Matching Process Research Articles

Distributed Reputation-Distance iterative auction system for Peer-To-Peer power trading

Peer-to-peer energy trading is a promising approach for decentralized electricity markets to achieve the decarbonization of power. In the trading process, the misbehavior of contracted users and power loss in network delivery can lead to transaction failure and higher delivery costs. This paper presents a distributed reputation-distance-driven iterative auction mechanism to promote energy trading among users who commit contracts well and among electrically closer peers. A reputation-distance index is built based on the historical trading performance and the electricity distance between users. The index is introduced into the auction matching process. Then, a self-adaption algorithm based on game theory is used to model the iterative auction to arrive at a Nash equilibrium. This paper also extends the combined factors framework to improve the flexibility and extensibility of the mechanism. A case study validates that the proposed decentralized mechanism can reduce network loss and peak loads, ensure power reliability, achieve high market efficiency, social welfare. Specifically, compared with the traditional model without considering reputation and distance, the proposed decentralized mechanism can reduce network loss by 50%, increase 7.1% the market share of users with good reputations, improve market efficiency by 10%, and increase social welfare by 7.69%.

REAL WORLD AND MENDELIAN RANDOMIZATION ANALYSES OF THE IMPACT OF AUTOIMMUNE DISEASE ON CLINICAL CHARACTERISTICS IN LYMPHOMA

Background: Previous studies have shown associations between the risk of lymphoma and autoimmune diseases (AIDs). Mendelian randomization (MR) can estimate causal effects of a modifiable exposure on outcomes. Herein, we estimated causal effects of AID on lymphoma using MR and analyzed clinical signatures in patients of AID-associated lymphoma. Methods: A two-sample MR framework was conducted to estimate the causal effect of genetic liability for AID on lymphoma. We enrolled in patients with a history for at least one year of AID, who diagnosed with lymphoma at Shandong Provincial Hospital. For each lymphoma case, 1 lymphoma-free AID individual was identified as a control. Measures of relative risk were calculated using conditional logistic regression. Results: Strong evidence of a potential causal effect of AID on the risk of lymphoma was found (p = 0.040). Slopes of MR regressions and causal estimates associated with each SNP are illustrated (Figure 1). In the examination of the association between single autoimmune disease and lymphoma, the only potential causal effect of Sjögren syndrome (SS) was detected (p < 0.01). There was no heterogeneity, and these effects were robust to various MR assumptions. 55 cases of lymphoma were collected and matched to 110 controls randomly selected. The number of patients according to AID lymphoma subtypes were described using heatmap (Figure 2). The median age at AID was 60 years, and the median lymphoma-diagnosing age was 40 years. The clinical course from AID to lymphoma varies considerably, with a median time of 10 years. There was a female predominance (n = 29, 52.7%). Cases with a history of rheumatoid arthritis (RA) accounted for 32.73% (n = 18), followed by psoriasis (PsO) (n = 9). Baseline characteristics of patients and controls did not significantly differ in sex, age at AID onset, age of matching time or duration of AID. Cohorts as generated by the matching process were balanced. The most represented subtype was NHL (n = 45), of which 15 were DLBCL. Among those patients, 7 were GCB type and 1 was non-GCB type. According to incomplete IHC results, 23/28 patients expressed CD20. Ki67 index of 15 patients was greater than or equal to 60%. Median follow-up for surviving patients was 49.5 months. The 1-, 3-, and 5-year OS rates were 72.5%, 45%, and 22.5%, respectively. On univariate analysis, factors including sex, type of AID, age at AID/lymphoma and disease duration were not related to lymphoma risk. The assessment of ORs for the history of RA and PsO revealed no significantly increased risks for lymphoma (OR 1.027 for RA, 1.022 for PsO). Keyword: aggressive B-cell non-Hodgkin lymphoma No conflicts of interests pertinent to the abstract.

