Matching System Research Articles

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.

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.

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.

Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery.

Objective.The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity.Approach.We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity.Main results.The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75,p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers.Significance.We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.

Multistage data center cooling system for temperature gradation and matching

A prototype of a multistage cooling system with a cooling capacity of 20 kW is assembled and studied. The traditional single-stage system is split into two sub-systems according to the temperature level, which reduces the temperature difference and irreversible heat transfer loss, and further improves the system's energy efficiency. The system mainly features three modes including free-air cooling, refrigeration-based cooling and waste heat recovery, which can achieve efficient cooling while capable of recovering the waste heat to the surrounding heat users. The efficiency of the proposed cooling and heat recovery system was assessed by measuring its energy reuse effectiveness (ERE). The energy savings and environmental benefits of the system were analyzed across five cities in China, each representing different climates. Results indicate that the system achieved an ERE of 0.91 in Beijing, 38.9 % lower than the national average of the city. Additionally, an economic analysis showed a payback period of less than two years for the proposed system across all climates considered, which is 30 % shorter than that of a single-stage cooling system.

3D electromagnetic simulation of the coupling characteristics and double-stub Ferrite tuners impedance matching for EAST ICRH four-strap antenna

A program developed with COMSOL software integrates EAST four-strap antenna coupling with the double-stub Ferrite tuners (FT) impedance matching, obtaining physical quantities crucial for predicting the overall performance of the ion cyclotron resonance heating (ICRH) antenna and matching system. These quantities encompass S-matrix, port complex impedance, reflection coefficients, electric field and voltage distribution, and optimal matching settings. In this study, we explore the relationship between S-matrix, reflection coefficients, port complex impedance, and frequency. Then, we analyze the impact of Faraday screens placement position and transparency, the distance from the Faraday screen (FS) to the current straps (CS), the relative distance between ports, and the characteristic impedance of the transmission line on the coupling characteristic impedance of the EAST ICRH system. Finally, we simulate the electric field distribution and voltage distribution of the EAST ICRH system for plasma heating with double-stub FT impedance matching. Using optimized parameters, the coupling power of the ICRH system can be approximately doubled. The results present herein may offer guidance for the design of high-power, long-pulse operation ICRH antenna systems.

An Improved Point System for Cricket’s World Test Championship

With the advent of formats of limited overs in cricket like the One Day and Twenty-20 Internationals, test cricket, the longest-running version of the game, has witnessed a significant decline in popularity. The World Test Championship (WTC) was launched in 2019 by the International Cricket Council to reignite interest in test cricket. However, the initial point distribution system devised for the inaugural WTC had several shortcomings, which include allocating points based on the outcome of the entire series rather than the number of test matches played, the absence of rewards for teams winning matches away from home, failing to take into consideration the margin of victory (MOV) and not accounting for the relative strength of competing teams. Considering these shortcomings, this article proposes a new point system, thus providing an alternative to the existing one. The point system uses the Elo rating system, including factors such as home-field advantage, impact of the toss and MOV. Statistical analyses were conducted to validate the home-field advantage and toss impact. Subsequently, the proposed model developed was named the points won per match system. The team rankings in the WTC are established based on this proposed model and compared with the actual one.

Portability of IMRT QA between matched linear accelerators.

To determine if patient-specific IMRT quality assurance can be measured on any matched treatment delivery system (TDS) for patient treatment delivery on another. Three VMAT plans of varying complexity were created for each available energy for head and neck, SBRT lung, and right chestwall anatomical sites. Each plan was delivered on three matched Varian TrueBeam TDSs to the same Scandidos Delta4 Phantom+ diode array with only energy-specific device calibrations. Dose distributions were corrected for TDS output and then compared to TPS calculations using gamma analysis. Round-robin comparisons between measurements from each TDS were also performed using point-by-point dose difference, median dose difference, and the percent of point dose differences within 2% of the mean metrics. All plans had more than 95% of points passing a gamma analysis using 3%/3mm criteria with global normalization and a 20% threshold when comparing measurements to calculations. The tightest gamma analysis criteria where a plan still passed>95% were similar across delivery systems-within 0.5%/0.5mm for all but three plan/energy combinations. Median dose deviations in measurement-to-measurement comparisons were within 0.7% and 1.0% for global and local normalization, respectively. More than 90% of the point differences were within 2%. A set of plans spanning available energies and complexity levels were delivered by three matched TDSs. Comparisons to calculations and between measurements showed dose distributions delivered by each TDS using the same DICOM RT-plan file meet tolerances much smaller than typical clinical IMRT QA criteria. This demonstrates each TDS is modeled to a similar accuracy by a common class (shared) beam model. Additionally, it demonstrates that dose distributions from one TDS show small differences in median dose to the others. This is an important validation component of the common beam model approach, allowing for operational improvements in the clinic.

