Solution Of Model Research Articles

Dynamic Incentive Mechanism of Collaboration in Prefabricated Building Supply Chain Based on Differential Game

Supply chain collaboration is an important guarantee for improving the performance of prefabricated construction projects and exerting its advantages. The aim of this study was to explore the level and effect of supply chain collaboration under a no-cost-sharing contract, a cost-sharing contract, and a centralized decision-making contract. A dynamic incentive model of prefabricated building supply chain collaboration based on the differential game model was proposed. Considering the impact of BIM on collaboration, the BIM application level was also introduced into the incentive model. Through the comparative analysis of model solution results and numerical simulation, it was found that, in the centralized decision-making contract, the optimal collaborative effort and the optimal total revenue of enterprises in the prefabricated building supply chain were the largest; the optimal collaborative effort of enterprise in the prefabricated building supply chain reduced first and then tended to be stable; and the improvement of the BIM application level could promote collaboration in the prefabricated building supply chain. The results of this study are helpful for managers to select and formulate a reasonable collaboration incentive contract in the prefabricated building supply chain.

Deep learning models security: A systematic review

Deep learning models and the digital records they generate have remarkably increased their adoption of many practical applications. While the success of deep learning in multimedia applications, especially images, helps tackle some of the most challenging problems, one of its copyright violations, ownership conflict, poses a grave concern for many potential applications. Many works on intellectual property protection for such models have proposed to verify ownership. Therefore, it is necessary to conduct a comprehensive study on the security of deep learning models to evaluate their strong background, state-of-the-art solutions, possible attacks, current limitations and notable improvements. This survey attempts to systematically discuss and summarise the recent advanced security solutions for deep learning models through watermarking, encryption and fingerprinting. Our study explores the recent applications, possible attacks, current limitations and notable suggestions regarding deep learning. It also comprehensively evaluates the recent research gaps and opportunities in detail to empower researchers and practitioners to provide additional secure solutions for deep learning models. This extensive survey is the first to consider model security through several notable techniques.

Indoor Pedestrian Positioning Method Based on Ultra-Wideband with a Graph Convolutional Network and Visual Fusion.

To address the challenges of low accuracy in indoor positioning caused by factors such as signal interference and visual distortions, this paper proposes a novel method that integrates ultra-wideband (UWB) technology with visual positioning. In the UWB positioning module, the powerful feature-extraction ability of the graph convolutional network (GCN) is used to integrate the features of adjacent positioning points and improve positioning accuracy. In the visual positioning module, the residual results learned from the bidirectional gate recurrent unit (Bi-GRU) network are compensated into the mathematical visual positioning model's solution results to improve the positioning results' continuity. Finally, the two positioning coordinates are fused based on particle filter (PF) to obtain the final positioning results and improve the accuracy. The experimental results show that the positioning accuracy of the proposed UWB positioning method based on a GCN is less than 0.72 m in a single UWB positioning, and the positioning accuracy is improved by 55% compared with the Chan-Taylor algorithm. The proposed visual positioning method based on Bi-GRU and residual fitting has a positioning accuracy of 0.42 m, 71% higher than the Zhang Zhengyou visual positioning algorithm. In the fusion experiment, 80% of the positioning accuracy is within 0.24 m, and the maximum error is 0.66 m. Compared with the single UWB and visual positioning, the positioning accuracy is improved by 56% and 52%, respectively, effectively enhancing indoor pedestrian positioning accuracy.

Analytic solution for the linear rheology of living polymers

It is often said that well-entangled and fast-breaking living polymers (such as wormlike micelles) exhibit a single relaxation time in their reptation dynamics, but the full story is somewhat more complicated. Understanding departures from single-Maxwell behavior is crucial for fitting and interpreting experimental data, but in some limiting cases numerical methods for solving living polymer models can struggle to produce reliable predictions/interpretations. In this work, we develop an analytic solution for the shuffling model of living polymers. The analytic solution is a converging infinite series, and it converges fastest in the fast-breaking limit where other methods can struggle.