Deep Fuzzy Clustering and Deep Residual Network for Prediction of Web Pages from Weblog Data with Fractional Order Based Ranking

Web page recommendation system has attracted more attention in recent decades. The web page recommendation has various characteristics than the classical recommenders. It is the process of predicting the request of the next web page that users are significantly interested while searching the web. It helps the users to find relevant pages in the field of web mining. In particular, web user may spend more time to identify expected information. To understand behavior of users and to visit the page based on their interest at a specific time, an effective web page recommendation method is developed by developed Multi-Verse Sailfish Optimization (MVSFO)-based Deep Residual network. Accordingly, proposed MVSFO is derived by the integration of Multi-Verse Optimizer (MVO) and Sailfish Optimizer (SFO), respectively. Here, the process of recommendation is carried out using weblog data and the web page image. The sequential patterns are acquired from weblog data, and the patterns are grouped with Deep fuzzy clustering based on cosine similarity. The matching process among test pattern and sequential patterns are made using Canberra distance. Here, the recommended web pages obtained from the weblog data and pages obtained from web pages image using the Deep Residual network are enable to generate the output using fractional order-based ranking. The developed scheme attained more effectiveness by the measures, such as F-measure, precision, and recall as 85.30%, 86.59%, and 86.04%, respectively for MSNBC dataset.

LINKS. A System for Historical Family Reconstruction in the Netherlands

LINKS stands for 'LINKing System for historical family reconstruction' and is a software system to link nominal data from the Dutch archives and ultimately reconstruct historical individuals and families. We present the background and philosophy of this matching system and explain its data structure and functioning. Currently the core data of the LINKS system consists of indexed civil certificates. These certificates are available from 1812 — the start of the Dutch Vital Registration — until the year they are confidential based on privacy laws. For more than 20 years, thousands of volunteers have been working to build this index, which contains not only the names of newborn, married and deceased persons, but also the names of their parents, places of birth, ages and sometimes their occupational titles. The software system LINKS includes the standardization of all input before linking, nominal record linkage procedures and identification of all unique persons involved in the system. All processes are repeatable and a strict distinction is maintained between source data, standardized, linked and enriched data and released data. Moreover, LINKS also informs archives about all kinds of errors and inconsistencies found during the cleaning and matching process. We will discuss two matching systems, the first is the original querying system that runs within a MySQL database environment and the second is a newly developed system, called burgerLinker, which is based on knowledge graphs and which is designed as a system that can be used independently from LINKS and is made available as open source software. Finally, we present the most important releases of LINKS data so far: two national releases that link birth and parental marriage certificates, creating families and pedigrees and an integrated dataset of persons, families and family trees in four provinces.

Burning Matches: Arguments for Improving the Match Process for Physician Trainee Couples.

Burning Matches: Arguments for Improving the Match Process for Physician Trainee Couples.

Recent Trends and Future Directions for the Integrated Plastic Surgery Match.

The integrated plastic surgery residency match has risen to be the most competitive specialty in the 2022 match. This reality has prompted medical students to reach a high level of personal achievements, including pursuing research fellowships to boost research productivity. The competitive nature of this specialty has highlighted several barriers for applicants, such as those from groups underrepresented in surgery, of lower socioeconomic backgrounds, or without a home program. In recent years, there have been several changes to the match that stand to attenuate disparities among applicants, such as the transition to virtual interviews and the shift of the United States Medical Licensing Examination Step 1 score to pass-fail. The introduction of the Plastic Surgery Common Application and standardized letters of recommendation has altered the application process for the plastic surgery match. Given these recent trends, evaluating the current landscape and looking toward future directions for the integrated plastic surgery match becomes necessary. Understanding these changes will not only benefit medical students by giving them a transparent look into the match process but also provide a framework for other specialties to follow to increase accessibility to their specific specialty.

Integrating modeled environmental variability into neural network training for underwater source localization.

