Matching System Research Articles

SymmPi: Exploiting Symmetry Removal for Fast Subgraph Matching

Abstract Symmetry, a phenomenon of self-similarity, is common in many networks, which often incurs a lot of redundant accesses and computations, even duplicate results when executing graph matching tasks. Many approaches (e.g. symmetry-breaking methods) try to disrupt symmetry by translating symmetry into restrictions and then imposing restrictions on the exploration order. However, the restrictions are finer-grained. If the pattern graph is complex, more restrictions are generated from symmetry breaking methods, thus complicating the exploration process and degrading the performance. Here, we present novel SymmPi, which exploits symmetry removal for fast graph matching. SymmPi first identifies the coarse-grained axisymmetric subgraphs of the given pattern graphs instead of finer relationships. If a pattern graph is not axisymmetric, SymmPi will remove some of its edges until axisymmetric subgraphs are found. Thus, the original pattern graph is transformed to a set of axisymmetric subgraphs plus some edges. Then, SymmPi finds the matches of the axisymmetric subgraph and extends these matches to the original pattern graphs by permuting the matches with additional checks. Our experiments on both directed and undirected graphs, demonstrate that SymmPi achieves a significant performance improvement over the state-of-the-art undirected and directed graph matching methods and systems.

Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model.

Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival. A risk stratification score integrated with the model of end-stage liver disease score in a donor-recipient (D-R) matching system was developed. This retrospective multicenter cohort study used 539 D-R pairs of cDCD livers recovered with NRP, including 20 donor, recipient, and NRP variables. The following machine learning-based classifiers were evaluated: logistic regression, ridge classifier, support vector classifier, multilayer perceptron, and random forest. The endpoints were the 3- and 12-mo graft survival rates. A 3- and 12-mo risk score was developed using the best model obtained. Logistic regression yielded the best performance at 3 mo (area under the receiver operating characteristic curve = 0.82) and 12 mo (area under the receiver operating characteristic curve = 0.83). A D-R matching system was proposed on the basis of the current model of end-stage liver disease score and cDCD-NRP risk score. The satisfactory performance of the proposed score within the study population suggests a significant potential to support liver allocation in cDCD-NRP grafts. External validation is challenging, but this methodology may be explored in other regions.

Assessment of the JET ICRH system performance since 2000

Abstract The paper provides an assessment of the ion-cyclotron resonance heating (ICRH) system performance on JET since the year 2000. The vast amount of collected data offer an insight into the historical challenges and trends in the ICRH system performance encompassing the transition from carbon (JET-C) to beryllium & tungsten ITER-like wall (JET-ILW) operations, the deuterium–tritium experiments (DTE2 & DTE3) and introduction of new RF antenna & matching systems. The best achieved operational parameters are reported and statistics on the RF plant reliability and performance is analysed. Antenna-plasma coupling is identified as the dominant factor critical to all the aspects of the ICRH system behaviour; parametric dependencies of coupling resistance on plasma parameters and the RF plant settings are discussed and the key role of local electron concentration profiles close to the antennas is highlighted. Following confident antenna performance at high RF voltages over the recent decade, observations are presented suggesting improved electrical strength of the RF vacuum components after the JET-C to JET-ILW transition; this is tentatively attributed to the reduction of dust levels in the JET vessel. Statistics on application rate and typical origins of the RF amplifier failures and protection power limits is presented indicating that the amplifier issues noticeably affected the high-power ICRH operations. Performance comparison is provided for different RF antenna & matching systems installed at JET since 2000 including the original system, two load-tolerant systems based on the 3 dB hybrid and external conjugate-T power-splitters, and the ITER-like antenna. The paper could be of interest both as a summary of technical challenges, constraints and achievements related to the ICRH application on JET and as a reference for design and operations of high-power RF systems in future fusion devices.

