Merger Model Research Articles

On using the counting method to constrain the anisotropy of kilonova radiation

ABSTRACT A large number of binary neutron star (BNS) mergers are expected to be detected by gravitational wave (GW) detectors and the electromagnetic (EM) counterparts (e.g. kilonovae) of a fraction of these mergers may be detected in multibands by large area survey telescopes. For a given number of BNS mergers detected by their GW signals, the expected numbers of their EM counterparts that can be detected by a survey with given selection criteria depend on the kilonova properties, including the anisotropy. In this paper, we investigate whether the anisotropy of kilonova radiation and the kilonova model can be constrained statistically by the counting method, i.e. using the numbers of BNS mergers detected via GW and multiband EM signals. Adopting simple models for the BNS mergers, afterglows, and a simple two (blue and red)-component model for kilonovae, we generate mock samples for GW detected BNS mergers, their associated kilonovae, and afterglows detected in multibands. By assuming some criteria for searching the EM counterparts, we simulate the observations of these EM counterparts and obtain the EM observed samples in different bands. With the numbers of BNS mergers detected by GW detectors and EM survey telescopes in different bands, we show that the anisotropy of kilonova radiation and the kilonova model can be well constrained by using the Bayesian analysis. Our results suggest that the anisotropy of kilonova radiation may be demographically and globally constrained by simply using the detection numbers of BNS mergers by GW detectors and EM survey telescopes in multibands.

EXPERIMENTAL AND THEORETICAL CHARACTERIZATION OF TWO-PHASE FLOW DISTRIBUTION IN UNBALANCED FLOW NETWORKS

This study investigates the two-phase flow distribution of R-1234yf into two-branched channels. The effect of unbalanced pressure drop and tube diameter on the flow distribution was especially characterized and modeled. The experiments were conducted for a mass flux ranging from 393 kg/m<sup>2</sup>s to 1179 kg/ m<sup>2</sup>s, and the inlet quality was fixed at 0.2. The flow distribution was mainly governed by the pressure drop ratio between two branched channels. The mal-distribution under the unbalanced pressure drop condition was mitigated by increasing mass flux, whereas sustained mal-distribution was observed under unbalanced tube diameter conditions. Correspondingly, a theoretical representation of the phenomenon based on the principle of minimum entropy production was developed and adopted to predict two-phase flow distribution in an unbalanced flow network. The characteristics of the experimental data were clearly and quantitatively reflected in the prediction results. Under the unbalanced pressure drop condition, the predictions agreed well with the experimental data, maintaining the maximum deviation within ± 30%, whereas it exceeded ± 30% under the unbalanced tube diameter condition. The analysis of such theoretical formulation suggests the necessity of appropriate pressure drop models of flow impact, contraction, and merging at the outlet channel, which are compatible with the extremization of entropy production for further improving the prediction accuracy without compromising its generality.

A Hybrid GaN HEMT Model Merging Artificial Neural Networks and ASM-HEMT for Parameter Precision and Scalability

A Hybrid GaN HEMT Model Merging Artificial Neural Networks and ASM-HEMT for Parameter Precision and Scalability

A Study of the Impact of Smart Contract Technology on the Understanding and Application of Contract Law from the Perspective of the Legal Profession

Abstract This paper constructs the logical framework of the smart contract model based on the sale and purchase contract transaction, integrating the legal framework and the smart contract. The smart contract model merges blockchain technology’s decentralized features with smart contracts to create a decentralized service mode for sale and purchase transactions. The classification of transaction attributes optimizes and adjusts the matching degree calculation method to meet the needs of both buyers and sellers. The matching degree determines the total satisfaction of both buyers and sellers. The simulation experiment model is designed to analyze the matching results, and the experimental results indicate that the overall utility value range is between 0.1 and 0.5, resulting in a higher level of satisfaction for both parties involved. Applying the model to actual purchase and sale transaction contracts, the expected profit of transaction users after using smart contracts is 26,300 yuan higher than that without using smart contracts. The accuracy and confidence scores of experienced users are 0,8886 and 4.0493, respectively, when evaluating their perceptions of the use of smart contracts. Smart contracts can help participants identify sales and purchase problems and provide innovative perspectives on the application of contract law.

