Matching Quality Research Articles

Fluxfinder: An R Package for Reproducible Calculation and Initial Processing of Greenhouse Gas Fluxes From Static Chamber Measurements

AbstractFluxes of greenhouse gases are a critical component of the earth's natural climate, but anthropogenic emissions have created an imbalance and resulted in global climate change. Quantifying the emission of these gases is vital to our understanding of their sources and sinks, both natural and anthropogenic. The static chamber method, in which a system of interest is enclosed, and gas concentrations are measured over time, is widely used to estimate fluxes of greenhouse gases. With the development of instruments such as infrared gas analyzers (IRGAs) supporting high‐frequency concentration data, there is a growing need for open‐source workflows to calculate fluxes. Here we present fluxfinder, an R package designed to support reproducible calculations and processing of greenhouse gas fluxes measured with the static chamber method. The package includes raw data file parsing from widely used IRGAs, metadata matching, unit conversion, flux estimations, and initial quality assurance/quality control (QA/QC). Diagnostic graphical plots provide a transparent way to differentiate between measurement issues and nonlinear behavior. The package is also designed to be easily integrated with the gasfluxes package for further fitting of nonlinear concentration‐time models, allowing alternative or additional flux QA/QC. The fluxfinder package offers a flexible workflow that is easily adaptable to promote open and reproducible greenhouse gas flux estimations.

Multimodal Data Mining Based on Self Attention Feature Alignment

This study explores the application of multimodal data mining in text and image vector processing, aiming to improve the depth and breadth of data analysis by integrating information from different data types. We use the FairFace dataset combined with the CLIP model encoding layer to obtain text and image vectors, and use the K-Means clustering algorithm to achieve vector dimensionality reduction. Subsequently, we introduced the bipartite graph matching algorithm to achieve maximum matching between text vectors and image vectors, and calculated the contrastive learning loss and similarity loss. The entire process covers steps such as data preparation, feature extraction, vector dimensionality reduction, matching algorithms, and loss assessment, constructing a complete text and image matching task process. Our research contributions include using K-Means clustering algorithm to achieve vector dimensionality reduction, as well as introducing bipartite graph matching algorithm and calculating two types of losses in text and image vector matching, further improving matching quality.

Low-carbon information quality dimensions and random forest algorithm evaluation model in digital marketing

The growing urgency for low-carbon lifestyles necessitates developing effective strategies to promote sustainable consumer choices. This study investigates key dimensions of information quality that shape consumer behavior within digital marketing to achieve this goal. Employing a mixed-methods approach that integrates grounded theory and machine learning, this study identifies three core dimensions of low-carbon information quality: matching quality, presentation quality, and interpretability quality. These dimensions underscore the importance of aligning information with consumer needs, ensuring clear and accurate presentation, and fostering transparency for trustworthiness. A Random Forest algorithm-based evaluation model is constructed to assess low-carbon information quality, demonstrating its effectiveness in identifying high-quality, sustainable content. This research provides a practical tool for digital marketers to enhance their strategies, raise consumer awareness of sustainable options, and ultimately contribute to the growth of the low-carbon consumption market.

Bayesian Inference for the Calibration of Progressive Damage Model of Dovetail Specimens from Laminated Composite Fan Blade

Abstract Composite fan blades are the preferred alternative for the fan stage of most advanced high bypass ratio turbofan engines. The ability of the dovetail to safely bear this load is one of the key points of the multi-level “test pyramid” approach of compliance demonstration for special airworthiness regulation. Debonding between adjacent layers is the main damage mode of laminated composite fan blades. However, there is difficulty in measuring the as manufactured interlaminar mechanical properties used in finite element models. In this study, tensile loading was applied to simulate the interacting centrifugal force and capture mixed mode damage evolution. Structural responses and material damages were calibrated with measured tensile loads through Bayesian inversion. Posterior probability distributions of maximum interface tractions and contact stresses were solved using Markov chain Monte Carlo sampler. Results indicated the two bilinear cohesive material models had a capacity of predicting empirical means of longitudinal reaction forces as that in test considering additional discrepancy term (0.035 kN and 0.96 kN respectively), while they made an significant impact on the prediction of tensile load history especially when two delamination cracks initiated and propagated. Interface elements provided a higher matching quality in predicting loading history and capturing damage mechanism in association with in-plane progressive damage analysis. This calibrated parameter set could be functioned as benchmark in numerically determining the ultimate tensile load of dovetail elements and reducing the necessary number of physical tests at elemental length level.

