Hypothetical Data Research Articles

On remedying the presence of heteroscedasticity in a multiple linear regression modelling

This study demonstrated the very essence of remedying the presence of heteroscedasticity, where it existed, in regression modelling. Two different hypothetical data, Data A (the Original) and Data B (the Original), were used in this study for the purpose of illustration. The normality, multicollinearity and autocorrelation assumptions were satisfied, but the Breusch-Pagan test and the White test established the existence of heteroscedasticity in the two datasets. The estimated multiple linear regression model for Data A (the Original) was statistically significant with an R-square value of 0.976, an AIC value of 332.5929, and an SBC value of 347.2533; and the one for Data B (the Original) was also statistically significant with an R-square value of 0.553, an AIC value of 69.89669, and an SBC value of 82.15499. The Log-transformation was applied on the variables in Data A (the Original) and Data B (the Original) to give rise to new sets of data, Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied); which equally satisfied the normality, multicollinearity and autocorrelation assumptions, and also satisfied that there were no existence of heteroscedasticity in the two datasets. Now, the estimated multiple linear regression model for Data A (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.986, an AIC value of -135.021, and an SBC value of -120.361; and the estimated model for Data B (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.624, an AIC value of -32.0801, and an SBC value of -19.8218. From the points of view of the values of the R-square (0.986>0.976 and 0.624>0.553), AIC (-135.021<332.5929 and -32.0801<69.89669) and SBC (-120.361<347.2533 and -19.8218<82.15499), it was evident that the estimated regression models for Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied) were, respectively, better models when compared to the regression models for Data A (the Original) and Data B (the Original).

Insights from an Experiment Crowdsourcing Data from Thousands of US Amazon Users: The importance of transparency, money, and data use

Data generated by users on digital platforms are a crucial resource for advocates and researchers interested in uncovering digital inequities, auditing algorithms, and understanding human behavior. Yet data access is often restricted. How can researchers both effectively and ethically collect user data? This paper shares an innovative approach to crowdsourcing user data to collect otherwise inaccessible Amazon purchase histories, spanning 5 years, from more than 5,000 U.S. users. We developed a data collection tool that prioritizes participant consent and includes an experimental study design. The design allows us to study multiple important aspects of privacy perception and user data sharing behavior, including how socio-demographics, monetary incentives and transparency can impact share rates. Experiment results (N=6,325) reveal both monetary incentives and transparency can significantly increase data sharing. Age, race, education, and gender also played a role, where female and less-educated participants were more likely to share. Our study design enables a unique empirical evaluation of the 'privacy paradox', where users claim to value their privacy more than they do in practice. We set up both real and hypothetical data sharing scenarios and find measurable similarities and differences in share rates across these contexts. For example, increasing monetary incentives had a 6 times higher impact on share rates in real scenarios. In addition, we study participants' opinions on how data should be used by various third parties, again finding that gender, age, education, and race have a significant impact. Notably, the majority of participants disapproved of government agencies using purchase data yet the majority approved of use by researchers. Overall, our findings highlight the critical role that transparency, incentive design, and user demographics play in ethical data collection practices, and provide guidance for future researchers seeking to crowdsource user generated data.

Investigating the Impact of Cloud Computing on Environmental Sustainability

Abstract: It discusses the effects of cloud computing on the environmental aspect of sustainability by focusing specifically on carbon emission reduction, cost-cutting, and exploiting renewable sources of energy. Using only hypothetical data obtained from TechCorp Solutions, Global Data Inc., InnovaTech Ltd., and NextGen Systems, this paper reveals strong decreases in energy usage - between 38.89 percent to 45 percent in cost savings following the implementation of cloud computing. The study also records remarkable carbon emission reductions. The cuts in drops range between 42.86% and 45%. Not only this, but on implementation, every firm asserted a flat rate cut of 40% in operating costs. The second investment area the research probes is the commitment of the major cloud providers to renewable energy sources. It finds some using up to 100% from renewable sources. Cloud data centers are much more efficient than traditional data centers, as the PUE comparison indicates, and hence produce maximum energy efficiency with the least negative impact on the environment. The results represent important implications regarding cloud computing in light of sustainability goals and revenue generation. This implies that cloud technology will be used much more widely within various industries going forward.