Sol-gel synthesis, structure and adsorption properties of LiMgxMn(2-x)O4 (0 ≤ x ≤ 0.7) Oxides

Lithium manganese оxides with a spinel structure LiMgxMn(2–x)O4, doped with Mg2+ ions in the range 0 ≤ x ≤ 0.7, were obtained by sol-gel synthesis. Phase composition and morphology of obtained оxides were studied by using X-ray phase analysis and scanning electron microscopy. It is shown, that in the studied range 0 ≤ x ≤ 0.7 Mg-doped lithium manganese оxides saved the structure of the original cubic spinel LiMn2O4, while an increase in parameter a was observed from 8.175 to 8.309 Å and average crystallite size practically unchanged (30–36 nm). Samples of the initial LiMn2O4 and Mg-doped spinels were represented by prismatic particles of submicron (0.1–0.2 µm) and micron (1.0–3.0 µm) sizes, respectively. The effect of the adsorbent dose (0.05–0.3 g/l) and pH (3.0–13.0) of the solution on the adsorption efficiency was studied. The adsorption isotherms of the LiMg0.3Mn1.7O4 samples were described by the Langmuir monomolecular adsorption equation. An increase in the temperature of the model solution from 25 to 45°C was accompanied by an increase in the maximum adsorption of the LiMg0.3Mn1.7O4 samples from 10.50 to 10.98 mmol/g, which indicates the endothermic nature of the adsorption process. The kinetics of adsorption was well described by a pseudo-second order equation, which indicates the occurrence of chemical interaction during the adsorption process.

Aging-induced dysbiosis worsens sepsis severity but is attenuated by probiotics in D-galactose-administered mice with cecal ligation and puncture model.

Despite the well-established effects of aging on brain function and gut dysbiosis (an imbalance in gut microbiota), the influence of aging on sepsis-associated encephalopathy (SAE) and the role of probiotics in this context remain less understood. C57BL/6J mice (8-week-old) were subcutaneously administered with 8 weeks of D-galactose (D-gal) or phosphate buffer solution (PBS) for aging and non-aging models, respectively, with or without 8 weeks of oral Lacticaseibacillus rhamnosus GG (LGG). Additionally, the impact of the condition media from LGG (LCM) was tested in macrophages (RAW 264.7 cells), microglia (BV-2 cells), and hippocampal cells (HT-22 cells). Fecal microbiome analysis demonstrated D-gal-induced dysbiosis (reduced Firmicutes and Desulfobacterota with increased Bacteroidota and Verrucomicrobiota), which LGG partially neutralized the dysbiosis. D-gal also worsens cecal ligation and puncture (CLP) sepsis severity when compared with PBS-CLP mice, as indicated by serum creatinine (Scr) and alanine transaminase (ALT), but not mortality, neurological characteristics (SHIRPA score), and serum cytokines (TNF-α and IL-6). Additionally, D-gal-induced aging was supported by fibrosis in the liver, kidney, and lung; however, CLP sepsis did not worsen fibrosis. Interestingly, LGG attenuated all parameters (mortality, Scr, ALT, SHIRPA, and cytokines) in non-aging sepsis (PBS-CLP) while improving all these parameters, except for mortality and serum IL-6, in aging sepsis (D-gal CLP). For the in vitro test using lipopolysaccharide (LPS) stimulation, LCM attenuated inflammation in some parameters on RAW264.7 cells but not BV-2 and HT-22 cells, implying a direct anti-inflammatory effect of LGG on macrophages, but not in cells from the brain. D-gal induced fecal dysbiosis and worsened sepsis severity as determined by Scr and ALT, and LGG could alleviate most of the selected parameters of sepsis, including SAE. However, the impact of LGG on SAE was not a direct delivery of beneficial molecules from the gut to the brain but partly due to the attenuation of systemic inflammation through the modulation of macrophages.

Deep reinforcement learning optimization scheduling algorithm for continuous production line

In order to improve the scheduling effect of continuous production lines and enhance the processing efficiency of production lines, a deep reinforcement learning optimization scheduling algorithm for continuous production lines is proposed. The Monte Carlo algorithm and Bayesian evaluation method are combined to reduce the data complexity of the continuous production line pipeline problem; the deep neural network model is used to optimize the pipeline scheduling parameters, evaluate and encode them; the iterative greedy algorithm is combined with the deep reinforcement learning method to implement the model solution for the scheduling data problem and realize the scheduling of continuous production lines. The experimental results show that the optimal comprehensive evaluation results of the scheduling results of the proposed algorithm are all higher than 0.9531, the process delay is optimized to less than 5 min, the collection speed is fast, and the processing efficiency of the production line is improved.