Supervised machine learning (ML) is a powerful tool that has been applied to many fields of underwater acoustics, including acoustic inversion. ML algorithms depend on the existence of extensive labeled datasets, which are difficult to obtain for the task of underwater source localization. A feed-forward neural network (FNN) trained on imbalanced or biased data may end up suffering from a problem analogous to model mismatch in matched field processing (MFP), that is, producing incorrect results due to a difference between the environment sampled by the training data and the actual environment. To overcome this issue, physical and numerical propagation models can act as data augmentation tools to compensate for the lack of comprehensive acoustic data. This paper examines how modeled data can be effectively used for training FNNs. Mismatch tests compare the output from a FNN and MFP and show that the network becomes more robust to various kinds of mismatches when trained on diverse environments. A systematic analysis of how the training dataset's variability impacts a FNN's localization performance on experimental data is carried out. Results show that networks trained with synthetic data achieve better and more robust performance than regular MFP when environment variability is taken into account.

The Influence of Anti-LGBTQIA+ Legislation on Graduate Medical Education

The Influence of Anti-LGBTQIA+ Legislation on Graduate Medical Education

Coupling between cathode and anode in hybrid charge storage

As a promising charge storage method, hybrid charge storage has a high energy density, high power density, and long cycle life due to its combination of the mechanisms of secondary batteries and electrochemical capacitors. However, the difference in the charge storage mechanisms of the cathode and anode and thus the strong coupling makes it impossible to match cathode and anode in all situations. Research has investigated cell configuration, material design, electrolyte composition, etc., for matching the cathode and anode of hybrid charge storage devices, but there is no complete understanding and analysis from an electrochemical perspective. To better guide and promote the development of hybrid charge storage, this study discusses the matching and coupling of the anode and cathode from the following aspects, using hybrid capacitors as a typical example and combining the analysis of mainstream electrochemical systems, strategies, and materials. First, the charge storage mechanism and the major problems involved in matching the cathode and anode, as well as the “self-matching” of potential and zero-voltage potential, are considered as the basis of coupling. Second, from the perspective of electrochemical behavior and potential range of electrodes, we analyze the conflicts and correlations in coupling between each match and discuss the problems and solutions faced in specific matching processes. Third, the problem of matching a practical but complex electrochemical system is analyzed from the perspective of the coupling relationship. Fourth, the design and development of hybrid charge storage, ideas for future research, and the use of machine learning for electrode matching and coupling are proposed. As a promising charge storage method, hybrid charge storage has a high energy density, high power density, and long cycle life due to its combination of the mechanisms of secondary batteries and electrochemical capacitors. However, the difference in the charge storage mechanisms of the cathode and anode and thus the strong coupling makes it impossible to match cathode and anode in all situations. Research has investigated cell configuration, material design, electrolyte composition, etc., for matching the cathode and anode of hybrid charge storage devices, but there is no complete understanding and analysis from an electrochemical perspective. To better guide and promote the development of hybrid charge storage, this study discusses the matching and coupling of the anode and cathode from the following aspects, using hybrid capacitors as a typical example and combining the analysis of mainstream electrochemical systems, strategies, and materials. First, the charge storage mechanism and the major problems involved in matching the cathode and anode, as well as the “self-matching” of potential and zero-voltage potential, are considered as the basis of coupling. Second, from the perspective of electrochemical behavior and potential range of electrodes, we analyze the conflicts and correlations in coupling between each match and discuss the problems and solutions faced in specific matching processes. Third, the problem of matching a practical but complex electrochemical system is analyzed from the perspective of the coupling relationship. Fourth, the design and development of hybrid charge storage, ideas for future research, and the use of machine learning for electrode matching and coupling are proposed.

Diversity in the Plastic Surgery Match: The Effect of Program Chair, Program Director, and Faculties' Race and Sex on Matched Applicants.