Accessible Trail Tourism: Trail Accessibility and Difficulty Rating Approach Designed for Individuals, Including With Mobility Impairments

ABSTRACTThis study introduces a novel approach to trail accessibility and difficulty rating tailored specifically for individuals, including those with mobility impairments, aiming to promote accessible trail tourism. Grounded in theoretical frameworks of inclusive outdoor recreation, equality, and complex adaptive systems, the proposed rating system incorporates criteria addressing physical access, terrain challenges, and amenities to accommodate diverse user needs. This study employed a sensor technology, called wheeled instrumentation sensor package (WISP) within the framework of the high‐efficiency trail assessment process (HETAP) to develop a new quantitatively based trail classification scheme. A case study trail and two hypothetical synthetic data trails were used to demonstrate both the trail wheelchair accessibility binary classification approach and the proposed trail difficulty classification approach. This research pioneers trail accessibility and difficulty rating and underscores the importance of integrating accessibility considerations into trail management practices to create truly inclusive outdoor recreational experiences.

Extracting metal oxide redox thermodynamics from TGA measurements requires moving beyond the linearized van ‘t Hoff approach

Thermodynamic modeling of metal oxide reduction is crucial for optimizing chemical processes and materials in systems dependent on off-stoichiometric reduction/re-oxidation cycling. Two prevalent methods for extracting reduction thermodynamics from thermogravimetric data are linearized van ‘t Hoff (VH) analysis and the compound energy formalism (CEF). This work evaluates the accuracy of these methods by constructing invertible ground truth thermodynamic models, generating hypothetical thermogravimetric data, and determining the reduction thermodynamic using both VH and CEF methods. Our findings reveal that the VH method produces absolute errors 3–5 times higher than the CEF in kJ/mol O or J/mol O K for enthalpy and entropy of reduction, respectively. In contrast, the CrossFit CEF (CF-CEF) method yields errors often less than 10 kJ/mol O or J/mol O K. Moreover, the CF-CEF method provides models based on mole fraction, temperature, and extent of reduction, while a typical VH analysis provides thermodynamics of only the specific compositions measured. Although simple to implement, the VH method suffers from significant, non-systematic errors due to entropy/enthalpy compensation and defect modeling. Consequently, we recommend the more complex but robust, CF-CEF method for extracting redox thermodynamics from thermogravimetric measurements.

Efficient approach of painting asteroids for planetary defense using network data envelopment analysis

The potential dangers of asteroids hitting the Earth are one of the challenges for mankind to preserve the human species. Various missions have been proposed to defend asteroids. Changing the Albedo magnitude by painting asteroids is a suitable and low-cost idea for the asteroid defense plan. Moving away from the Earth requires fuel consumption and the limitations of carrying the necessary materials for painting. Due to these limitations, we are not able to paint the entire asteroid, or for other reasons, we have to paint parts of the asteroid. Therefore, we should find a relative compromise between the variables to get most efficiency from painting parts of the asteroid. In this paper, we present a network structure of Data Envelopment Analysis (DEA) and consider the relationships of all variables including physical and orbital characteristics, spacecraft limitations, effective solar mass, albedo change rate, and elapsed time of the mission. We look for parts of the asteroid’s surface where we can get the most displacement by spending the least amount of raw material. One of the advantages of the proposed method is the capability of sensitivity analysis to any of the variables affecting the magnitude of the final displacement. The theorems related to the stability were proven analytically, and we show how to reduce the elapsed time by maintaining efficiency in asteroid painting. The performance of the proposed model was analyzed using real and hypothetical data from asteroid Apophis 99942.