Analysis and optimization of the dynamic response characteristics of aircraft cargo rack

In order to effectively achieve the optimization design of the aircraft cargo rack, based on finite element modal simulation, the structure was redesigned and analyzed using two schemes of surrogate model size optimization and topology optimization, ensuring the strength requirements while improving the natural frequency and reducing the weight. According to the modal shape of vibration, additional bridge structures were added at the weak points to improve the dynamic stiffness of the structure. According to the optimization design requirements, the response surface function was constructed, and the particle swarm optimization algorithm was applied in the size optimization and surrogate model solution. A multi-objective optimization model was established for flexibility and low-order natural frequencies, and topology optimization was carried out in HyperWorks. The structural dynamic modification of the topology optimization model was performed using the modal strain energy analysis method. The research results show that both optimization methods can achieve good lightweight design. The static performance of the optimized structure is stable, and the overall modal frequency is improved.

A study on analytical solutions of one of the important shallow water wave equations and its stability analysis

Abstract The present research uses the modified w/g-expansion technique to give analytical solutions for the nonlinear time fractional integro-differential Kadomtsev Petviashvili (KP) hierarchy equation with beta fractional derivative. The modified w/g-expansion technique is an important method for finding analytical solutions to nonlinear equations. The suggested approach produces three sorts of solutions trigonometric, hyperbolic, and rational. These answers have been discovered with the aid of a software tool. Additionally, stability analysis of observed governing model solutions is used to validate scientific calculations. Furthermore, the 3-dimensional, contour, and 2-dimensional images are provided for a better understanding of the behavior of the solutions.

A Genetic algorithm approach for improving the probabilistic inventory model with the continuous review: A practical application in the department of pharmacy

This study investigates to shed light on artificial intelligence techniques, specifically the genetic algorithm, to improving traditional solutions, as well as finding the best policy for the problem of probabilistic inventory with continuous review by finding the optimal reorder point and the optimal economic quantity to avoid the risk of stock out (shortage), reduce total costs, and reach the optimal solution with little time and effort. The research was conducted in the stores of the department of pharmacy of the Ninawa health department for the period from January 1, 2021, to January 1, 2023, on a sample of three drugs (most demanded drugs). An analysis of the demand data for the study sample was conducted using the statistical program (SPSS Statistics Version 22) to determine the type of inventory. it was found that the type of inventory is probabilistic and the demand data follows a normal distribution. Based on this foundation, the mathematical model for the study problem was built. Given the complexity of the steps and iterations of the traditional solution, the solution steps were applied using the R programming language to reach the model's solution. Additionally, the solution steps were programmed for the genetic algorithm and applied using the R programming language. The results of the genetic algorithm showed an improvement in the traditional solution results by reducing total costs. Based on the results of the genetic algorithm, the economic order quantity, safety stock, reorder period, and safety period were found. Paper type: Research Paper

Branch and Price for the Stochastic Traveling Salesman Problem with Generalized Latency

We consider an extension of the symmetric traveling salesman problem with generalized latency that explicitly models uncertainty. The stochastic traveling salesman problem with generalized latency (STSP-GL) aims to choose a subset of nodes of an undirected graph and determines a Hamiltonian tour among those nodes, minimizing an objective function that is a weighted combination of route design and passenger routing costs. These nodes are selected to ensure that a predefined percentage of uncertain passenger demand is served with a given probability. We formulate the STSP-GL as a stochastic program and propose a branch-and-price algorithm for solving its deterministic equivalent. We also develop a local search approach with which we improve the performance of the branch-and-price approach. We assess the efficiency of the proposed methods on a set of instances from the literature. We demonstrate that the proposed methods outperform a known benchmark, improving upper bounds by up to 85% and lower bounds by up to 55%. Finally, we show that solutions of the stochastic model are both more cost-effective and robust than those of the deterministic model. History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023. Funding: This work was supported by the Bundesministerium für Bildung und Forschung [Grant 03ZU1105FA]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0417 .

