and their corresponding HLA restrictions could contribute to the optimisation of functional assays and aid in the design and improvement of immunotherapy strategies against BK virus infections.

A knowledge graph-enabled multi-domain mapping approach supporting product rapid design: A case study of new energy vehicles

In the context of mass personalization, the dynamic and erratic nature of customer requirements (CRs) is increasingly pronounced. Amidst intensifying market competition, enterprises are compelled to swiftly adjust their products to satisfy the ever-changing CRs. However, the complicated design processes often result in prolonged product update and iteration cycles. Thus, it is urgent for enterprises to promote product rapid design, in which, it is essential to map CRs to functional requirements (FRs), then to design parameters (DPs). Traditional mapping methods are time-consuming and labor-intensive, struggle to keep pace with the ever-changing CRs. Moreover, enhancing the efficiency of design knowledge management and reuse is imperative for achieving product rapid design. Considering the strengths of knowledge graphs (KGs) in knowledge management and mining, we establish a multi-domain product design KG (MPDKG) including a CR domain, a FR domain, and a DP domain, and facilitate the mapping process by constructing mapping relations between domains. Firstly, CR entities in the CR domain are extracted through a triple-task cascaded CRs identification model from online reviews and complaints. Secondly, FR entities are identified from product functional lists and instructions, and the Sentence-Bert model is employed to establish the mapping relations between CR entities and FR entities. Subsequently, a corpus comprising product design knowledge is constructed and a cross-domain joint entity relation extraction model is developed to extract DP entities and construct their mapping relations with FR entities. Lastly, the multi-domain mapping of BYD Qin is implemented as a case study to elucidate the practical utility and effectiveness of the proposed methodology.

Integrating time series Sentinel-2 images and tide height to mapping tidal flats in the Chinese mainland

As a unique and important ecosystem, tidal flats provide a variety of ecosystem functions and services. Mapping tidal flats is essential for the protection and management of coastal ecosystems. However, large-scale tidal flats mapping still faces challenges due to the tidal variation and spectral similarity between tidal flats and inland wetlands. Previous methods rely on the coastlines or maximum seawater extent to exclude inland areas, which is limited by its inability to effectively differentiate tidal flats from spectrally similar inland wetlands. To address these issues, we proposed a new tidal-flat mapping method by integrating Sentinel-2 time series imagery with tide height (TH) data from ground-based tide stations on Google Earth Engine. We first generated images at the lowest and highest tidal stages, established the statistical relationship between the Normalized Difference Water Index (NDWI) of each pixel and TH, and then concatenated them into a Random Forest classifier for further classification. The statistical relationship between NDWI and TH amplified the difference between tidal flats and inland wetlands, thus significantly reducing the influence of spectral similarity. This method could produce a high-precision tidal flats map with an overall accuracy of 97.30% in the coastal zone of the Chinese mainland. By quantitatively comparing with the previous tidal flat maps, we found that the strategies of tidal-level information simulation and inland area exclusion were the two main reasons producing the differences among the maps. The proposed method does not rely on space constraints to exclude inland wetlands and can capture more estuarine tidal flats, so it can be used as a reliable means to monitor the tidal flats in large-scale areas.

Layout optimization of pore microstructure in fluid-saturated porous media using Galerkin decoupling technology and independent continuous mapping method (GDT-ICM)

A layout optimal design method combining Galerkin decoupling technology and independent continuous mapping method (GDT-ICM) is proposed to rearrange the layout of pore microstructure. Firstly, the Galerkin decoupling technology (GDT) is employed to solve the Brinkman equation for fluid flow in fluid-saturated porous media, which integrates the Stokes equation and Darcy's law. Within this framework a self-programmable element balance equation is developed. Secondly, a permeability interpolation function is defined for each element, incorporating the permeability of the fluid and pore microstructure. This function distinguishes strictly between the fluid domain and the pore domain. Pore microstructures with arbitrary shape including permeability information are established in an Euler mesh by introducing the Carman-Kozeny equation, shape functions, the Joukowski mapping and filter functions (the modified arctan mapping function and the ‘inpolygon’ function) based on the ICM method. Thirdly, a layout optimization model aiming at minimizing energy dissipation is established to accomplish the optimal distribution of pore microstructures. A sensitivity analysis is calculated with respect to design variables and the optimal model is solved by a genetic algorithm. Numerical results demonstrate that the proposed method is effective and feasible in 2D-space. The maximum velocity within the fluid field is reduced by 45%, and the issue of localized high pressure occurring around the pore is solved. This paper provides guidance for solving the Brinkman coupled equation and the layout optimization of the microstructure in porous media.