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

This study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

Three-step kinetic model for fisetin dye diffusion into fibroin fibre

BackgroundThe study’s relevance is determined by the current desire to reduce the negative environmental impact of the textile industry. The study aims to develop and optimise dyeing processes using natural dyes in the textile industry.ResultsThe process of dye transfer from solution to Bombyx mori natural silk fibre can be divided into three successive kinetic stages. The first stage involves the adsorption of dye molecules on the active surface of the fibre, the second, their diffusion deep into the fibre towards its centre, and the third, the uniform distribution of dye molecules along the fibre starting from its centre. It is noticed that diffusion at the third stage slows down significantly, and the third stage lasts much longer than the first and second stages. The analysis of experimental data on dye concentration over time on dyed materials and their comparison with hypothetical data will make it possible to establish time intervals for each stage of the process and diffusion coefficients for each of them.ConclusionThis study has practical implications as it may contribute to more efficient and sustainable dyeing of textile materials using natural dyes, helping to reduce the negative environmental impact of the textile industry, and contributing to our knowledge of diffusion and dyeing processes.

On Remedying the Presence of Heteroscedasticity in a Multiple Linear Regression Modelling

This study demonstrated the very essence of remedying the presence of heteroscedasticity, where it existed, in regression modelling. Two different hypothetical data, Data A (the Original) and Data B (the Original), were used in this study for the purpose of illustration. The normality, multicollinearity and autocorrelation assumptions were satisfied, but the Breusch-Pagan test and the White test established the existence of heteroscedasticity in the two datasets. The estimated multiple linear regression model for Data A (the Original) was statistically significant with an R-square value of 0.976, an AIC value of 332.5929, and an SBC value of 347.2533; and the one for Data B (the Original) was also statistically significant with an R-square value of 0.553, an AIC value of 69.89669, and an SBC value of 82.15499. The Log-transformation was applied on the variables in Data A (the Original) and Data B (the Original) to give rise to new sets of data, Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied); which equally satisfied the normality, multicollinearity and autocorrelation assumptions, and also satisfied that there were no existences of heteroscedasticity in the two datasets. Now, the estimated multiple linear regression model for Data A (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.986, an AIC value of -135.021, and an SBC value of -120.361; and the estimated model for Data B (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.624, an AIC value of -32.0801, and an SBC value of -19.8218. From the points of view of the values of the R-square (0.986>0.976 and 0.624>0.553), AIC (-135.021<332.5929 and -32.0801<69.89669) and SBC (-120.361<347.2533 and -19.8218<82.15499), it was evident that the estimated regression models for Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied) were, respectively, better models when compared to the regression models for Data A (the Original) and Data B (the Original).

Travel bubble policies for low‐risk air transport recovery during pandemics

AbstractGlobal pandemics restrict long‐haul mobility and international trade. To restore air traffic, a policy named “travel bubble” was implemented during the recent COVID‐19 pandemic, which seeks to re‐establish air connections among specific countries by permitting unrestricted passenger travel without mandatory quarantine upon arrival. However, travel bubbles are prone to bursting for safety reasons, and how to develop an effective restoration plan through travel bubbles is under‐explored. Thus, it is vital to learn from COVID‐19 and develop a formal framework for implementing travel bubble therapy for future public health emergencies. This article conducts an analytical investigation of the air travel bubble problem from a network design standpoint. First, a link‐based network design problem is established with the goal of minimizing the total infection risk during air travel. Then, based on the relationship between origin‐destination pairs and international candidate links, the model is reformulated into a path‐based one. A Lagrangian relaxation‐based solution framework is proposed to determine the optimal restored international air routes and assign the traffic flow. Finally, computational experiments on both hypothetical data and real‐world cases are conducted to examine the algorithm's performance. The results demonstrate the effectiveness and efficiency of the proposed model and algorithm. In addition, compared to a benchmark strategy, it is found that the infection risk under the proposed travel bubble strategy can be reduced by up to 45.2%. More importantly, this work provides practical insights into developing pandemic‐induced air transport recovery schemes for both policymakers and aviation operations regulators.