Error estimates of effective boundary conditions for the heat equation with optimally aligned coatings

We are interested in the validity of effective boundary conditions for a heat equation on a coated body as the thickness of the coating shrinks to zero. The coating is optimally aligned in the sense that the normal vector in the coating is an eigenvector of the thermal tensor. If the heat equation satisfies Neumann boundary condition on the outer boundary of the coating, Chen et al. (Arch. Ration. Mech. Anal. 206 (2012) 911-951) derived the complete list of effective boundary conditions satisfied by the limiting model. In this paper we provide explicit error estimates between the full model and the effective model. Moreover, our error estimates are independent of time, which shows that the maximal time interval in which the effective boundary conditions remain valid are infinite. The proof is based on H2 estimates for solutions of the full model, characterization of large time behaviors for solutions of the effective model, and interaction estimates between the two models.

Sequential bias-corrected weighted least squares solution of mixed additive and multiplicative error models

In the era of big data, the number of observations in adjustment calculations may reach tens or even hundreds of thousands. When dealing with these large dataset problems, many matrix operations are often required. At this time, the dimensions of the matrix will be large, which will generate a great computational burden. At present, no research results have been published on the computational efficiency of bias-corrected weighted least squares (bcWLS) for mixed additive and multiplicative error models (MAMEM). Sequential adjustment (SEA) groups the observations for calculation and can provide the same computational precision while greatly improving computational efficiency. This paper applies the idea of SEA to the calculation of bcWLS and proposes an iterative solution for sequential bcWLS (SEbcWLS). Using three simulation experiments to verify the effectiveness of our method, it was found that when the number of observations is 10000, the effect is better when the number of groups does not exceed 100, achieving the same precision as the original method while having high computational efficiency. The calculation results of line fitting and plane fitting are not affected by the number of grouping groups. For DEM (Digital elevation model) experiments with strong nonlinearity, when the number of grouping groups is too large, the effect is not very good, but the calculation efficiency is also higher than the original method, and the difference in calculation results is not significant.

Effect of Ultrahigh Dose Rate on Biomolecular Radiation Damage.

Dose rate is one of the important parameters in radiation-induced biomolecular damage. The effects of dose rate have been known to modify radiation toxicity in biological systems. The rate and extent of sublethal DNA damage (e.g., base damage and single-strand breaks) repair and those of cell proliferation have been manifested by dose rate. However, the recent preclinical application of ultrahigh dose rate [(UHDR) ca. 40 Gy/s and higher] radiation modalities have been shown to lower the type and extent of radiation damage to biological systems. At these UHDR, radiation-induced physicochemical and chemical processes are expected to differ from those observed after irradiation at conventional dose rates (CONV). It is unclear whether these UHDR conditions can affect the quality (type) and quantity (extent) of biomolecular damage such as DNA lesions. Here, we comparatively study the influence of indirect effects of CONV and UHDR on the formation of DNA strand breaks and clustered damage including densely accumulated lesions in an aerated and an anoxic dilute aqueous solution of a plasmid DNA model under low and high hydroxyl radical (•OH) scavenging conditions. Aqueous solutions of purified supercoiled plasmid DNA (pUC19) were prepared in either air- or nitrogen-saturated conditions, with Tris buffer added as the radiation-produced •OH scavenger at low and high scavenging capacities. These DNA samples were irradiated using kV X-ray systems at CONV (0.1 Gy/s) and high dose rate (HDR, 25 Gy/s) as well as UHDR (55 and 125 Gy/s) under different scavenging and environmental conditions. DNA lesions including strand breaks and clustered damage including densely accumulated lesions were quantified by gel electrophoresis and the yields of these lesions were calculated from the dose-response curve. Non-DSB clustered damage including densely accumulated lesions were evaluated by treating DNAs using bacterial endonuclease enzymes (Fpg and Nth) prior to gel electrophoresis. UHDR of 55 and 125 Gy/s induced lower amounts of both isolated strand breaks and clustered DNA damage including densely accumulated lesions at doses >40 Gy in the presence of oxygen, compared to the abundance of these lesions induced by 0.1 and 25 Gy/s irradiation under the same dose conditions. Overall, the strand break and clustered damage including densely accumulated lesions yields decreased by factors of 1.3-3.5 after UHDR. We did not observe these differences either via •OH scavenging or by removing oxygen from the solution. In addition, our results point out that the inter-track recombination reactions did not contribute to the observed dose-rate effects on DNA damage. The effects of dose rate on DNA damage are highly dependent on the total dose, as expected, but also on the •OH scavenging capacity that is employed in the aqueous DNA solutions. These important variables may be relevant in biological systems as well. On a practical level, our in vitro plasmid DNA model, which permits to precisely vary the scavenging capacity and gassing conditions (air saturated vs. N2 saturated) can help to differentiate dose-rate effects on biomolecular damage. Our results indicate that the radical-radical reactions are important in understanding the dose-rate effect on DNA damage.