Prior studies have identified the lack of diversity within the field of plastic surgery (PS) and associated residency matches. In this study, the authors aimed to examine the effect of PS program chairs (PCs), program directors (PD), and faculty members' race and sex on PS applicants and matriculants within the same program. Deidentified applicant data from 2017 to 2021 submitted to the University of Wisconsin-Madison's integrated PS program were analyzed. Applicants' self-reported sex and race were collected. Integrated PS program websites were assessed to determine whether applicants matched the program. Race and sex of PCs, PDs, and faculty members were collected from each program's website. Overall, 79 integrated PS programs' websites and 1470 applicants were reviewed. The majority of PCs, PDs, and faculty members were male (87.3%, 81.0%, and 69.6%, respectively) and white (74.7%, 70.9%, and 68.8%, respectively). Most applicants were male (54.0%) and white (60.8%). For a given program, white PCs and PDs have 5 and 6 times the odds of matching an applicant of the same race ( P <0.001 for both). Having male PCs and PDs did not significantly increase the odds of matching an applicant of the same sex ( P =0.198 and 0.905, respectively). Although the field of PS has made great strides to close the sex gap within the residency match process, this study highlights ongoing racial disparities among nonwhite applicants.

Resume Parser Analysis Using Machine Learning and Natural Language Processing

Abstract: With the rise of online job application processes, submitting a resume has become easier than ever before. As a result, a larger number of individuals are impacted by this change. Many organizations still accept resumes by mail, which can cause challenges for their human resources departments. Sorting through a large number of applications to find the most suitable candidates can be a time-consuming process for these agencies. Job applicants submit resumes in a wide range of formats, including various fonts, font sizes, colors, and other design elements. Human resources departments are responsible for reviewing each application and selecting the most qualified candidate for the job. My suggestion is to incorporate natural language processing techniques into the project's parser to assist the human resources department or recruiting manager in analyzing the information provided in resumes. This approach can involve using keyword matching and other natural language processing methods to identify the most suitable candidates and obtain the most effective resumes. By doing so, the organization can improve its recruitment process and identify the best candidates for the job.

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era.

The Internet-of-Things (IoT) has been used with greater frequency to track peoples' daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

Matching by value congruence for high-quality mentoring: evidence from a student peer mentoring program

ABSTRACT University mentoring programs are often implemented as an instrument to support students’ personal development and successful transition to university. Scholars and practitioners alike emphasize the importance of matching for high-quality peer mentoring. However, mentoring theory and research lacks specificity regarding relevant characteristics and type of fit, as well as predictive power, as fit between mentors and protégés is often conceptualized as subjective perception of a conglomerate of various characteristics. In the present study, we address these issues by examining the extent to which deep-level similarity between mentors’ and protégés’ personal values (i.e. conservation, self-transcendence, openness to change, self-enhancement) predicts high-quality mentoring relationships in terms of protégés’ learning and growth and willingness to mentor 12 months later through role modeling. We tested the proposed mediation model in a prospective three-wave field study, using polynomial regression analyses and response surface analysis in a sample of 149 mentor-protégé dyads in a formal peer mentoring program at a German university. Results show that the impact of deep-level value congruence on the mentoring process varied depending on the value dimension, revealing self-enhancement as a key dimension. In this regard, deep-level congruence in self-enhancement values – independently rated prior to the mentoring relationship – positively predicted protégé’s learning and growth and willingness to mentor in the future through role modeling support. The implications of these findings, especially for the matching process, are discussed.

Competition in Ride-Hailing Service Operations: Impacts on Travel Distances and Service Performance

Competition in the ride-hailing market can influence traffic congestion and deteriorate the quality of service. A customer can request to be matched with a ride-hailing vehicle from their preferred company, which might not be the nearest vacant vehicle. This can increase both the customer’s matching and pick-up waiting time and the vehicle’s travel distance to the customer, and contribute to traffic congestion. Recent studies focus on the long-term competition effect by considering network equilibrium. In this work, we target a shorter timeframe and investigate how competition influences the passenger–driver matching process, the consequent vehicle travel to the customer, and more globally the system at the operational level. To this end, we propose a modeling and simulation framework based on the Macroscopic Fundamental Diagram (MFD). We apply the so-called M-model, a continuum approximation of the trip-based MFD. Compared with the accumulation-based approach, it explicitly monitors the remaining travel distance of all vehicles. We extend the mathematical M-model decomposition and focus on accurate dynamic estimation of trip lengths for the different vehicle states based on the immediate system state. For this, we suggest creating an additional proxy simulation framework replicating the demand requests and the service vehicle movements. We propose calibrating the matching function by sampling observations on a proxy grid network. Finally, we assess and compare different matching processes that define diverse competition scenarios: competition, cooperation, and competition with partial cooperation (coopetition). The cooperation scenario shows the best results in service performance.

