Matching Results Research Articles

Outcomes of the Main Residency Match for Applicants With Disability

This cohort study assesses match rates of US applicants with and without disability into specialty residence programs.

The Influences of Ties in Preference on Market Matching

The ties of preference and priorities are commonly seen and widely used in daily life. When the agent considers the priority order of some selected objects to be the same, the final matching result may be affected. People sometimes require strict preference ranking in order to facilitate the use of matching models, which can cause controversy and dissatisfaction with people in the matching. In order to effectively solve the above problems, this paper surveys what these impacts are and how they affect the final result of matching. According to the analysis, it is proven that when there is a preference of indifference, Pareto efficiency is unstable and the deferred acceptance algorithm cannot be used for market matching. In addition, the Pareto-improving draft mechanism has proven to be powerful without imposing a strict preference. This mechanism can also avoid the complex process and other negative consequences of handing ties arbitrarily.

Augmented reality presentation system of skeleton image based on biomedical features

Aimed at limitations in the description and expression of three-dimensional (3D) physical information in two-dimentsional (2D) medical images, feature extraction and matching method based on the biomedical characteristics of skeletons is employed in this paper to map the 2D images of skeletons into a 3D digital model. Augmented reality technique is used to realize the interactive presentation of skeleton models. Main contents of this paper include: Firstly, a three-step reconstruction method is used to process the bone CT image data to obtain its three-dimensional surface model, and the corresponding 2D–3D bone library is established based on the identification index of the 2D image and the 3D model; then, a fast and accurate feature extraction and matching algorithm is developed to realize the recognition, extraction, and matching of 2D skeletal features, and determine the corresponding 3D skeleton model according to the matching result. Finally, based on the augmented reality technique, an interactive immersive presentation system is designed to achieve visual effects of the virtual human bone model superimposed and rendered in the world scenes, which improves the effectiveness of information expression and transmission, as well as the user's immersion and embodied experience.

Point-Sim: A Lightweight Network for 3D Point Cloud Classification

Analyzing point clouds with neural networks is a current research hotspot. In order to analyze the 3D geometric features of point clouds, most neural networks improve the network performance by adding local geometric operators and trainable parameters. However, deep learning usually requires a large amount of computational resources for training and inference, which poses challenges to hardware devices and energy consumption. Therefore, some researches have started to try to use a nonparametric approach to extract features. Point-NN combines nonparametric modules to build a nonparametric network for 3D point cloud analysis, and the nonparametric components include operations such as trigonometric embedding, farthest point sampling (FPS), k-nearest neighbor (k-NN), and pooling. However, Point-NN has some blindness in feature embedding using the trigonometric function during feature extraction. To eliminate this blindness as much as possible, we utilize a nonparametric energy function-based attention mechanism (ResSimAM). The embedded features are enhanced by calculating the energy of the features by the energy function, and then the ResSimAM is used to enhance the weights of the embedded features by the energy to enhance the features without adding any parameters to the original network; Point-NN needs to compute the similarity between each feature at the naive feature similarity matching stage; however, the magnitude difference of the features in vector space during the feature extraction stage may affect the final matching result. We use the Squash operation to squeeze the features. This nonlinear operation can make the features squeeze to a certain range without changing the original direction in the vector space, thus eliminating the effect of feature magnitude, and we can ultimately better complete the naive feature matching in the vector space. We inserted these modules into the network and build a nonparametric network, Point-Sim, which performs well in 3D classification tasks. Based on this, we extend the lightweight neural network Point-SimP by adding some trainable parameters for the point cloud classification task, which requires only 0.8 M parameters for high performance analysis. Experimental results demonstrate the effectiveness of our proposed algorithm in the point cloud shape classification task. The corresponding results on ModelNet40 and ScanObjectNN are 83.9% and 66.3% for 0 M parameters—without any training—and 93.3% and 86.6% for 0.8 M parameters. The Point-SimP reaches a test speed of 962 samples per second on the ModelNet40 dataset. The experimental results show that our proposed method effectively improves the performance on point cloud classification networks.