Analysis of Instruments for Implementing Intelligent Job Matching Models

Vocational high schools in Indonesia face challenges in producing graduates who meet the needs of the job market. The link and match policy along with the Job Matching system are proposed to enhance curriculum relevance to the workforce. The intelligent job matching model, which integrates machine learning technology, is expected to provide job recommendations aligned with graduates' competencies. Data analysis techniques used include qualitative and quantitative statistical analysis, encompassing Validity Analysis, Practicality Analysis, and Effectiveness Analysis. The intelligent job matching model has proven effective in assisting in matching vocational high school students' competencies with workforce demands in the industry. In the 2023 job fair, the success ratio of students invited for interviews until the employment process reached 53.13%, exceeding the Ministry of education and culture's target of 40%. This indicates that the model can provide job recommendations accurately enough, aiding in improving the direct employment rate of vocational high school students. Research indicates that the intelligent job matching model is effective in facilitating students' transition to the workforce. The model has been tested for validity, practicality, and effectiveness in enhancing students' employability and strengthening the relationship between education and industry.

Evaluating equity, diversity, and inclusion in Canadian Postgraduate Medical Education: A cross-sectional analysis of online content.

Medical graduates applying to Residency through the Canadian Resident Matching System (CaRMS) utilize the internet to gather information on programs and their overarching Postgraduate Medical Education (PGME) Office. This study aims to evaluate how PGME websites across Canada convey their commitment to equity, diversity, and inclusion (EDI) through their website features. Cross-sectional analysis of the 17 Canadian PGME websites against 20 EDI criteria based on contemporary literature, across five domains: leadership and governance, recruitment, accommodations, community engagement, and pathways to entry. Non-parametric testing was conducted to explore the relationship between EDI performance and municipal population diversity and geographic region. The evaluation of PGME websites, policies, reports, and plans revealed a mean score of 8.65/20 (SD = 3.00), with scores ranging from a minimum of 4/20 to a maximum of 13/20, indicating variability in EDI representation. Specifically, the domain of leadership and governance demonstrated the highest mean proportion of completed criteria (51%), while community engagement had the lowest (24%). Notably, 9 out of 17 PGME websites (53%) met at least 10 EDI criteria. Analysis by geographic region demonstrates significant mean differences (p = 0.02), with Ontario (10.50, SD = 2.17) and Western Provinces (11.00, SD = 0.00) scoring notably higher than Quebec (4.50, SD = 0.58), the Prairies (8.50, SD = 2.12), and the Atlantic region (8.00, SD = 2.83). The assessment of Canadian PGME websites reveals varying levels of commitment to EDI. While many programs exhibit strong EDI representation in mission statements, access to mental health services, and anti-discrimination policies, there are notable gaps in leadership messaging, diverse interview panels, family-friendly policies, and deliberate recruitment of underrepresented groups. Regional differences highlight the need for sharing best practices to promote inclusivity across the country. Improving EDI efforts on PGME websites can promote the recruitment and retention of a diverse resident population.

Tailoring age-friendly technology-enabled transportation service solutions in rural communities

Rural older adults requiring transportation assistance represent an historically underserved demographic, who often have greater needs for transportation services but may face challenges in benefiting from the latest transportation technological developments. This paper presents one of the first studies that aims to investigate the effective technology-enabled solutions that can cater to the diverse needs of rural older adults throughout the entire service process. The study first uses latent class analysis (LCA) structure model to identify three distinct groups of older adults with varying attitudes toward information and transportation technologies: (1) Class 1 – tech-challenged, moderate transportation tech adopters, (2) Class 2 – tech-savvy, modest transportation tech adopters, and (3) Class 3 – tech-savvy, enthusiastic transportation tech adopters. The estimated results from the LCA membership model demonstrated that Class 1 appear to constitute a more vulnerable population compared to their counterparts in Class 2 and Class 3, as they tend to be characterized by older age, lower household income, living alone, and more dependence on mobility aids. The study then delves into qualitative insights through focus group discussions with individuals balanced from these identified groups, revealing ten critical elements that encompass the entirety of service reservation, recommendation, scheduling, and reception. The findings suggest several tech-based solutions, including: (1) the establishment of a regional “one call does it all” center, (2) the implementation of a centralized ride matching and recommendation system, (3) the introduction of an automated scheduling system aligned with healthcare services, and (4) the development of a user-friendly portable device for service reception. These solutions aim to empower both technology and transportation service professionals in delivering age-friendly rural transportation services customized to the unique needs and limitations of older adults.

Navigating techniques in job recommender systems on internship profile matching: a systematic review

PurposeThis review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?Design/methodology/approachThere are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.FindingsCollecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.Research limitations/implicationsThe study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.Practical implicationsThe outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.Social implicationsInternship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.Originality/valueWhile surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.