Methodology for estimating regional production capacity loss rate in industrial sectors caused by disasters: A case study of the 2016 Kumamoto earthquakes

The estimation of regional Production Capacity Loss Rate (PCLR) in industrial sectors after disasters is critical for disaster mitigation, resource allocation and restoration. Recent studies to estimate the PCLR in industrial sectors were based on hypothetical disaster survey data for resistance analysis and the linear regression model for recovery analysis, which may cause non-negligible bias. Moreover, these models did not incorporate the impact of lifeline disruption and restoration, thus further amplifying the estimation bias. To fill this gap, this study proposes a comprehensive regional PCLR estimation framework that incorporates the impacts of lifeline disruptions into resistance analysis and lifeline restoration into recovery analysis to improve the PCLR estimation accuracy. Specifically, this study utilizes lifeline resilience factors (LRFs) to quantify the impact of lifeline disruption on the production capacity rate immediately after the earthquake, and apply a semi-Markov recovery function to model the recovery rates considering the initial damage and lifeline availability influences. Then, the proposed methodology is applied to a case study of 2016 Kumamoto earthquakes to verify its effectiveness and accuracy. In this case study. Results shows that the non-manufacturing sector is more resistant than the manufacturing sector, yet lower recovery ability, ultimately resulting in greater production losses. The Mean Square Errors between the estimated PCLR and the official statistical of Kumamoto prefecture's Index of Industrial Production in each industry are less than 0.05, proves the validity of the proposed model and indicates the improved accuracy of loss estimation compared to previous studies. In addition, comparison results indicate improved accuracy of loss estimation compared to previous studies.

Optimization of implementation strategies using the Multiphase Optimization STratgey (MOST) framework: Practical guidance using the factorial design.

The Multiphase Optimization STrategy (MOST) is a framework that uses three phases-preparation, optimization, and evaluation-to develop multicomponent interventions that achieve intervention EASE by strategically balancing Effectiveness, Affordability, Scalability, and Efficiency. In implementation science, optimization of the intervention requires focus on the implementation strategies-things that we do to deliver the intervention-and implementation outcomes. MOST has been primarily used to optimize the components of the intervention related to behavioral or health outcomes. However, innovative opportunities to optimize discrete (i.e. single strategy) and multifaceted (i.e. multiple strategies) implementation strategies exist and can be done independently, or in conjunction with, intervention optimization. This article details four scenarios where the MOST framework and the factorial design can be used in the optimization of implementation strategies: (i) the development of new multifaceted implementation strategies; (ii) evaluating interactions between program components and a discrete or multifaceted implementation strategies; (iii) evaluating the independent effects of several discrete strategies that have been previously evaluated as a multifaceted implementation strategy; and (iv) modification of a discrete or multifaceted implementation strategy for the local context. We supply hypothetical school-based physical activity examples to illustrate these four scenarios, and we provide hypothetical data that can help readers make informed decisions derived from their trial data. This manuscript offers a blueprint for implementation scientists such that not only is the field using MOST to optimize the effectiveness of an intervention on a behavioral or health outcome, but also that the implementation of that intervention is optimized.