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

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

Meta-analysis of the effects of VAR on goals scored and home advantage in football

Video assistant referee (VAR) has been introduced to elite football (soccer) to avoid clear and obvious mistakes, especially for goal scoring situations. Some literature has reported on VAR’s impact on the game across particular league competitions. The current study meta-analysed data across two seasons (pre-VAR and post-VAR) from 20 competitions ( n = 9076 matches; men’s domestic leagues and both men’s and women’s international tournaments) to examine the overall impact that VAR initially had on number of goals scored, and the extent of inter-competition heterogeneity. A secondary aim was to determine any VAR-associated reduction in home advantage for goals scored, mean result direction and match closeness. Findings demonstrated that there were no overall statistically significant nor meaningful differences between pre and post-VAR seasons/competitions for total number of goals scored per match. Similarly, there were no VAR-associated reductions in home advantage in terms of goals scored, mean result direction and closeness of match outcome. There was moderate heterogeneity for some comparisons, and VAR lessened inter-competition variance for number of goals scored and match score closeness. Implications of findings are discussed. As the largest VAR-focused study and the first to use meta-analytic approach, the current findings are the most comprehensive and definitive to date.

History matching for geological carbon storage using data-space inversion with spatio-temporal data parameterization

History matching based on monitoring data will enable uncertainty reduction, and thus improved aquifer management, in industrial-scale carbon storage operations. In traditional model-based data assimilation, geomodel parameters are modified to force agreement between flow simulation results and observations. In data-space inversion (DSI), history-matched quantities of interest, e.g., posterior pressure and saturation fields conditioned to observations, are inferred directly, without constructing posterior geomodels. This is accomplished efficiently using a set of O(1000) prior simulation results, data parameterization, and posterior sampling within a Bayesian setting. In this study, we develop and implement (in DSI) a deep-learning-based parameterization to represent spatio-temporal pressure and CO2 saturation fields at a set of time steps. The new parameterization uses an adversarial autoencoder (AAE) for dimension reduction and a convolutional long short-term memory (convLSTM) network to represent the spatial distribution and temporal evolution of the pressure and saturation fields. This parameterization is used with an ensemble smoother with multiple data assimilation (ESMDA) in the DSI framework to enable posterior predictions. A realistic 3D system characterized by prior geological realizations drawn from a range of geological scenarios is considered. A local grid refinement procedure is introduced to estimate the error covariance term that appears in the history matching formulation. Extensive history matching results are presented for various quantities, for multiple synthetic true models, using the new DSI framework. Substantial uncertainty reduction in posterior pressure and saturation fields is achieved in all cases. The framework is also applied to efficiently provide posterior predictions for a range of error covariance specifications. Such an assessment would be very expensive using a traditional model-based approach.

Position and ranking influence in padel: somatic anxiety and self-confidence increase in competition for left-side and higher-ranked players when compared to pressure training.

This study aimed to analyze the differences in the precompetitive anxiety and self-confidence according to the side of play, the ranking and the match outcome, under different competitive scenarios, in high level men's padel players from Finland who trained under pressure prior to the competition. 10 men's padel players (28.60 (4.17) years old) from the highest category participated in the research. The CSAI-2R (Competitive State Anxiety Inventory-2 Revised) and STAI-S (State-Trait Anxiety Inventory - State) questionnaires were used and descriptive and inferential analyzes were performed, including Mann-Whitney's U tests. The findings illuminate that, across the player spectrum, somatic anxiety and self-confidence levels are higher before competition compared to training matches. This trend holds true for left-side, higher-ranked and match winning players. Even lower-ranked players exhibit heightened self-confidence preceding competitions. These insights offer valuable considerations for players, coaches, and sports psychologists, fostering a deeper understanding of the intricate interplay between pressure training, competition, and the athlete's psychological landscape.

Direction of Arrival Estimation of Coherent Sources via a Signal Space Deep Convolution Network

In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets.

Cricket Score Predictor using Machine Learning

In cricket, particularly the twenty20 format is most watched and loved by the people, where no one can guess who will win the match until the last ball of the last over. In India, The Indian Premier League (IPL) started in 2008 and now it is the most popular T20 league in the world. So we decided to develop a machine learning model for predicting the outcome of its matches. Winning in a Cricket Match depends on many key factors like a home ground advantage, past performances on that ground, records at the same venue, the overall experience of the players, record with a particular opposition, and the overall current form of the team and also the individual player. This paper briefs about the key factors that affect the result of the cricket match and the regression model that best fits this data and gives the best predictions. In these the score prediction includes linear regression, lasso regression and ridge regression whereas in winning prediction SVC classifier, decision tree classifier and random forest classifier are used. The model used the supervised machine learning algorithm to predict the winning. Random Forest Classifier used for good accuracy and the stable accuracy so that desired predicted output is accurate