Recruitment Policies, Job-Filling Rates, and Matching Efficiency

Abstract Recruitment intensity is important for the matching process in the labor market. Using unique linked survey–administrative data, we investigate the relationships between hiring and recruitment policies at the establishment level. Faster hiring goes along with higher search effort, lower hiring standards, and more generous wages. We develop a directed search model that links these patterns to the employment adjustments of heterogeneous firms. The model provides a novel structural decomposition of the matching function that we use to evaluate the relative importance of these recruitment policies at the aggregate level. The calibrated model shows that hiring standards play an important role in explaining differences in matching efficiency across labor markets defined as region/skill cross-products and for the impact of labor market policy, whereas search effort and wage policies play only a minor role.

SiamCMN: Jointing cycle memory and correlation network for Siamese object tracking

In heterogeneous scenes with latent non-deterministic states, template features determine the performance of the Siamese network-based trackers, however, background noise is easily introduced in the search area matching process, which causes the trackers lack sufficient discrimination. To solve this problem, this paper proposes an object tracking algorithm based on the cyclic memory module. Firstly, the extracted primary features are mapped to the cyclic spatial memory network, the spatial memory unit is used to store the interference components. On this basis, the cyclic structure is used to filter out the interference components and refine the template features. Then a channel correlation network is introduced into the proposed method, which can mine semantic complementary information and capture global contextual relevance by modeling the positive and the negative correlation between the channels. The experimental results on tracking benchmarks such as OTB50, OTB100, UAV123, VOT2018, GOT10k, and LaSOT have shown that the proposed method outperforms recent representative algorithms in tracking accuracy in challenging scenarios.

Robust H-K Curvature Map Matching for Patient-to-CT Registration in Neurosurgical Navigation Systems

Image-to-patient registration is a coordinate system matching process between real patients and medical images to actively utilize medical images such as computed tomography (CT) during surgery. This paper mainly deals with a markerless method utilizing scan data of patients and 3D data from CT images. The 3D surface data of the patient are registered to CT data using computer-based optimization methods such as iterative closest point (ICP) algorithms. However, if a proper initial location is not set up, the conventional ICP algorithm has the disadvantages that it takes a long converging time and also suffers from the local minimum problem during the process. We propose an automatic and robust 3D data registration method that can accurately find a proper initial location for the ICP algorithm using curvature matching. The proposed method finds and extracts the matching area for 3D registration by converting 3D CT data and 3D scan data to 2D curvature images and by performing curvature matching between them. Curvature features have characteristics that are robust to translation, rotation, and even some deformation. The proposed image-to-patient registration is implemented with the precise 3D registration of the extracted partial 3D CT data and the patient’s scan data using the ICP algorithm.

Effects of a mastitis J5 bacterin vaccination on the productive performance of dairy cows: An observational study using propensity score matching techniques