Multi-target matching implemented with a single serial all-optical sequence matching system

Multi-target matching implemented with a single serial all-optical sequence matching system

Map-Matching Algorithm Based on Hidden Markov and Constraint Value Pruning

Map matching is the process of matching global positioning system (GPS) trajectory data with map data. Its purpose is to determine the actual route of the moving object. Because of factors such as positioning devices and the environment, the GPS trajectory data obtained may not be accurate. In location-based services, map matching can be used to address the accuracy and reliability issues of GPS location data. There are currently many map-matching algorithms, for example, spatio-temporal-based (STD) matching algorithm, improved interactive voting-based map-matching (IIVMM) algorithm, and turning-point-based (TPB) offline map-matching algorithm, but existing algorithms have some shortcomings, such as low matching accuracy on complex roads or low sampling rates, and routing calculation time bottlenecks. Therefore, this paper proposes a fast matching algorithm based on constrained value pruning that is suitable for complex roads. The algorithm considers the multi-directional features within the road and uses secondary calculations to determine candidate points, improving the accuracy of candidate point selection. Additionally, two pruning strategies based on constrained values are introduced to reduce the majority of the routing calculation process and improve the matching efficiency. Finally, comparative experiments are conducted on a real trajectory dataset. The results show that, compared with STD, IIVMM, and TPB algorithms, the algorithm accuracy is improved by about 2% to 4%, and the running time is reduced by about 30%.

The Digital Boss: Algorithms, Housekeeping Apps, and the Digitalization of Domestic Work in Colombia

Domestic work in Colombia has historically been undervalued and unpaid, with informality continuing to rise despite legal efforts to dignify it, such as the ratification of ILO C189. In this context, domestic work gig platforms have emerged as a source of formal employment. Unlike most gig economy models, these platforms operate as agencies that directly hire workers, ensuring key conditions like stable income and social security. However, gaps remain in understanding the algorithmic design of these platforms. Through an ethnographic study of one domestic work platform interface called Hogaru and semi-structured interviews with workers and customers, we examine algorithmic governance and its impact on domestic workers’ experiences, particularly concerning employment and social relations. Our research focuses on the platform’s matching system, ranking, and pricing mechanisms. We conclude with implications for designing more effective and equitable platforms, specifically tailored to the unique context of Latin America.

Intelligent Employer Matching System for Young Professionals and Students Based on Multifactor Analysis

The article discusses the development of an intelligent employer matching system based on multifactor analysis. The system uses a career test that evaluates 40 factors, divided into four categories: career development, work environment, social and corporate benefits, and personal values and lifestyle. Machine learning algorithms account for the interconnections between attributes and provide personalized recommendations for students and young professionals. The test results are compared with employer profiles, created based on feedback from company employees. The system is planned to integrate with platforms that scrape job postings from popular websites like dou.ua, which will increase its relevance. Implementing this system will help optimize the employment process and facilitate more accurate matching of specialists to the labor market's needs.

Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching

AbstractIn the evolving landscape of smart cities, employment strategies have been steering towards a more personalized approach, aiming to enhance job satisfaction and boost economic efficiency. This paper explores an advanced solution by integrating multimodal deep learning to create a hyper-personalized job matching system based on individual personality traits. We employed the First Impressions V2 dataset, a comprehensive collection encompassing various data modalities suitable for extracting personality insights. Among various architectures tested, the fusion of XceptionResNet with BERT emerged as the most promising, delivering unparalleled results. The combined model achieved an accuracy of 92.12%, an R2 score of 54.49%, a mean squared error of 0.0098, and a root mean squared error of 0.0992. These empirical findings demonstrate the effectiveness of the XceptionResNet + BERT in mapping personality traits, paving the way for an innovative, and efficient approach to job matching in urban environments. This work has the potential to revolutionize recruitment strategies in smart cities, ensuring placements that are not only skill-aligned but also personality-congruent, optimizing both individual satisfaction and organizational productivity. A set of theoretical case studies in technology, banking, healthcare, and retail sectors within smart cities illustrate how the model could optimize both individual satisfaction and organizational productivity.

Design and simulation analysis of electromagnetic gun feeding and conveying device

Abstract The electromagnetic gun is a new type of weapon equipment powered by electric energy. Compared with the traditional artillery, the electromagnetic gun has the obvious advantages of high and controllable initial velocity, no explosive in the bore, and high safety. It is a new concept weapon that will be developed in the future. At present, there are many researches on the launching mechanism of electromagnetic gun, but the related research on the matching ammunition supply system is not yet mature. Therefore, in this paper, aiming at the characteristics of electromagnetic gun without propellant, through the comparison and selection of various ammunition supply system schemes, the ammunition supply scheme of horizontal magazine was determined, and the structural design and strength check were carried out to complete the model of each component and the overall assembly of ammunition supply system. After that, the finite element method was used to check the strength of the sprocket, and the dynamic simulation analysis of the ammunition supply system was carried out, so as to complete the design work of the whole ammunition supply system. This paper provides a certain reference for the development of electromagnetic gun feeding system.