A Study on Sina’s Delisting under the Variable Interest Entity Structure Model: Motivations, Pathways, and Effects

Purpose - In recent years, with the increasingly stringent regulatory conditions for U.S. stocks and the full implementation of China’s registration system, Chinese stocks made a wave of privatization exits. The purpose of this study is to explore the intrinsic motivations for the privatization of Chinese stocks, analyze its privatization approach and implementation path, and the resulting effects of privatization.
 Design/Methodology/Approach - Taking Sina as the object of study, a case study approach is adopted to analyze the intrinsic motives, paths, and effects of privatization based on the analysis of its Variable Interest Entity (VIE) shareholding structure.
 Findings - Valuation discrimination, business hollowing, and fragmentation of shareholdings are the main drivers of privatization exits. Adopting a long form merger model is an effective route to delisting, and delisting contributes positively to the short-term effects on the market. It also contributes to the diversification of the company’s business and the governance of its shareholding structure.
 Research Implications - This research verifies the theory related to privatization, enriches the case study literature on the delisting of listed companies, and provides experience and reference for enterprises intending to delist abroad. It has important reference significance for maintaining the normal order of A-shares and improving the delisting system.

Active–Passive Remote Sensing Evaluation of Ecological Environment Quality in Juye Mining Area, China

The coal industry is a crucial component of China’s energy sector. However, the persistent exploitation of coal resources has gravely impacted the ecological environment. While the Remote Sensing Ecology Index (RSEI) is predominantly used for assessing ecological quality, its primary focus has been urban or aquatic environments. There is limited research focused on the evaluation of the ecological environment quality in mining areas. Moreover, the information regarding surface deformation caused by coal mining extraction is an essential factor in the ecological monitoring of mining areas. Therefore, this study proposed the Modified Remote Sensing Ecology Index (MRSEI). This enhanced model merges active and passive remote sensing techniques and incorporates a deformation factor (Surface Deformation Index, SDI) to provide a holistic evaluation of mining area ecologies. Furthermore, for comparative verification, we developed the Eco-environmental Quality Index (EQI) model by selecting 12 ecological parameters and employing a hierarchical analysis. The Juye mining area in Shandong Province was selected as the region of study. MRSEI results from 2015 to 2021 indicate a decline in the ecological quality of the Juye mining area, with MRSEI values registering at 0.691, 0.644, and 0.617. The EQI model mirrors this decreasing trend over the same period. Despite MRSEI using fewer indicators, its assessments align closely with the multi-indicator EQI method. This validates the accuracy of the MRSEI method, providing reliable technical support for the monitoring and evaluation of ecological environment quality in mining areas.

An attention-mechanism-based deep fusion model for improving quantitative precipitation estimation in a sparsely-gauged basin

Improving quantitative precipitation estimation (QPE) in sparsely-gauged basins via merging remote sensing precipitation data and rain gauge data still remains a challenge since most existing merging models degrade when the rain gauge data become limited. To address the challenge, we propose an attention-mechanism-based deep learning model, Multi-Level Transformer Fusion (MLTF) model, which allows to capture the inner interactions among the multi-source input data (TRMM 3B42 V7 data, GridSat-B1 data, and DEM) in the sparsely-gauged basin. Taking the source region of the Yellow River basin (SRYRB) as a representative case study, we demonstrate the performance of the proposed model and compare it with conventional methods (e.g., Multiplicative Bias Correction, Additive Bias Removal, linear regression, and Kriging) and deep-learning-based models (CNN, CNN-LSTM). Results indicate that the merged precipitation in SRYRB produced by the MLTF model exhibits a RMSE reduction of 27.1 %, MAE decrease of 11.2 %, and CC increase of 19.2 % in comparison to the original TRMM data, outperforming all the selected comparative methods. Finally, an improved daily precipitation dataset during 1999–2019 with a spatial resolution of 0.05° is produced for the study area. This study proposes a new method for QPE improvement in a sparsely-gauged basin, which would provide valuable data support for regional hydrological study and water resources management.