An approach for estimating microbial growth and chlorinated ethenes biotransformation in groundwater based on discrete observations

The bioremediation of chlorinated ethenes (CEs) contaminated groundwater is attracting increasingly attention in practical remediation projects. However, modelling of microbial metabolic processes under the constraints of substrate and environmental factors is inadequate. This study developed a new kinetic model, which incorporated the logistic model and Dual-Monod kinetic to represent the interaction between the controlled microbial growth and the bioavailable substrates in CE-contaminated groundwater. The proposed model was based on discrete observations to simulate microbial growth under the constraints of substrate and environmental conditions, reducing the amount of observational data required for the model. Meanwhile, the proposed model introduced two new kinetic parameters, the effective specific growth rate μeff and the real self-limiting coefficient of microbial growth keff,sl, to simplified the number of independent parameters. A parameter estimation method based on the quasi-Newton's algorithm for the proposed model was also developed. The model was validated based on the hypothetical data, experimental results, and a published dataset, demonstrated the successful simulation of microbial growth and the sequential biodegradation of PCE in groundwater systems (*E < 0.3). The monitoring duration and the sampling schedule have significant impacts on estimating the biological parameters, and large errors would be induced when the data from the periods of extremely low substrate concentration or microbial growth decline were involved in parameter estimation. The research suggested that the proposed kinetic model provided a new insight to express the limitation of microbial population growth due to the available substrates and environmental factors, and is hoping to be applied in actual CE-contaminated sites.

Dynamic content layout optimization for news website front pages

Purpose The home page design of a digital news website is a key factor in determining its attractiveness to readers. This study aims to propose an approach to manage the frequent adjustment of the dynamic layout of the news content on the website home page in a real-time environment to increase its attractiveness to readers. Design/methodology/approach This paper shows that this news website layout design problem can be modeled as an optimization problem based on the information of news contents that change within a multiple-period planning horizon similar to the dynamic facility layout problem. A hybrid genetic algorithm-based approach integrated with local search heuristic methods is also proposed to improve the solution. Findings This paper finds that the DPLP model is effective in modeling the changing layout of a digital news website. The problem can solved in a timely manner using the proposed hybrid genetic algorithm. Research limitations/implications This paper was based on hypothetical data and on the assumption of equal section size. Actual data would help fine-tune the application of the dynamic facility layout model. As well the algorithm could be enhanced for unequal size sections. Practical implications The model should help online newspapers apply sophisticated algorithms to optimize the layout of news websites dynamically in a timely manner. Social implications News websites are increasingly the desired medium to consume news. So it has an important role in educating society. Thus optimizing and improving the process will help in this regard. Originality/value To the best of the authors’ knowledge, this paper is the first one to apply the DPLP model to the digital newspaper website dynamic design problem.

Data envelopment analysis for identification of bank branches appropriate locations in terms of their efficient operation

One of the main banking activities is the branch network. It provides the effective banking, the profitability. Choosing the right location of branches is very important for the bank effective operation. Random or intuitively locations can have a negative impact on key performance indicators in the future. The correct and thoughtful choice of the bank branch location ultimately determines its maximum efficiency. The paper concerns with the determining of the bank branch location in terms of thorough mathematical calculation and many factors influencing bank activity. We developed a special calculation method using the DEA (Data Envelope Analysis) methodology, considering financial, branch location, location features, etc. The paper dwells on the issue of one large branch effectiveness comparing to several smaller branches covering a wider consumer area on the example of the branch on A. Sharifzade street, Baku, Azerbaijan. Indeed, it is important to consider the location in terms of its geolocation and landscape. However, one of the main issues is expediency of establishing one large or several small branches in densely populated areas. Moreover, it is necessary to take into account the infrastructure to improve the bank performance. To consider this issue, we use both hypothetical and statistical data of Capital Bank. It is one of the largest banks in Azerbaijan. Also it has a large network of branches. The article examines expediency of establishing one large or several small branches in the area close to the existing branch of Capital Bank on A. Sharifzade street. The efficiency of the current operating branch was compared with the total efficiency of virtual branches in several combinations. We analysed the indicators of the Inshaatchylar branch and virtual branches in different combinations in different locations in accordance with DEA CRS (Constant Return to Scale) method. Additionally, we determined the effectiveness of both the Inshaatchylar branch and virtual branches in various combinations. Thus, the highest score is the most effective one.