Data-Driven Model-Free Adaptive Containment Control for Uncertain Rehabilitation Exoskeleton Robots with Input Constraints

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model.

New Processing Technique of Jacobian Elliptic Equation and Its Application to the (3+1)-Dimensional Modified Korteweg de Vries–Zakharov–Kuznetsov Equation

This article introduces two kinds of processing techniques to solve Jacobian elliptic equations and obtain rich periodic wave solutions. Then, the equation was used as an auxiliary equation to solve the (3+1)-dimensional modified Korteweg de Vries–Zakharov–Kuznetsov (mKDV-ZK) equation. Combined with the mapping method, a large number of new types of exact periodic wave solutions were obtained, many of which were rarely found in previous research. Numerical simulations have demonstrated the evolution of various periodic waves in (3+1)-dimensional mKDV-ZK. The solutions and wave phenomena obtained in this article will help expand our understanding of the equation.

“My Japanese is Blue, Because it Makes Me Blue”: Centering Emotion in Language Practices Through Language Mapping

Since the social turn in applied linguistics, there has been growing interest in the role of emotion in language practices. This role is especially relevant to self-access language learning in terms of how it influences learner autonomy and motivation. With its focus on autonomy and sociality, self-access learning offers unique affordances for facilitating learner and advisor awareness of emotion in language learning. To this end, this study used an arts-based method, language mapping, for learners to express their language practices in multimodal ways. Language mapping integrates body mapping and language portraits methods to catalyze individual and group reflection anchored by embodied, emotional experiences. The researchers collaborated with a Japanese university self-access language learning center to engage seven volunteer participants in a series of two workshops. Data were gathered through participants’ language maps, narratives shared in workshops, and questionnaire responses. Findings highlight the potential of language mapping in exploring learners’ affective connections to their named languages. While mother tongues were generally portrayed as comforting, vivid, default, and unconscious, relationships to learned languages were represented through more diverse visual and verbal metaphors. Key pedagogical considerations include the impact of exemplars and group processes.

Point-of-care ultrasound of the inflammatory bowel disease

Background. The widespread use of portable ultrasound scanners has advanced the concept of ultrasound diagnosis (POCUS), namely «ultrasound examination (US) is performed at the bedside and is interpreted directly by a physician». Pocus is not a substitute for complex ultrasound, but rather allows the ultrasound doctor to quickly access clinical images for rapid diagnosis and effective examination and treatment of patients. Purpose – the work to identify ultrasound signs of inflammatory bowel disease (IBD) using POCUS. Materials and Methods. 70 patients aged 18 to 39 years (24 men and 46 women, mean age – 30.3 ± 13.7 years). The first group of patients included 32 people with a morphologically confirmed CD diagnosis, the second group included 38 people with a morphologically confirmed UC diagnosis. The control group consisted of 2 8 patients with no clinical and laboratory data on gastrointestinal lesions. Results. It was shown that the ultrasound can assess the thickness of the intestinal wall with sensitivity – 90.3% and specificity – 87.1% and ROC – 0.882, differentiation of the wall layers, surrounding structures (omentum, mesentery, lymph nodes), as well as the localization of the affected intestinal segment. Color Doppler Mapping (CDM) method with a sensitivity of 86.5% and specificity of 81.5% and ROC of 0.852 allows not only to assume the presence of active inflammation in the wall of the affected intestinal segment, but also to monitor the dynamics of the process during treatment. Conclusion. In this work, the capabilities of POCUS for the diagnosis of IBD were evaluated. Correct and accurate interpretation of POCUS findings is not only an important diagnostic step, but also a complement to other radiation and endoscopic imaging techniques. The main advantage of POCUS over CT and MRI is its fast availability, low cost, and high level of safety. An experienced ultrasound doctor is needed to examine the gastrointestinal tract, so training with ultrasound doctors and mastering practical skills is key to ensuring the effective use of POCUS.