Radiomics models based on cortical damages for identification of multiple sclerosis with cognitive impairment

BackgroundCognitive impairment (CI) is a common symptom in multiple sclerosis (MS) patients. Cortical damages can be closely associated with cognitive network dysfunction and clinically significant CI in MS. So, in this study, We aimed to develop a radiomics model to efficiently identify the MS patients with CI based on clinical data and cortical damages. MethodsOne hundred and eighteen patients with MS were divided into CI and normal cognitive (NC) cohorts (62/56) as defined by the Montreal Cognitive Assessment (MoCA). All participants were randomly divided into train and test sets with a ratio of 7:3. The radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO) method. The discrimination models were built with the support vector machines (SVM) by the clinical data, radiomic features, and merge data, respectively. And the patients were further divided according to each cognitive domain including memory, visuospatial, language, attention and executive, and each domain model was applied by the most suitable classifier. ResultsA total of 2298 features were extracted, of which 36 were finally selected. The merge model showed the greatest performance with the area under the curve (AUC) of 0.86 (95 % confidence interval: 0.81–0.91), accuracy (ACC) of 0.78, sensitivity of 0.79 and specificity of 0.77 in test cohort. However, although the visuospatial domain model showed the highest AUC of 0.71 (95 % confidence interval: 0.61–0.81) among five domain models, other domain models did not meet satisfactory results with a relatively low AUC, ACC, sensitivity and specificity. ConclusionsThe radiomics model based on clinical data and cortical damages had a great potential to identify the MS patients with CI for clinical cognitive assessment.

GMP-selected dual and lensed AGNs: Selection function and classification based on near-IR colors and resolved spectra from VLT/ERIS, Keck/OSIRIS, and LBT/LUCI

The Gaia Multipeak (GMP) technique can be used to identify large numbers of dual or lensed active galactic nucleus (AGN) candidates at subarcsec separation, allowing us to study both multiple supermassive black holes (SMBHs) in the same galaxy and rare, compact lensed systems. The observed samples can be used to test the predictions of the models of SMBH merging when (1) the selection function of the GMP technique is known, and (2) each system has been classified as a dual AGN, a lensed AGN, or an AGN/star alignment. Here we show that the GMP selection is very efficient for separations above 0.15″ when the secondary (fainter) object has a magnitude G ≲ 20.5. We present the spectroscopic classification of five GMP candidates using VLT/ERIS and Keck/OSIRIS and compare them with the classifications obtained from (a) the near-IR colors of seven systems obtained with LBT/LUCI, and (b) the analysis of the total spatially unresolved spectra. We conclude that colors and integrated spectra can already provide reliable classifications of many systems. Finally, we summarize the confirmed dual AGNs at z > 0.5 selected by the GMP technique, and compare this sample with other such systems from the literature, concluding that GMP can provide a large number of confirmed dual AGNs at separations below 7 kpc.

The Formation of Blue Large-amplitude Pulsators from White-dwarf Main-sequence Star Mergers

Blue large-amplitude pulsators (BLAPs) are hot low-mass stars that show large-amplitude light variations likely due to radial oscillations driven by iron group opacities. Period changes provide evidence of both secular contraction and expansion among the class. Various formation histories have been proposed, but none are completely satisfactory. Zhang et al. proposed that the merger of a helium-core white dwarf with a low-mass main-sequence star (HeWD+MS) can lead to the formation of some classes of hot subdwarfs. We have analyzed these HeWD+MS merger models in more detail. Between helium-shell ignition and full helium-core burning, the models pass through the volume of luminosity–gravity–temperature space occupied by BLAPs. Periods of expansion and contraction associated with helium-shell flashes can account for the observed rates of period change. We argue that the HeWD+MS merger model provides at least one BLAP formation channel.

Orbit decay in encounters between anisotropic spherical galaxies of equal mass

ABSTRACT We investigate the effect of radial anisotropy on the rate of orbit decay in parabolic encounters of identical spherical galaxies. Our galaxy models have Hernquist density profiles and Osipkov–Merritt velocity distributions. We find that radially anisotropic models merge in as little as half the time of their isotropic counterparts. Anisotropic models are more susceptible to tidal deformation; this accelerates the transfer of orbital angular momentum to internal degrees of freedom. Even during the initial approach, the anisotropic models become more distorted, and arrive at pericentre already having lost substantial amounts of angular momentum. Our results may have implications for estimates of merger rates and the persistence of tidal tails.