Accuracy of measurements on CBCT-generated digital models using different exposure parameters (in vitro study).

Dental imaging comprises a wide range of techniques and modalities, each with different diagnostic features influenced by numerous parameters, all of which contribute to the precision and effectiveness of dental evaluations and treatments. This study examined the dimensional reproducibility of arch measurements from CBCT scans with different voxel sizes and exposure parameters compared to a reference model from Extra-oral scanners (EOS) and how these parameters affected digital matching and diagnostic image quality. A diagnostic observational study of arch dimensions, including inter-canine, inter-premolar, intermolar, arch width and arch length, was conducted on digital models (DMs) created from 65 CBCT scans of a full dentate epoxy maxillary model. The measurements obtained from EOS scans served as the control for the study. Normality was tested with the Shapiro-Wilk test, comparisons used the Kruskal-Wallis test with Bonferroni-adjusted pairwise comparisons for significant results, and data were analysed using IBM SPSS (Version 26.0), with significance set at p < .05. Significant deviations were revealed among study groups, with group I (smallest voxel size) consistently displaying the lowest values, mean (SD) deviation was reported as 0.01 (0.006) and group IV (lowest kV value) exhibiting the highest deviations, mean (SD) deviation of 0.16 (0.17). Combining a small voxel size (0.12 mm) with high milliampere (8 mA) and kilovoltage (90 kV) settings in CBCT ensured detailed anatomical visualization and accurate linear measurements, crucial for precise dental assessments, and emphasizing the necessity for strict control over CBCT parameters in dental applications.

State primary standard of units of complex reflection coefficient and complex transmission coefficient in waveguide paths in the frequency range from 2.14 to 178.4 GHz GET 219-2024

Measurement of complex reflection coefficients and complex transmission coefficients of radio engineering devices in waveguide paths is widely used in radar and radio navigation in the development, production, testing and operation of microwave devices and components. These parameters characterize the quality of matching of transmit-receive paths at ultra-high frequencies. Before the introduction of the State primary standard of units of complex reflection coefficient and complex transmission coefficient in waveguide paths in the frequency range from 2.14 to 178.4 GHz GET 219-2024, metrological traceability of units of complex reflection coefficient and complex transmission coefficient in waveguide paths was ensured to the working standards developed in the 1980s. The operating frequency ranges and automation level of outdated standards did not meet modern requirements and did not fully provide a solution to modern measuring tasks. In order to ensure the uniformity of measurements of complex reflection coefficients and complex transmission coefficients, the State primary standard of units of complex reflection coefficient and complex transmission coefficient in waveguide paths in the frequency range from 2.14 to 178.4 GHz has been developed and approved. The standard provides reproduction, storage and transmission of units of complex reflection coefficient and complex transmission coefficient in 16 domestic and 22 foreign waveguide paths. The block diagram, technical and metrological characteristics of the standard are given. The research results confirmed that standard has comparable metrological characteristics with the standards of national metrological institutes of other countries.

APPROACH TO CREATION OF GEOLOGICAL AND RESERVOIR SIMULATION MODEL AND METHODS FOR HISTORY MATCHING OF RESERVOIR ENERGY STATE