Evaluating Post-Treatment Quality of Life in Cervical Cancer Patients in Tripura, India

Background: In India, cervical cancer (CC) is the most common gynecological cancer. This harms the patient's physical and psychological health, lowering their quality of life (QOL). The study aimed to compare QOL of healthy women and cervical cancer survivors in Tripura, India. Materials and Methods: With a propensity score matching system, this cross-sectional study compared 384 healthy women with 384 cervical cancer survivors. In order to assess sexual functioning and quality of life, we administered three questionnaires: the EORTC QLQ-C30, the EORTC QLQ-CX24 (the cervical cancer module), and the sociodemographic and clinical record form. Result: Cervical cancer patients had a 64.67±2.68 overall quality of life and global health status. We discovered no statistically significant differences in age, education, economic position, marital status, first pregnancy, or residency between cervical cancer survivors and controls (p 0.05). Cervical cancer survivors reported higher lymphoedema scores than healthy women (p = 0.04). The EORTC QLQ-C30 and CX24 showed significant differences in cognitive ability (p = 0.01) and constipation (p = 0.03) between the two groups. Conclusion: Research indicates that many cervical cancer survivors can maintain an acceptable quality of life despite their condition. Chemoradiotherapy significantly impaired the sexual functioning of cc survivors in comparison to a healthy control group.

Suitability matching evaluation of the low-carbon regional integrated energy system based on the “resource-demand” balance

The integrated energy system (IES) stands out due to its advantages of multi-energy complementarity, green and efficiency. This paper aims to provide a practical model for suitable region site selection of low-carbon regional IES based on the “resource-demand” balance. A “resource-demand” suitability matching analysis indicator system is established. By using prospect theory and variable precision rough set theory, the decision-making preferences and the cognitive fuzziness influence of expert scoring were corrected respectively, and reasonable subjective weights were obtained. Then, the objective weights are obtained using the entropy method, and the optimal combined weights are obtained through the game theory weighting method. Finally, this paper establishes a “resource-demand” suitability matching model consisting of three sub models: similarity matching degree, numerical matching degree, and suitability matching degree. An empirical study is conducted on 16 regions in Beijing, and the results show that the numerical matching degree of “resource-demand” gradually decrease from the central urban regions to the surrounding regions. The similarity matching degree in most regions reaches the highest range (0.776,1.00). There are 4 regions with a suitability matching rating of perfect, 7 regions with a rating of great, 1 region with a rating of good, and 4 regions with a rating of poor. The suitability matching model established in this paper can guide energy project decision-makers to select regions with greater potential for the development of IES. In addition, adjusting the similarity matching coefficient and numerical matching coefficient can meet the diversified needs of decision-makers for the similarity matching degree and numerical matching degree of the “resource-demand” balance, providing support for energy project planning decisions.

Moving toward automated µFTIR spectra matching for microplastic identification: addressing false identifications and improving accuracy

Infrared spectroscopy is a widely used tool for studying microplastics and identifying microparticles. Researchers rely on spectral libraries to differentiate between synthetic and natural materials. Unfortunately, spectral library matching is not perfect, and best practices require researchers to use time consuming, manual peak matching to assess spectral matches. Moving toward automated matching requires increased confidence in the matching process. Using spectra matching software may increase the efficiency of particle identification, however some matching strategies may confuse natural materials such as cotton, silk, and plant matter with common classes of synthetics such as polyesters and polyamides. In this experiment, we prepared 22 pristine sample materials from natural and synthetic sources and measured micro-Fourier transform infrared (µFTIR) spectra in transmission mode for each sample using a Thermo Nicolet iN10 MX instrument. The collected spectra were then input into two spectral library matching systems (Omnic Picta and Open Specy), using a total of five identification routines. Next, we placed a subset of four pristine microplastic materials in a biologically active river system for two weeks to simulate environmental samples. These simulated environmental samples were processed using 10% hydrogen peroxide for 24 h to remove organic contamination and then identified using the strongest performing library. We found that libraries with fewer sample spectra produced lower correlation matches and that using derivative correction greatly reduced the number of inaccuracies in identifying materials as either natural or synthetic. We also found that environmental fouling reduced the correlation value of library matches when compared to pristine particles, however the effect was not consistent across the four materials tested. Overall, we found that the accuracy of automated library matching in the tested systems and processing routines varied from 64.1 to 98.0% for distinguishing between natural and synthetic materials, and that a high Hit Quality Index (HQI) did not always correlate with accuracy. These results are important for the microplastic field, demonstrating a need to rigorously test spectral libraries and processing routines with known materials to ensure identification accuracy.