Single-layer digitized-counterdiabatic quantum optimization for p-spin models

Abstract Quantum computing holds the potential for quantum advantage in optimization problems, which requires advances in quantum algorithms and hardware specifications. Adiabatic quantum optimization is conceptually a valid solution that suffers from limited hardware coherence times. In this sense, counterdiabatic quantum protocols provide a shortcut to this process, steering the system along its ground state with fast-changing Hamiltonian. In this work, we take full advantage of a digitized-counterdiabatic quantum optimization algorithm to find an optimal solution of the p-spin model up to four-local interactions. We choose a suitable scheduling function and initial Hamiltonian such that a single-layer quantum circuit suffices to produce a good ground-state overlap. By further optimizing parameters using variational methods, we solve with unit accuracy two-spin, three-spin, and four-spin problems for 100%, 93%, and 83% of instances, respectively. As a particular case of the latter, we also solve factorization problems involving 5, 9, and 12 qubits. Due to the low computational overhead, our compact approach may become a valuable tool towards quantum advantage in the NISQ era.

Analytical solution of a microrobot-blood vessel interaction model

AbstractThis study develops a dynamics model of a microrobot vibrating in a blood vessel aiming to detect potential cancer metastasis. We derive an analytical solution for microrobot’s motion, considering interactions with the vessel walls modelled by a linear spring-dashpot and a constant damping value for blood viscosity. The model facilitates instantaneous state transitions of the microrobot, such as contact with the vessel wall and free motion within the fluid. Amplitudes and phase angles from the transient solutions of dynamics model of the microrobot are solved at arbitrary moments, providing insights into its transient dynamics. The analytical solution of the proposed system is validated by experimental data, serving as a benchmark to examine the influence of pertinent parameters on microrobot’s dynamic response. It is found that the contact force transmitted to the vessel wall, assessed by system’s transmissibility function dependent on damping and frequency ratios, decreases with increasing damping ratio and intensifies when the frequency ratio is below $$\sqrt 2$$ 2 . At the frequency ratio is equal to 1, resonance phenomenon is dominated by the magnification factor linked to the damping ratio, increasing the amplitude of resonance as damping decreases. Finally, different sets of system parameters, including excitation frequency and magnitude, fluid damping, vessel wall’s stiffness and damping, reveal multi-periodic motions and fake collision of the microrobot with the vessel wall. Simulation results imply that these phenomena are minimally affected by vessel wall’s stiffness but are significantly influenced by other parameters, such as fluid damping coefficient and damping coefficient of the blood vessel wall. This research provides a robust theoretical foundation for developing control strategies for microrobots aimed at detecting cancer metastasis.

Development and Validation of a Pediatric Celiac Disease-Specific Quality of Life Measure.

Quality of life (QOL) is critical in screening and management of chronic medical conditions, including celiac disease (CD). This project aimed to develop a CD-specific pediatric QOL measure ("CDLIFE") with parallel self- and parent-report forms by generating items through concept elicitation interviews, iterative refinement using cognitive debriefing interviews, and evaluating its psychometric properties and validity. Concept elicitation interviews were conducted to develop items (nine youth ages 8-19 with CD; 10 parents of youth with CD), followed by cognitive interviews with additional stakeholders (three youth with CD, three parents, and eight clinicians), and item administration (parent/youth reports: n=103/102). Analyses included response frequencies, internal consistency reliability, exploratory factor analyses, and correlations with related measures (PROMIS, PEDSQL, and GFD-VAS). From concept elicitation interviews, 42 youth and 45 parent items were developed. Cognitive debriefing interviews yielded 36 refined items. Psychometric analyses identified 15 items to remove due to ceiling/floor effects, poor item-to-test correlations; youth-parent mismatch or conceptual mismatch. Total score internal consistency was high (alphas=0.89-0.90). A four-factor model solution had the best fit (Social Impact, External Support, Adaptive Vigilance, Eating Behaviors/Adjustment) with a fifth single-item domain (Financial Resources). The final CDLIFE (21 items) total scores correlated with most related measures in expected directions for parent and youth forms. The CDLIFE may facilitate insight into CD-specific QOL for youth ages 2-18 years, capturing important dimensions of physical and socioemotional health. Administering the CDLIFE will help identify and track families needing support.