Under conditions of complex geological structure and substantial share of hard-to-recover oil reserves, reservoir simulation is a key tool in reservoir management. Direct problems solved by reservoir simulation include forecasting of the production performance, control and management of reservoir development, and localization of residual oil reserves. Inverse problem solution is useful for updating the knowledge of geological structure, reservoir quality, and historical production data by means of history matching. The quality of history matching is fundamental to ensure reliable future forecasts and localization of residual oil zones. The main approaches to appropriate history matching rely on analysis and adjustment of well logging and geological parameters, as well as checking and correction of errors, inadequate historical data, application of physics-based and reasonable approaches and tools. The prerequisite for adequate history matching and predictive ability of the resultant reservoir simulation model is the compliance of simulated and actual energy state of the reservoir. In this paper, the initial stage of reservoir simulation is performed, the approach to history matching of reservoir energy state aimed at improving the quality of further predictions is described as exemplified by one of the production areas of Russian oil field. The main causes of poor history match of formation and bottomhole pressures are revealed. These causes are grouped as follows: — geological causes/reservoir quality, which include permeability field distribution, reservoir connectivity, and determination of rock and fluid compressibility;— technical causes (during reservoir simulation), that is model limitations in case of larger areas, the need to use sector models, consideration of buffer region;— technological causes, including inadequate historical data or lack of data, detection of behind-the-casing flows and production casing leaks in wellbore, etc. For each group of factors that have a substantial impact on reservoir pressure, recommendations and methods of energy state history matching on geological and reservoir simulation models are provided, examples are given.

Digitalization of industries and labor mobility in China

The paper utilizes data from the China Family Panel Studies (2014–2018) and China's Input-Output Table to investigate how digital development affects labor mobility decisions at industry level. Our findings demonstrate that the increased levels of digitalization, both in manufacturing and services dimensions, significantly boost the likelihood of labor mobility across industries. This relationship remains valid even after considering endogeneity issues. Mechanism analysis shows that digitalization affects the cross-industry mobility decision of workers by affecting the quality of skill matching, income level, and occupational prestige. The heterogeneous results show that the impact of digital development on industry mobility decisions in the overall and manufacturing dimensions exists in the labor force in economically underdeveloped regions, the labor force engaged in consumer services, and entrepreneurs. The development of digitalization in the services dimension mainly has a strong industry crowding-out effect on the workers from economically developed regions, the workers engaged in non-productive service industries, and the workers with employment security.

O-015 Enabling laboratory-quality male fertility analysis with 72-hour post ejaculation sample viability

Abstract Study question Can SPX72-treated semen samples sustain diagnostic viability and integrity for up to 72 hours, enabling comparable analysis of mail-in samples to fresh samples? Summary answer SPX72 preserves sperm viability, motility, and integrity up to 72 hours post-collection, equalling the diagnostic precision of fresh samples and enhancing at-home testing capabilities. What is known already Challenges with laboratory-based fertility assessments stem from logistical burdens and the necessity for timely sample delivery, often deterring male patient participation. Semen samples typically need to be assessed within 60 minutes of collection to ensure accuracy. Pre-existing solutions have failed to sufficiently prolong sample viability while matching the diagnostic accuracy of immediate laboratory analyses, an issue Sapyen's SPX72 medium addresses effectively. Study design, size, duration An exhaustive controlled study with over 100 semen samples compared SPX72-treated samples at 72 hours against fresh controls. The study incorporated actual postal logistics to emulate mail-in scenarios. Participants/materials, setting, methods A predetermined quantity of SPX72 per millilitre was added to the collected semen samples, with key indicators, including progressive and total motility, morphology, and DNA fragmentation, measured at intervals of 0, 24, 48, and 72 hours, contrasted with untreated, fresh samples. Main results and the role of chance SPX72 application preserved notable semen motility, evidenced by a mean progressive motility of 37.12% after 72 hours, a slight decrease from the initial 41.14%. This minor 4.02% reduction underscores SPX72's proficiency in maintaining diagnostic-grade motility over an extended duration. In terms of DNA integrity, SPX72 maintained DNA fragmentation at 14.78%, juxtaposed with 12.44% in control samples, a statistically insignificant variance (p-value of 1.15037E-08). This consistency in DNA fragmentation levels is pivotal for the accuracy of fertility diagnostic metrics. Additionally, SPX72 demonstrated its efficacy in stabilising the pH level of semen samples throughout the 72-hour period, ensuring an optimal environment for sperm preservation. This maintenance of pH is crucial as deviations can significantly affect sperm functionality and overall sample integrity. These results substantiate SPX72's capacity to preserve diagnostic sperm quality for up to three days, potentially redefining the logistics of male fertility testing by enabling accurate, laboratory-level assessment through a mail-in format. Limitations, reasons for caution Despite the controlled study and real-world postal validation, ongoing surveillance of sample stability in diverse global postal environments will be crucial to ensure consistent results across all geographies. Wider implications of the findings The data indicate that SPX72 sets a new standard for at-home fertility testing, matching clinical quality without the immediacy constraint. For the first time, home-based sperm diagnostics do not compromise clinical quality, revolutionising how male fertility is approached and managed. Trial registration number not applicable