Navigating Dividend Decisions: The Impact of Outsider CEOs in Imputation Tax Environments

This study examines the influence of outsider CEOs on corporate dividend policies, specifically within the framework of an imputation tax environment, like in Australia. The study analyses 9826 firm-year observations from Australian firms listed on the ASX from 2012 to 2021. The study reveals that outsider CEOs tend to distribute lower dividend payouts; a trend observed even after robustness tests. Methodologically, the study addresses potential endogeneity issues through sophisticated methods like propensity score matching, difference-in-difference approach, and two-stage system generalized method of moments. The findings indicate that firms led by outsider CEOs, particularly those with specialized industry knowledge, are more inclined to invest undistributed profits in capital expenditure projects, reflecting an investment-oriented strategy. This research contributes significantly to understanding the strategic decision-making of outsider CEOs and their impact on dividend policies in specific tax environments.

Helical-Sense Matching Facilitates Supramolecular Copolymerization of Helical-Chiral Pillar[5]arenes.

Supramolecular polymerization using two-dimensional π-conjugated chiral monomers has been mainly demonstrated because the supramolecular polymerization can be controlled by stereocommunication through π-π stacking between the two-dimensional chiral monomers. We herein report supramolecular copolymerization utilizing three-dimensional pentahedrons with twisted helical chirality through different combinations of helical-chiral acidic and basic pillar[5]arenes as comonomers. In this case, helical-sense matching is key to facilitating the supramolecular copolymerization. Based on the unique helical chirality of the three-dimensional pillared structure of the pillar[5]arenes and alternate ion-pairing interactions between acidic and basic groups on their bilateral rims, the homochiral helical-sense matching system forms kinetically stable nanowire-shaped supramolecular copolymers, generating the supramolecular gel in high concentrations. At elevated temperatures, the nanowire structure undergoes a transformation into thermodynamically stable nanoparticles, resulting in a gel-to-sol transition. This process can be hindered by introducing linear guest molecules, which prohibit the unit swing of pillar[5]arenes and stabilize the nanowires and supramolecular gel. By tailoring the enantiomeric ratio (e.r.) values of the chiral combinations, the helical-sense-dependent gel-to-sol transition was realized, specifically by decreasing the e.r. values. Because of helical-sense mismatching, the heterochiral system generates short, branched nanowires and presents as a turbid solution. These distinct differences reveal that the helical-sense matching between three-dimensional chiral pillar[5]arene comonomers is important for supramolecular copolymerization.

Investigating the suitability of the matched fiber Bragg grating approach for guided wave based structural health monitoring

Fiber Bragg grating (FBG) sensors are thought to be ideal sensors for structural health monitoring (SHM). Amplitude based techniques such as matched filters and the edge-filtering have been proposed to fulfill the high sampling rates necessary for guided waves (GW) sensing. The current research for the first time shows the inherent robustness the matched filter technique provides to the sensing system. The matched system is realized through a FBG deployed on a low cost micrometer screw gauge which allows flexibility in matching the reflected wavelengths of the FBGs. Based on the results it is shown that, in the remote configuration the matched FBG approach is robust under changing temperature conditions. This concept has been shown analytically and experimentally. Furthermore, the approach is then extended for multiplexing of the sensors and the deployed system is used for detecting and localizing damage in the plate.

Exploring the Link Between Roommate Compatibility and Academic Outcomes: A Systematic Review of Personality-Based Matching Systems

The collegiate experience significantly influences students’ academic, personal, and social development. Roommate relationships, a central part of shared living arrangements, are crucial for students’ psychosocial well-being and academic success. This systematic review aims to evaluate the effectiveness of personality-based roommate matching systems in improving roommate compatibility, fostering personal development, and enhancing academic performance. A comprehensive search of the Google Scholar and Scopus databases was conducted to identify studies on automated and personality-based systems. Selected studies were screened and analyzed to assess the impact on resource allocation, student satisfaction, and housing efficiency. Personality-based systems - such as fuzzy clustering models, genetic algorithms, and rule-based algorithms - significantly outperform traditional methods in enhancing compatibility and resource allocation. Findings highlight the positive impact of roommate compatibility on students’ self-confidence, communication, and independence, with female students facing unique challenges. Positive relationships, especially among students with high extraversion and conscientiousness, correlate with improved academic outcomes, while negative interactions and depressive symptoms are linked to lower performance and well-being. Personality assessments, like the Open Four Temperaments Scales Test, show promise in reducing conflicts and increasing satisfaction. The study suggests that integrating personality profiling with technology can provide supportive living environments in higher education. However, gaps remain in understanding their impact across different institutional and cultural contexts. Further research is needed to explore the long-term efficacy of these systems. This review provides insights for institutions seeking to optimize hostel management practices and support student development.