Dynamic open innovation to determine technology-based interoperability requirement for electric motorcycle swappable battery

Electric motorcycle swappable batteries (EMSB) require interoperability standards so that swappable batteries (SB) can be exchanged and operated between brands to minimize investment costs. The stages in standards development have similar characteristics to the open innovation process, namely the involvement of external interested parties: manufacturers, regulators, customers, suppliers, experts, and researchers. For that, in the standards development process, all stakeholders must collaborate to leverage knowledge, expertise, resources, and innovative ideas. This article, therefore, aims to determine the essential parameters that need to be established in interoperability requirements and develop an economic model to estimate the financial impact. The model framework in this research integrated the Framework for Analysis, Comparison, and Testing of Standards (FACTS) and The International Organization for Standardization (ISO) Methodology using the dynamic open innovation approach. The sample was 20 respondents representing stakeholders. This research used a semi-open questionnaire developed from the E-Mobility Systems Architecture (EMSA) model on three-layer interoperability (components, communications, and information) and economics benefit standard methodology. This research recommends SB dimensions, SB voltage, connector types, safety, and component compatibility requirements (BMS, sensors, controllers) to be regulated at the component layer. Meanwhile, further consensus is still needed at the communication and information layer. The economic model developed has been successfully applied using hypothetical data.

Understanding Factorial Designs, Main Effects, and Interaction Effects: Simply Explained with a Worked Example.

A factorial design examines the effects of two independent variables on a single, continuous dependent variable. The statistical test employed to analyze the data is a two-way analysis of variance (ANOVA). This test yields three results: a main effect for each of the independent variables and an interaction effect between the two independent variables. This article explains factorial designs and two-way ANOVA with the help of a worked example using hypothetical data in a spreadsheet provided as a supplementary file. The main effects and interaction effects are explained and illustrated using tables and figures. A short discussion provides general notes about the concepts explained in this article, along with brief notes on repeated measures ANOVA and higher order ANOVAs. Many additional examples, with figures and explanations, are provided in the supplementary materials, which the reader is strongly encouraged to view.

A Description of Missing Data in Single-Case Experimental Designs Studies and an Evaluation of Single Imputation Methods.

Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies.

Interpretation issues with "genomic vulnerability" arise from conceptual issues in local adaptation and maladaptation.

As climate change causes the environment to shift away from the local optimum that populations have adapted to, fitness declines are predicted to occur. Recently, methods known as genomic offsets (GOs) have become a popular tool to predict population responses to climate change from landscape genomic data. Populations with a high GO have been interpreted to have a high "genomic vulnerability" to climate change. GOs are often implicitly interpreted as a fitness offset, or a change in fitness of an individual or population in a new environment compared to a reference. However, there are several different types of fitness offset that can be calculated, and the appropriate choice depends on the management goals. This study uses hypothetical and empirical data to explore situations in which different types of fitness offsets may or may not be correlated with each other or with a GO. The examples reveal that even when GOs predict fitness offsets in a common garden experiment, this does not necessarily validate their ability to predict fitness offsets to environmental change. Conceptual examples are also used to show how a large GO can arise under a positive fitness offset, and thus cannot be interpreted as a population vulnerability. These issues can be resolved with robust validation experiments that can evaluate which fitness offsets are correlated with GOs.

A multiparameter mathematical model for the problem of nonlinear filtration of fluids in two-layer media

In the realm of fluid dynamics and porous media, the nonlinear filtration of fluids in two-layer media presents a complex and intriguing challenge that demands a comprehensive mathematical framework for accurate representation and analysis. This paper introduces a multiparameter mathematical model designed to elucidate the intricacies of fluid filtration in heterogeneous environments, specifically focusing on two-layer media. The authors research the construction of a multiparameter mathematical model and numerical solution of nonlinear problems of the theory of filtration of unstructured and structured fluids in hydrodynamically connected multilayer formations with different characteristics. The developed models and solution algorithms were tested on hypothetical data.