Kinetic simulations of capacitively coupled plasmas driven by tailored voltage waveforms with multi-frequency matching

Impedance matching is crucial for optimizing plasma generation and reducing power reflection in capacitively coupled plasmas (CCP). Designing these matchings is challenging due to the varying and typically unknown impedance of the plasma, especially in the presence of multiple driving frequencies. Here, a computational design method for impedance matching networks (IMNs) for CCPs is proposed and applied to discharges driven by tailored voltage waveforms (TVW). This method is based on a self-consistent combination of particle in cell/Monte Carlo collision simulations of the plasma with Kirchhoff’s equations to describe the external electrical circuit. Two Foster second-form networks with the same structure are used to constitute an L-type matching network, and the matching capability is optimized by iteratively updating the values of variable capacitors inside the IMN. The results show that the plasma density and the power absorbed by the plasma continuously increase in the frame of this iterative process of adjusting the matching parameters until an excellent impedance matching capability is finally achieved. Impedance matching is found to affect the DC self-bias voltage, whose absolute value is maximized when the best matching is achieved. Additionally, a change in the quality of the impedance matching is found to cause an electron heating mode transition. Poor impedance matching results in a heating mode where electron power absorption in the plasma bulk by drift electric fields plays an important role, while good matching results in the classical α-mode operation, where electron power absorption by ambipolar electric fields at the sheath edges dominates. The method proposed in this work is expected to be of great significance in promoting TVW plasma sources from theory to industrial application, since it allows designing the required complex multi-frequency IMNs.

Intelligent Matching Method for College Dormitory Roommates: Chameleon Algorithm Based on Optimized Partitioning

A chameleon algorithm based on optimized partitioning was studied to solve the intelligent matching problem of college dormitory roommates. Using quantitative research methods, data on personal preferences and lifestyle habits of college students were collected, and the K-center object chameleon algorithm was used for data analysis and roommate matching. Test the algorithm performance on the BBC dataset, compare clustering quality indicators such as entropy, purity, and RI value, and verify the effectiveness of the algorithm. This algorithm can accurately assign students to their respective dormitories, avoiding overlapping situations and achieving excellent matching results. In terms of matching accuracy and running time, the K-center object chameleon algorithm shows superior performance compared to other algorithms. In terms of clustering quality evaluation, comparisons were made from three dimensions: entropy value, purity, and RI value. The experimental results show that the closer the entropy value is to 0, the closer the purity and RI value are to 1, and the better the matching effect. This result further validates the effectiveness of the algorithm in the intelligent matching problem of college dormitory roommates. The matching accuracy of this algorithm on the BBC dataset reached 98.82%, showing better clustering quality than other algorithms in terms of entropy, purity, and RI values. The entropy value approached 0, while the purity and RI values approached 1, verifying the efficiency of matching quality. The chameleon algorithm based on optimized partitioning proposed in the study has shown excellent performance in intelligent matching of college dormitory roommates, with the characteristics of high-precision matching and fast running time. It has important practical significance for improving the quality of life and learning efficiency of college dormitory students, and provides new research methods and ideas for related fields.