PRISM: Patient Records Interpretation for Semantic clinical trial Matching system using large language models

Clinical trial matching is the task of identifying trials for which patients may be eligible. Typically, this task is labor-intensive and requires detailed verification of patient electronic health records (EHRs) against the stringent inclusion and exclusion criteria of clinical trials. This process also results in many patients missing out on potential therapeutic options. Recent advancements in Large Language Models (LLMs) have made automating patient-trial matching possible, as shown in multiple concurrent research studies. However, the current approaches are confined to constrained, often synthetic, datasets that do not adequately mirror the complexities encountered in real-world medical data. In this study, we present an end-to-end large-scale empirical evaluation of a clinical trial matching system and validate it using real-world EHRs. We perform comprehensive experiments with proprietary LLMs and our custom fine-tuned model called OncoLLM and show that OncoLLM outperforms GPT-3.5 and matches the performance of qualified medical doctors for clinical trial matching.

Ontology and its applications in skills matching in job recruitment

In the recruitment process, manually selecting suitable candidates from curriculum vitae (CVs) for a job description (JD) is both time-consuming and expensive. Traditional keyword-based methods struggle to capture skill semantics, prompting the development of more advanced JD-CV matching systems. This paper aims to investigate and construct an ontology-based skills recommendation system, with objectives including creating a skills ontology and developing skills matching methods for JD-CV pairs. The objective of our approach is to enhance the accuracy and contextual relevance of recommendations by utilizing the proposed score. The proposed skills ontology and skills matching strategies are applied to a real dataset in Vietnam. The results of our study can automatically recommend a list of CVs for a given JD. Furthermore, the findings indicate that our proposed model surpasses comparative approaches by a margin of at least 1% to 5%. Overall, the study demonstrates the potential of utilizing ontology-based approaches to offer a practical solution for enhancing hiring practices.

Élő method based mobile TV recommendation system

AbstractStreaming services spread rapidly. Among these services there are the linear TV, video library or program review system, while the online platform offering these contents is called mobile TV. A recommendation system may not only keep existing clients, but may also generate further turnover, should it introduce new content to the users. In this paper a recommendation system based on the Élő point calculation method is addressed. It is detailed how the programs should be grouped into different dimensions and what type of categories should be considered. Further, the idea of punch cards is introduced. Besides, the user profiles are set. The match system introduced by Élő is applied to the present situation. The system is introduced at a local mobile TV provider with 20,000 users.

Microchannel percutaneous nephrolithotomy versus flexible ureteroscopic lithotripsy for the treatment of 1-2cm high hardness single upper ureteral stones: a propensity score-matched study.

The objective of this study was to compare the clinical efficacy and safety of microchannel percutaneous nephrolithotripsy (MPCNL) with flexible ureteroscopic lithotripsy (FURL) in the treatment of single upper ureteral stones with a diameter of 1-2cm and high hardness. This study retrospectively analyzed 89 patients diagnosed with a single upper ureteral stone with a 1-2cm diameter and a computed tomography value > 1000 Hounsfield units. A propensity score matching system matched this study with factors to minimize the effect of baseline differences between patients. Ultimately, 29 patients in each of the two groups were successfully matched. The stone-free rate was marginally higher in the MPCNL group than in the FURL group (93.10% vs. 86.21%), although the difference did not reach statistical significance (P = 0.666). Furthermore, the mean operative time in the MPCNL group, although slightly longer than that in the FURL group, did not demonstrate a statistically significant difference (P = 0.833). However, patients in the MPCNL group exhibited a significantly more substantial decrease in hemoglobin than those in the FURL group (P < 0.001) and a substantially more extended postoperative hospital stay (P < 0.001). Regarding perioperative complications, the incidence of moderate pain was higher in the MPCNL group than in the FURL group (P = 0.037). The difference in overall complication rates between the two groups did not reach statistical significance (P = 0.108). MPCNL and FURL are efficacious surgical procedures for treating single upper ureteral stones with a 1-2cm diameter and high hardness.