Research on the optimize doctor-patient matching in China

In China, patients are given the opportunity to know background information about their doctors, and patients choose their doctors to see. Due to the shortage of medical resources and the uncertainty of the quality of medical services, as well as some external factors, patients will prefer to choose well-known doctors in the hope of getting a better medical experience. In order to match the preferred healthcare resources, they may choose to lie to increase the chance of visiting the doctor. This study adopts a combination of theoretical modeling and algorithmic simulation. Through theoretical analysis, a framework model of doctor-patient matching is established. Doctors with different skills and experience, who select patients according to their conditions and their own preferences in the matching process, as well as patients with different conditions and preferences, who want to receive treatment by matching to a suitable doctor, are clarified. The Deferred Acceptance algorithm is written and operated to simulate the matching process where patients apply to doctors and doctors are screened based on their priorities. Analyze and evaluate the performance of Strategy-Proofness and Pareto Efficiency in matching by iterating the algorithm. In this case, the DA algorithm establishes a stable match between the patient and the physician despite the possibility that the patient may deceive his/her preferences. However, patient behavior may affect the efficiency and fairness of the matching process, highlighting the importance of transparency and integrity of the doctor-patient matching system.

A Qualitative Inquiry into Nurse-Family Partnership Black Client Perspectives.

Nurse-Family Partnership (NFP) is a home visiting program designed to improve pregnancy outcomes, child health and development, and life course outcomes for families facing socioeconomic inequalities through support and education provided by nurses to first-time mothers during pregnancy and up to 2years postpartum. Studies show that home visiting programs like NFP have positive outcomes, but attrition remains a concern which may impact the desired health equity goals. Black mothers are more likely to withdraw from the NFP program, and research is lacking regarding their experiences in home visiting programs despite facing maternal health inequities rooted in racism. The present study aimed to understand factors that influenced program continuation and provide insights for program improvement. Semi-structured qualitative interviews were conducted with 21 Black NFP clients from multiple sites. Key findings include the importance of the nurse-client relationship, access to reliable health information, and racial concordance in the nurse-client pairing. Clients valued supportive, nonjudgmental nurses who provided dependable support and education. Racially concordant partnerships were perceived as more comfortable and understanding, fostering trust and open communication. Clients also suggested that invasion of privacy during home visits and a lack of connection with their nurse could contribute to program discontinuation. Efforts to increase program retention of Black clients should focus on fostering a strong nurse-client alliance. Recommendations include increasing racial diversity in the nurse workforce, implementing a nurse-client matching system, and allowing clients to request a new nurse if needed.

P4Rex: Accelerating regular expression matching with programmable switches

Regular expression matching is pivotal in numerous network applications. With the ever-increasing scale of data center traffic, deploying regular expression matching modules on traditional servers struggles to meet throughput demands. Emerging programmable switches have brought new prospects for high-speed pattern matching. However, deploying regular expression matching onto programmable switches presents the challenge of space explosion incurred by compiling regular expressions into a Deterministic Finite Automata (DFA). In this paper, we introduce P4Rex, a regular expression matching system designed for programmable switches. P4Rex synergistically leverages two following techniques: an efficient regular expression grouping algorithm that partitions regular expressions into different groups to reduce memory consumption, and DFA compression technology to achieve transition sharing. Experimental results demonstrate that P4Rex exhibits an average improvement of 17% and maximum improvement of 30% on memory consumption compared to prior regular expression grouping schemes and saves more than 10x memory consumption compared to deploying DFA directly on programmable switch.

New directions in pass-and-swap queues

Recently, driven by redundancy systems and matching systems, there has been renewed interest in models with product form stationary distributions. By a “product form,” we mean that the stationary distribution can be expressed as a product of terms, each of which corresponds to a job in the system. Given the recent discovery of many such systems, it is natural to ask: how broad is this class of systems? In this paper, we consider extensions and generalizations of the recently-proposed pass-and-swap queue, which has a product-form stationary distribution. We make three main contributions. First, we identify sufficient conditions under which pass-and-swap queues can be connected in a closed network, while still preserving the product form. Second, we identify dimensions along which the pass-and-swap system can be extended while preserving the product-form stationary distribution. At the same time, we also identify cases in which generalizing the pass-and-swap queue causes the product-form nature of the stationary distribution to break. Finally, we identify questions that remain open and present a road map for future study.

LaMMOn: language model combined graph neural network for multi-target multi-camera tracking in online scenarios

Multi-target multi-camera tracking is crucial to intelligent transportation systems. Numerous recent studies have been undertaken to address this issue. Nevertheless, using the approaches in real-world situations is challenging due to the scarcity of publicly available data and the laborious process of manually annotating the new dataset and creating a tailored rule-based matching system for each camera scenario. To address this issue, we present a novel solution termed LaMMOn, an end-to-end transformer and graph neural network-based multi-camera tracking model. LaMMOn consists of three main modules: (1) Language Model Detection (LMD) for object detection; (2) Language and Graph Model Association module (LGMA) for object tracking and trajectory clustering; (3) Text-to-embedding module (T2E) that overcome the problem of data limitation by synthesizing the object embedding from defined texts. LaMMOn can be run online in real-time scenarios and achieve a competitive result on many datasets, e.g., CityFlow (HOTA 76.46%), I24 (HOTA 25.7%), and TrackCUIP (HOTA 80.94%) with an acceptable FPS (from 12.20 to 13.37) for an online application.