EMC+GD_C: circle-based enhanced motion consistency and guided diffusion feature matching for 3D reconstruction

Robust matching, especially the number, precision and distribution of feature point matching, directly affects the effect of 3D reconstruction. However, the existing methods rarely consider these three aspects comprehensively to improve the quality of feature matching, which in turn affects the effect of 3D reconstruction. Therefore, to effectively improve the quality of 3D reconstruction, we propose a circle-based enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction named EMC+GD_C. Firstly, a circle-based neighborhood division method is proposed, which increases the number of initial matching points. Secondly, to improve the precision of feature point matching, on the one hand, we put forward the idea of enhancing motion consistency, reducing the mismatch of high similarity feature points by enhancing the judgment conditions of true and false matching points; on the other hand, we combine the RANSAC optimization method to filter out the outliers and further improve the precision of feature point matching. Finally, a novel guided diffusion idea combining guided matching and motion consistency is proposed, which expands the distribution range of feature point matching and improves the stability of 3D models. Experiments on 8 sets of 908 pairs of images in the public 3D reconstruction datasets demonstrate that our method can achieve better matching performance and show stronger stability in 3D reconstruction. Specifically, EMC+GD_C achieves an average improvement of 24.07% compared to SIFT-based ratio test, 9.18% to GMS and 1.94% to EMC+GD_G in feature matching precision.

Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study

Objective. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness. Approach. We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms. The detectability index was used as a comprehensive task-based image quality metric. A statistical analysis based on the Intraclass Correlation Coefficient was performed to determine if matching image quality/appearance could enhance the robustness of radiomics features extracted from the phantom images. Additionally, an Artificial Neural Network was trained on these features to automatically classify the scanner used for image acquisition. Main results. We found that the ICC of the features across protocols providing a similar detectability index improves with respect to the ICC of the features across protocols providing a different detectability index. This improvement was particularly noticeable in features relevant for distinguishing between scanners. Significance. This preliminary study demonstrates that a harmonization based on image quality/appearance matching could improve radiomics features robustness and heterogeneous protocols can be used to obtain a similar image appearance in terms of the detectability index. Thus protocols with a lower dose level could be selected to reduce the amount of radiation dose delivered to the patient and simultaneously obtain a more robust quantitative analysis.

Firm cooperation policies: the impact of territorial spillovers

Research on program evaluation, and in particular on firm cooperation policies, has been scant on the impact of space-specific characteristics on program impacts. Few studies have analyzed how spatial features, that are sticky and non-mobile, may affect the intensity of a program’s effect on the targeted economic outcome. This paper uses a regional program (ERGON1) aimed at fostering the creation of Network Contracts to shed light on the contribution of spatial features to policy effectiveness. Network Contracts have been introduced in Italy with Law 9 April 2009, N. 33 to stimulate the formation of firm aggregations and to increase economic efficiency for network members. Empirical results, using Propensity Score Matching Estimates, suggest a positive and causative relation between membership in a Network Contract and firm productivity. Furthermore, evidence suggests that matching for urban characteristics significantly improves matching quality. Evidence is thus provided on the relevance of spatial features in shaping the returns to policies, thereby suggesting that ignoring such features may provide a biased picture of the true effect of a program.

Automated Trace Clustering Pipeline Synthesis in Process Mining

Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs.

Ethnic-Racial (Mis)Match between Mentors and Mentees on Perceived Strength of Relationship

Mentoring programs are popular mechanisms for promoting positive youth development due to developmental research suggesting that youth with strong relationships with a special adult have a lower likelihood of a range of negative outcomes. Community-based mentoring programs are mechanisms for promoting access to mental health support and positive youth outcomes. Youth mentorship programs reveal mixed and modest outcomes, yet youth outcomes are more robust for high-quality relationships. Ethnic-racial identity is hypothesized to affect the relationship quality because shared identities are thought to facilitate trust and empower youth to succeed. However, studies have found that ethnic-racial match does not affect the strength of the dyadic relationship. This study attempts to address these disparate findings by explicitly examining ethnic-racial matching and relationship quality at two-time points. We conducted a two-step hierarchical regression to determine whether ethnic-racial match affected mentee reports of the relationship at follow-up. The results show ethnic-racial match did not significantly predict positive dimensions of the relationship at follow-up. Moreover, same-ethnic-racial matches reported a lower rate of change and slightly less relationship dissatisfaction at time 2 compared to cross-ethnic-racial matches. Results contribute to existing literature showing mixed results in ethnic-racial matching in youth mentorship programs.