Inferring causal effects between variables when utilizing observational data is challenging due to confounding factors not controlled through a randomized experiment. Propensity score matching can decrease confounding in observational studies and offers insights about potential causal effects of prophylactic management interventions such as vaccinations. The objective of this study was to determine potential causality and impact of vaccination with an Escherichia coli J5 bacterin on the productive performance of dairy cows applying propensity score matching techniques to farm-recorded (e.g., observational) data. Traits of interest included 305-d milk yield (MY305), 305-d fat yield (FY305), 305-d protein yield (PY305), and somatic cell score (SCS). Records from 6,418 lactations generated by 5,121 animals were available for the analysis. Vaccination status of each animal was obtained from producer-recorded information. Confounding variables considered were herd-year-season groups (56 levels), parity (5 levels: 1, 2, 3, 4, and ≥5), and genetic quartile groups (4 levels: top 25% through bottom 25%) derived from genetic predictions for MY305, FY305, PY305, and SCS, as well as for the genetic susceptibility to mastitis. A logistic regression model was applied to estimate the propensity score (PS) for each cow. Subsequently, PS values were used to form pairs of animals (1 vaccinated with 1 unvaccinated control), depending on their PS similarities (difference in PS values of cows within a match required to be <20% of 1 standard deviation of the logit of PS). After the matching process, 2,091 pairs of animals (4,182 records) remained available to infer the causal effects of vaccinating dairy cows with the E. coli J5 bacterin. Causal effects estimation was performed using 2 approaches: simple matching and a bias-corrected matching. According to the PS methodology, causal effects of vaccinating dairy cows with a J5 bacterin on their productive performance were identified for MY305. The simple matched estimator suggested that vaccinated cows produced 163.89 kg more milk over an entire lactation when compared with nonvaccinated counterparts, whereas the bias-corrected estimator suggested that such increment in milk production was of 150.48 kg. Conversely, no causal effects of immunizing dairy cows with a J5 bacterin were identified for FY305, PY305, or SCS. In conclusion, the utilization of PS matching techniques applied to farm-recorded data was feasible and allowed us to identify that vaccination with an E. coli J5 bacterin relates to an overall milk production increment without compromising milk quality.

Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients

Atrial fibrillation (AF) is an independent risk factor that increases the risk of stroke 5-fold. The purpose of our study was to develop a 1-year new-onset AF predictive model by machine learning based on 3-year medical information without electrocardiograms in our database to identify AF risk in older aged patients. We developed the predictive model according to the Taipei Medical University clinical research database electronic medical records, including diagnostic codes, medications, and laboratory data. Decision tree, support vector machine, logistic regression, and random forest algorithms were chosen for the analysis. A total of 2,138 participants (1,028 women [48.1%]; mean [standard deviation] age 78.8 [6.8]years) with AF and 8,552 random controls (after the matching process) without AF (4,112 women [48.1%]; mean [standard deviation] age 78.8 [6.8]years) were included in the model. The 1-year new-onset AF risk prediction model based on the random forest algorithm using medication and diagnostic information, along with specific laboratory data, attained an area under the receiver operating characteristic of 0.74, whereas the specificity was 98.7%. Machine learning-based model focusing on the older aged patients could offer acceptable discrimination in differentiating the risk of incident AF in the next year. In conclusion, a targeted screening approach using multidimensional informatics in the electronic medical records could result in a clinical choice with efficacy for prediction of the incident AF risk in older aged patients.

Neurosurgery residency rankings: An assessment of Doximity’s survey-based data and objective program characteristics

BackgroundMedical students often consult residency program reputation rankings whilst creating residency program rank lists. Accurate assessment of a residency program remains a cumbersome task, primarily due to varying degrees of interpretation and bias. The Doximity Residency Navigator (DRN) is a tool used to assist medical students in making informed residency decisions whilst simultaneously increasing transparency in the residency match process. DRN is comprised of data that assist applicants research prospective training programs, including reputation rankings across specialties, resident and alumni satisfaction surveys, and objective statistics such as training program size and research output. ObjectiveThis study aims to evaluate the correlation between different Doximity rankings across neurosurgical residency programs. MethodsRankings and the relevant criteria (reputation, research output, and percentage of graduates that subspecialize) and program size for all neurological surgery residency programs in the United States were obtained from the 2020–2021 Doximity Residency Navigator. Independent correlation analyses were run to ascertain the relationship between reputation ranking and 1) program size, 2) research output ranking and 3) ranking of the percentage of graduates who subspecialize. ResultsThe reputation rankings of neurosurgical residency programs are determined, in part, by nomination surveys completed by board-certified physicians. A statistically significant correlation was observed between reputation ranking and 1) program size and 2) research output ranking. ConclusionReputation rankings of neurosurgical residency programs put forth by Doximity Inc. incorporate reputational data and other objective outcome criteria. A nuanced understanding of the Doximity rankings will facilitate its use by medical students and healthcare professionals.