Development of A Learning Model for Blended Collaborative Knowledge Construction in Vocational Education

Vocational Education readies students for specific careers and industries. A robust learning approach is crucial to meet modern workforce demands and foster knowledge exchange. This article introduces the origination of a Learning Model for Blended Collaborative Knowledge Construction in Vocational Education. This model merges methods from blended learning with collaborative strategies for constructing knowledge, fuelling student engagement, skills acquisition, and shared knowledge creation. The research utilizes the Delphi method to accumulate convincing proof for learning models prioritizing blended collaboration in knowledge generation. The sample pool comprises 60 students spanning two Zigong Vocational Training Institute classes, gathered via selective sampling. Data collection involved pre and post-testing of the student’s performance in the experimental and control groups, reinforced by the satisfaction survey from the experimental group. Data analysis employed statistical tools like percentages, means, standard deviations, and the t-test for related samples. The study found that pretest scores and SPSS data analysis showed that the f-test significant value is .702, which exceeds .05. This implies that, under the assumption of equal variance, the mean equivalence t-test findings for the first row of data, as shown in the table above, show that the experimental class averages 81.02 and the control class 81.05. The experimental and control groups' pretest scores are not significantly different, as the computed p-value of .984 is larger than .05. Thus, teaching practice is reasonable. The experimental group's student satisfaction measure averaged 4.27, ranging from 4.10 to 4.43, indicating that students are content with their academics. The control group's student satisfaction measure averaged 2.87, ranging from 2.50 to 3.10, indicating low satisfaction with learning. Students in a vocational education setting were compared using a hybrid collaborative knowledge creation learning model, demonstrating its potential to enhance student satisfaction and outcomes.

Vertical accuracy assessment for open-source digital elevation models using GPS control points and watershed basins delineation using GIS

Digital elevation models (DEMs) are crucial for numerous assessments of the Earth's surface, so this research aim to evaluate the quality of several open source DEMs like advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTM DEM), SENTINEL-1, and TerraSAR-X for digital elevation measurement (TanDEM-X). TanDEM-X is one of the newest DEM models; however, ASTER and SRTM are still the most used. To evaluate the qualities of these DEMs, the vertical accuracies of ASTER, STRM (all 30 m), SRTM, TanDEM-X (90 m), and SENTINEL 1 (range from 5 to 12.5 m) were tested using GPS reference points and The RMSE gave the values of ± 3.56 m ± 57.68 m, ±5.89 m, ±10.21 m and ± 20.25 mfor used SRTM1, SENTINEL-1, SRTM3, TanDEM-X, and ASTER DEMs respectively. As is well known, obtaining a high-quality DEM is exceedingly challenging since it is time-consuming, expensive, and often constrained, therefore this study examined how to enhance SRTM1 as it gave the best result and assessed the effects of increasing the number of points on digital elevation model accuracy by using raster calculator and merge model in ArcGIS. The results showed that the merge model method gave the best result and, by increasing the number of reference points, the accuracy increased. The DEM produced by the enhanced SRTM (iSRTM) demonstrated noticeably better outcomes than the baseline SRTM DEM, with around 3.56 m RMSE.In this research, STRM-1 DEM is used to determine watershed basin delineation. The topographic map showed significant variations in the level of the Earth's surface, as the heights of the area ranged between 20 m and 120 m. The number of watersheds extracted in the Benban is 132 with different areas, the large area is about 72.14 km2, while the small area is about 1.13 km2, and between those areas, there are various areas. The largest recorded watershed circumference reached 48.94 km in area (72.14 km2), while the lowest recorded perimeter is 5.40 km for the area (1.13 km2).

RXCJ1111.6+4050 galaxy cluster: Observational evidence of a transitional fossil group