Designing Service Menus for Bipartite Queueing Systems

Designing Fair and Incentive-Compatible Matching Systems Many service systems face the challenge of dynamically matching customers who have heterogeneous preferences with an available pool of service providers in a fair and equitable manner. The first come, first served (FCFS) service discipline offers a straightforward and fair approach to allocate arriving customers to available servers and manage congestion. However, FCFS may limit the ability of service providers to reach an efficient allocation based on the individual preferences of customers and the characteristics of servers. In “Designing Service Menus for Bipartite Queuing Systems,” Caldentey, Hillas, and Gupta develop a queueing framework to address this dynamic matching problem. Their proposed methodology enables service providers to offer a menu of different service classes, each served by a distinct pool of servers. By thoughtfully designing this menu, a service provider can encourage self-interested customers to select service classes in a way that optimizes the trade-off between quality of matching and waiting time delays.

Video2Music: Suitable music generation from videos using an Affective Multimodal Transformer model

Numerous studies in the field of music generation have demonstrated impressive performance, yet virtually no models are able to directly generate music to match accompanying videos. In this work, we develop a generative music AI framework, Video2Music, that can match a provided video. We first curated a unique collection of music videos. Then, we analyzed the music videos to obtain semantic, scene offset, motion, and emotion features. These distinct features are then employed as guiding input to our music generation model. We transcribe the audio files into MIDI and chords, and extract features such as note density and loudness. This results in a rich multimodal dataset, called MuVi-Sync, on which we train a novel Affective Multimodal Transformer (AMT) model to generate music given a video. This model includes a novel mechanism to enforce affective similarity between video and music. Finally, post-processing is performed based on a biGRU-based regression model to estimate note density and loudness based on the video features. This ensures a dynamic rendering of the generated chords with varying rhythm and volume. In a thorough experiment, we show that our proposed framework can generate music that matches the video content in terms of emotion. The musical quality, along with the quality of music-video matching is confirmed in a user study. The proposed AMT model, along with the new MuVi-Sync dataset, presents a promising step for the new task of music generation for videos.

A simple and efficient approach to unsupervised instance matching and its application to linked data of power plants

Knowledge graphs store and link semantically annotated data about real-world entities from a variety of domains and on a large scale. The World Avatar is based on a dynamic decentralised knowledge graph and on semantic technologies to realise complex cross-domain scenarios. Accurate computational results for such scenarios require the availability of complete, high-quality data. This work focuses on instance matching — one of the subtasks of automatically populating the knowledge graph with data from a wide spectrum of external sources. Instance matching compares two data sets and seeks to identify instances (data, records) referring to the same real-world entity. We introduce AutoCal, a new instance matcher which does not require labelled data and runs out of the box for a wide range of domains without tuning method-specific parameters. AutoCal achieves results competitive to recently proposed unsupervised matchers from the field of Machine Learning. We also select an unsupervised state-of-the-art matcher from the field of Deep Learning for a thorough comparison. Our results show that neither AutoCal nor the state-of-the-art matcher is superior regarding matching quality while AutoCal has only moderate hardware requirements and runs 2.7 to 60 times faster. In summary, AutoCal is specifically well-suited to be used in an automated environment. We present its prototypical integration into the World Avatar and apply AutoCal to the domain of power plants which is relevant for practical environmental scenarios of the World Avatar.

Multi-stage probabilistic linguistic matching method with the screening mechanism and individual preference relationship fusion

Considering the diversification of transaction information and complexity of matching environment, the original information with the language tendency plays an important role. Thus, under this circumstance, this paper gives a matching decision-making system under the probabilistic linguistic environment so as to improve the matching efficiency and quality. First, this paper demonstrates the multi-stage two-sided matching problem and illustrates the system frame. Then, for the multiple indicators, a probabilistic linguistic integrated cloud Bayesian network is constructed to present the dependency relationship, and determine the corresponding probability. It is known that the accuracy of agents’ preference information acts on the stability of final matching results, which may involve the strong ranking, agents’ psychological preferences and personal interests, etc. Thus, the improved ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method is introduced to derive strong ranking and determine preference, indifference, and incomparability relation (PIR). Furthermore, this paper constructs the dynamic two-sided matching model considering the screening effect. Finally, a case study in second-hand house transaction is used to demonstrate the matching process. Simulation and comparison analysis validate its feasibility and effectiveness.