“My Japanese is Blue, Because it Makes Me Blue”: Centering Emotion in Language Practices Through Language Mapping

Since the social turn in applied linguistics, there has been growing interest in the role of emotion in language practices. This role is especially relevant to self-access language learning in terms of how it influences learner autonomy and motivation. With its focus on autonomy and sociality, self-access learning offers unique affordances for facilitating learner and advisor awareness of emotion in language learning. To this end, this study used an arts-based method, language mapping, for learners to express their language practices in multimodal ways. Language mapping integrates body mapping and language portraits methods to catalyze individual and group reflection anchored by embodied, emotional experiences. The researchers collaborated with a Japanese university self-access language learning center to engage seven volunteer participants in a series of two workshops. Data were gathered through participants’ language maps, narratives shared in workshops, and questionnaire responses. Findings highlight the potential of language mapping in exploring learners’ affective connections to their named languages. While mother tongues were generally portrayed as comforting, vivid, default, and unconscious, relationships to learned languages were represented through more diverse visual and verbal metaphors. Key pedagogical considerations include the impact of exemplars and group processes.

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

Rectifying Discrepancies Between GB-SAR Images and Terrain Model Caused by Measurement Deviations in Open-Pit Mines

Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR) is a valuable technique for monitoring deformation of landslides. GB-InSAR can construct high-resolution 2D images in a range-doppler plane. However, it is difficult for geo-engineers who are unfamiliar with radar monitoring geometry to interpret the results. Geometric mapping method has been applied to the co-registration of terrain models and radar images for the deformation zonation, but the mismatching correction with GB-InSAR and terrain model is not fully investigated by existing researches. To address this issue, this paper proposes a method exploiting an optimum linear transformation based on ground control points (GCPs). The proposed method was evaluated on simulated data and a field campaign in an open pit mine. The deformation mapping result of the method was verified by a collapse event. The method proposed in this paper has an RMSE value ranging from 0.502 to 0.720[Formula: see text]m (@700[Formula: see text]m average monitoring range) when the average density of the terrain point cloud is 0.5[Formula: see text]m. Although this method cannot accurately evaluate the antenna footprint vector deflection parameters, it can meet the needs of coarse matching calibration in relatively flat slope sub-target areas of open-pit mines for engineering applications.

Ідентифікація координат імпульсних джерел забруднень стічних вод в кусково-однорідних середовищах числовими методами квазіконформних відображень

The process of filtration in a single-connected curvilinear domain bounded by streamlines and equipotential lines is considered, provided that the medium under study is piecewise homogeneous. It is assumed that certain unknown curves act as impulse sources of pollution. It is assumed that their propagation occurs only due to the convective component, without significantly affecting the filtration background. It is proposed to use the method of characteristics for solving the convection equation to identify the coordinates of pollution sources. In this case, quasipotentials at the fluid inlet and outlet at the boundary of the domain, coordinates of the points of pollution detection, and the time of its movement downstream can serve as a priori data. The general algorithm involves the adaptation of the numerical quasiconformal mapping method to build a hydrodynamic mesh, according to which the coordinates of pollution sources are identified. Numerical experiments were carried out and analysed. In particular, it is emphasised that with a sufficient mesh division, the maximum discrepancies between the a priori known data and the calculated data are small compared to the size of the studied domain. This indicates the effectiveness of the developed algorithm for identifying pollution sources in the case of a piecewise homogeneous environment. As an additional measure to reduce the magnitude of the uncertainties, it is proposed to use more accurate approximation schemes for specific expressions. On the other hand, there is an increase in computational complexity compared to the case of a continuous setting of the filtration coefficient. Given the relatively high accuracy of the calculations, it seems advisable to further develop an described approach to larger-scale in comparison with point sources of pollution and to spatial case. Taking into account the sensitivity of the solutions to the discontinuity of the filtration coefficient values, it is also worthwhile to introduce additional conditions at the contact of homogeneous media in the future.