We present a detailed kinematical and dynamical study of the galaxy cluster RXCJ1111.6+4050 (RXCJ1111), at z = 0.0756 using 104 new spectroscopic redshifts of galaxies observed at the Telescopio Nazionale Galileo and SDSS DR16 public archive. Our analysis is performed in a multiwavelength context in order to study and compare mainly optical and X-ray properties using XMM-Newton data. We find that RXCJ1111 is a galaxy cluster showing a velocity distribution with clear deviations from Gaussianity, that we are able to explain by the presence of a substructure within the cluster. The two cluster components show velocity dispersions of 644 ± 56 km s−1 and 410 ± 123 km s−1, which yield dynamical masses of M200 = 1.9 ± 0.4 × 1014 M⊙ and 0.6 ± 0.4 × 1014 M⊙ for the main system and substructure, respectively. The 2D spatial distribution of galaxies and X-ray surface brightness of RXCJ1111 presents an elongation in the North–South direction. These observational facts, together with a gradient of 250−350 km s−1 Mpc−1 in the velocity field, following the NNE–SSE direction, suggest that the merger axis between the main system and substructure is slightly tilted with respect to the line-of-sight. The substructure is characterized by a magnitude gap Δm12 ≥ 1.8, so it fits the “fossil-like” definition of a galaxy group. From the X-ray observations, we estimate a M500, X = 1.68 ± 0.25 × 1014 M⊙, which is in good agreement with the dynamical masses when two galaxy components are considered separately. This suggests that the mass estimates obtained from X-ray and velocity dispersion are compatible even for non-relaxed clusters, at least when we are able to identify and separate galaxy clumps and derive masses by considering the virialized regions. We propose a 3D merging model and find that the fossil group is in an early phase of collision with the RXCJ1111 main cluster and placed at ∼8° ( ± 3° ) from line-of-sight. This merging model would explain the slight increase found in the TX with respect to what we would expect for relaxed clusters. Due to the presence of several brightest galaxies, after this collision, the substructure would presumably lose its fossil condition. Therefore, RXCJ1111 represents the observational evidence that the fossil stage of a system can be temporary and transitional.

Full waveform model for axisymmetric black hole mergers

Full waveform model for axisymmetric black hole mergers

Analysis of key intermediates in the growth of polycyclic aromatic hydrocarbons using the parameter-selected merged model

Soot, a by-product of incomplete combustion of organic fuels, can cause engine exhaust blockage, affect engine performance, and increase fuel consumption. Regulations have been introduced to reduce soot emissions, and studying the formation and growth of polycyclic aromatic hydrocarbons (PAHs) is essential to achieve this. Having a sub-model with sufficient PAH targets and understanding the key intermediates in the growth paths of PAHs is important for constructing a comprehensive model from fuel to PAHs. This enables the fuel pyrolysis or oxidation sub-models to interface effectively with the PAHs generation. The species in the 6 basic models, which target toluene, o-xylene, xylenes, trimethylbenzenes, indene, and α-methyl-naphthalene, were represented using the IUPAC Standard InChI (InChI) and their structural features were extracted using RDKit in Python. Then, 6 merged models were created by merging the same species and reactions in the 6 basic models based on 6 different parameter selection orders, resulting in a sub-model with 1376 species that includes 11 priority PAHs. The DRGEP method was used to simplify the 6 merged models and identify the key intermediates involved in the generation of benzene, which is considered as the key aromatic compound in the growth pathway of PAHs. A total of 91 species were obtained, present in both the 6 basic models and the 6 simplified models, and the performance of the merged models was evaluated based on the peak concentration results of these species. The selected merged model was further simplified using the DRGEP method with different targets to identify the key intermediates involved in the generation of the 11 priority PAHs. Through a comprehensive analysis of species based on 6 basic models, a conclusion was drawn that 32 C2-C4 hydrocarbon species are essential intermediates in the growth pathway of 11 discussed priority PAHs.

Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) have become a relevant technique for modeling and forecasting time series due to their advantages in dealing with uncertainty and simulating the dynamics of complex systems. Although numerous univariate and multivariate FCM-based forecasting models have been presented in the literature, one of the still open questions is how to enable FCMs to forecast multivariate time series for multiple-input, multiple-output (MIMO) systems with an efficient learning mechanism. from a computational point of view. This paper suggests a randomized MIMO FCM-based forecasting approach called MO-RHFCM to predict low-dimensional multivariate time series. More specifically, MO-RHFCM is a hybrid model merging the concepts of multivariate fuzzy time series, high order FCM (HFCM), and Echo State Networks (ESN). The structure of MO-RHFCM consists of three layers: input layer, reservoir (internal) layer, and output layer. Only the output layer is trainable using the Least Squares minimization algorithm; hence training the proposed MO-RHFCM method is fast and simple. The weights inside each sub-reservoir are selected randomly and remain fixed during the training process. The obtained results indicate the efficacy and validity of the proposed MO-RHFCM technique compared with some machine learning and deep learning baseline models.