Identifying optimal cloud cover for enhanced forest carbon uptake: Periodic-case NEE-overshoot modelling

On certain kinds of cloudy days, many forested ecosystems exhibit enhanced carbon uptake and water-use efficiency-the cloudy-day forest flux anomaly. Using ensemble methods to analyze eddy-covariance fluxes, we have diagnosed net ecosystem exchange (NEE) and water-use efficiency (WUE) of a temperate broadleaf forest and a tropical evergreen forest as they responded to natural fluctuating-light regimes. Here we apply average NEE and evapotranspiration solutions of a first-order dynamic model to describe the observed whole-canopy sensitivity to periodic light. On partly-cloudy days, maximum overall NEE enhancements over conventional steady-state equilibrium estimates are ≈ 25% for a midlatitude deciduous forest and ≈ 15% for a tropical evergreen forest. This finding supports our conclusion that in many cases the cloudy-day anomaly is a consequence of a dynamic response by the trees responding to fluctuating-light regimes occasioned by passing cumulus clouds.

On the Mathematical Background of Sliding Mode-Based Friction Compensation of a Micro-Telemanipulation System

Modeling of various phenomena in engineering work is always a kind of simplification of real processes, aiming at a model where a certain level of mathematical theory and computational procedures is sufficient. If the complexity of the required theory corresponds to the general mathematical competence of engineers, then technical problems can be treated separately in engineering (or physical) models without regard to the mathematical background. However, in some advanced engineering fields, the harmonized development of engineering and mathematical models and toolboxes is necessary to find efficient solutions. For example, modeling variable structure systems in ideal sliding mode requires a mathematical toolbox that goes far beyond general engineering competence through the theory of discontinuous right-hand-side differential equations. Although sliding mode control is popular in practice and the concept of sliding mode allows a significant reduction of model complexity, its exact mathematical description is rarely encountered. The problem of friction compensation of a micro-telemanipulator using sliding mode control demonstrates a harmonized application of the mathematical and engineering approaches. Based on Filippov’s theory, the ideal sliding mode can be discussed. Although an ideal system cannot be implemented in reality, the real systems can be kept close enough to it; therefore, the discussion of the solution of the ideal model is important for practical applications. Although several elements of the topic are available in the literature, in this paper a unique complex approach is given for users of sliding mode control with experimental considerations, different engineering models, and codes. The paper concludes that sliding mode control is a case where engineering and mathematical modeling are inseparable and requires the competence of both fields.

The Frank-Wulf method in modeling information systems

Subject of research: the process of choosing the best option for the structure of an information system under given conditions at the design stage. Purpose of research: to increase the efficiency and quality of the validity of decision-making, to reduce time and financial costs when choosing the structure of an information system at the design stage. Methods and objects of research: the object of research is an information system. The problem of nonlinear programming is formulated. A mathematical model of choosing the optimal variant of an information system is presented. By the method of expert assessments, information system projects with point values are determined. Information system projects with maximum values of scores are selected to determine the optimal option. The method of the conditional Frank-Wolf gradient determines the optimal solution of a nonlinear mathematical model. The essence of gradient methods is a sequential change in the value of the objective function when moving from the starting point of the domain of acceptable solutions to the optimal value of the function. The algorithm of the Frank-Wulff method includes the following steps: determining the initial value of the point of the domain of acceptable solutions, calculating the gradient, determining the optimal solution to a linear programming problem, moving to a new point, checking the condition for the end of the iterative process. The iterative process of determining the optimal solution ends when the calculated value of the calculation accuracy is less than the given value. Main results of research: preliminary projects of the information system were determined using the method of expert assessments, the optimal solution of the mathematical model of nonlinear programming by the Frank-Wolf method was found. A mathematical model for determining the optimal design of an information system will improve the efficiency and quality of the validity of decisions made, reduce financial costs and design time of information systems. The results obtained can be used in further research on this topic.