The AMIGA sample of isolated galaxies

Context. In the standard cosmological model of galaxy evolution, mergers and interactions play a fundamental role in shaping galaxies. Galaxies that are currently isolated are thus interesting because they allow us to distinguish between internal and external processes that affect the galactic structure. However, current observational limits may obscure crucial information in the low-mass or low-brightness regime. Aims. We use optical imaging of a subsample of the AMIGA catalogue of isolated galaxies to explore the impact of different factors on the structure of these galaxies. In particular, we study the type of disc break as a function of the degree of isolation and the presence of interaction indicators such as tidal streams or plumes, which are only detectable in the ultra-low surface brightness regime. Methods. We present ultra-deep optical imaging in the r band of a sample of 25 low-redshift (z < 0.035) isolated galaxies. Through careful data processing and analysis techniques, the nominal surface brightness limits achieved are comparable to those to be obtained on the ten-year LSST coadds (μr,lim ≳ 29.5 mag arcsec−2 [3σ; 10″ × 10″]). We place special emphasis on preserving the low surface brightness features throughout the processing. Results. The extreme depth of our imaging allows us to study the interaction signatures of 20 galaxies since Galactic cirrus is a strong limiting factor in the characterisation of interactions for the remaining 5 of them. We detect previously unreported interaction features in 8 (40% ± 14%) galaxies in our sample. We identify 9 galaxies (36% ± 10%) with an exponential disc (Type I), 14 galaxies (56% ± 10%) with a down-bending (Type II) profile, and only 2 galaxies (8% ± 5%) with up-bending (Type III) profiles. Isolated galaxies have considerably more purely exponential discs and fewer up-bending surface brightness profiles than field or cluster galaxies. We find clear minor merger activity in some of the galaxies with single exponential or down-bending profiles, and both of the galaxies with up-bending profiles show signatures of a past interaction. Conclusions. We show the importance of ultra-deep optical imaging in revealing faint external features in galaxies that indicate a probable history of interaction. We confirm that up-bending profiles are likely produced by major mergers, while down-bending profiles are probably formed by a threshold in star formation. Unperturbed galaxies that slowly evolve with a low star formation rate could induce the high rate of Type I discs in isolated galaxies.

Okul Öncesi Eğitime Devam Eden Ulus Aşırı Göçmen ve Yerel Çocukların Sınıf Yaşantılarına Katılım Hakları

The purpose of this research is to examine the perceptions of transnational immigrant and local children attending pre-school education in Turkey regarding their right to participate in classroom life and their participation in classroom life according to teachers' views. In this research, sequential explanatory design merging model, which is one of the mixed research methods, was used. The study group consists of 41 children, 21 of whom are transnational immigrants, 20 of whom are local, attending a pre-school under the Directorate of National Education of Antalya, where immigrant children are dense and 9 teachers of these children. The Right to Participate in Pre-school Classes Scale was applied to measure the perceptions of pre-school children in the study group about their right to participation. Semi-Structured Interviews were conducted with teachers to get information about children's participation in classroom life. In the analysis of the quantitative data of the research, Mann Whitney U-Test was used and qualitative data were analyzed with descriptive analysis. The results of the analysis showed that there was no significant difference between the scores of transnational immigrant children and local children on the Right to Participate in Pre-school Classes Scale. According to the teachers' views, it was determined that transnational immigrant children participated in play activities in the classroom more than other activities. It has been found that the participation of transnational immigrant children who speak Turkish in classroom life is similar to that of local children. The research findings were discussed based on the relevant literature. The implications of research findings for future theoretical and applied research are presented.

An end-to-end technology management model in cross-border M&A transactions

Subject. This article examines an adapted management model based on the use of end-to-end technologies in key business processes for finding target companies and deciding on the feasibility of implementing mergers and acquisitions. Objectives. The article aims to present an author-developed model for managing end-to-end technologies in cross-border mergers and acquisitions. Methods. For the study, we used empirical and logical constructions, analysis and synthesis, generalization, formalization, systems approach, and the graphic and tabular methods of visualization. Results. The article identifies trends in the use of artificial intelligence in the main elements of the developed management model along with traditional ways of managing mergers and acquisitions. The proposed system management integrator helps use machine learning algorithms and business process controlling to increase the accuracy and efficiency of decisions and maximize the synergy of the buyer and the target company after the implementation of mergers and acquisitions, which is verified using mathematical algorithms and developed indicators for the use of artificial intelligence and big data business process management. Conclusions. The management model of cross-border mergers and acquisitions of companies determines the use of end-to-end technologies to improve the time and quality of management decision-making.