End-to-end information fusion method for transformer-based stereo matching

In stereo matching, the application of transformers can overcome the limitations of disparity range and capture long-range matching information. However, the lack of cross-epipolar context information often leads to numerous mismatches, especially in low-texture regions. An end-to-end information fusion stereo matching method is proposed to address this issue. In the proposed method, a feature extraction method that combines dense connections and a residual block is proposed. Global and local semantic information can be effectively fused by incorporating dense connections among multiscale feature maps. Additionally, the inclusion of a residual block helps extract more representative feature maps. The idea of criss-cross attention is introduced in the transformer implicit matching process. Criss-cross attention enables the capture of cross-epipolar context information by combining horizontal and vertical attention mechanisms. This method improves the matching accuracy from the perspective of multi-path information fusion. According to the matching results, the disparity regression layer and the context adjustment layer are used to generate the initial and final disparity maps, respectively. The proposed method is evaluated on the Scene Flow, KITTI 2012, and Middlebury 2014 datasets. Experimental results indicate that the proposed method effectively enhances matching accuracy. Moreover, the proposed method exhibits strong generalization ability, allowing for direct application to synthetic, real outdoor, and real indoor scene images.

814 Results and Opinions on the Implementation of a Preference Signaling System in Neurosurgical Residency Applications

INTRODUCTION: The 2022-2023 neurosurgery residency application cycle employed an optional preference signaling system that indicated a focused interest in a particular program. During this first iteration, applicants could signal interest in up to eight participating programs. METHODS: A survey consisting of 28 questions pertaining to the signaling process was distributed via email to all neurosurgery applicants participating in the 2022-2023 cycle. Results were compiled, and descriptive statistics were used to generate results. RESULTS: Of the 361 total applicants, 82 (22.7%) completed the survey; 84.1% attend a US MD program, 1.2% a DO program, 12.2% are international medical graduates, and 2.4% are post-graduates. The mean number of programs applied to was 78 (range 21-116 (all). The median number of interviews received was 17 (range 0 to 57). Notably, 14.6% of individuals signaled their home programs, and 63.4% signaled at least one externship program. 90.2% of applicants utilized all eight signals. Applicants averaged 3.5 interviews from programs they signaled, and 15.6% of applicants matched at a program they signaled. Sixty-five percent of applicants were unsatisfied with the application process, but 53.1% indicated preference signaling should continue. Regarding the number of available signals, 43.75% felt eight was appropriate, 32.5% felt it was too few, 13.75% thought it was too many, and 10% were indifferent. CONCLUSIONS: Preference signaling was implemented in the 2022-2023 applicant cycle to improve interview efficiency and equity. Applicant opinions were mixed about this first iteration. Two consistently noted themes were the desire for improved home and away rotation signaling standardization and the perceived lack of impact on match outcome.

Influence of contextual factors on match running performance of starters and non-starters in elite youth male soccer players.

This study investigated the effects of match location, quality of opposition (classified into: strong [1st to 4th position]; intermediate [5th to 15th position]; weak [16th to 20th position]), and match outcome on the match running performance of starters and non-starters from a top elite Brazilian soccer team. Absolute measures were calculated using total distance, high-speed (19.8-25.2 km·h-1), sprinting (≥ 25.2 km·h-1), total distance high-acceleration (> 2m·s2), and deceleration (< -2m·s2) were recorded by GPS units from a sample of young soccer players (N = 25) in a total of 17 matches. Relative measures were calculated by dividing absolute measures by the total duration of the matches. Non-starters covered greater total distance (p = 0.02), sprinting (p = 0.02), high-acceleration and deceleration (p = 0.04), sprinting distance relative per minute played (p = 0.005), and high-acceleration and deceleration relative per minute played (p < 0.001) when the team plays at home, strong opponents, and wins the matches. Starters covered greater total distance in high-speed running (p = 0.04), high-acceleration and deceleration (p = 0.03), and high-speed running relative per minute played (p = 0.04) when the team plays strong opponents and wins the matches. These findings highlight the impact of contextual factors during matches on the locomotor performance of young soccer players.

Matching Against Men: 5 Years of Residency Match Data Show Disparities Still Exist.

Despite progress toward equal representation by sex in medical practice, women remain underrepresented in many specialties. This study sought to examine the current state of gender equality among recently graduated doctors in multiple specialties. Deidentified demographics, standardized examination scores, and Match results were gathered for 829 graduates. Participants were selected from an allopathic medical school between 2016 and 2020. Nineteen students (2.29%) were excluded from the study. Descriptive statistics were calculated, and χ2 tests for independence were used to compare proportions between reported sex and specialty and program Match results. One-way analysis of variance was then performed to test for differences in US Medical Licensing Examination Step 1 and Step 2 scores between sexes. P < 0.05 was considered statistically significant. Of the 829 individuals studied, 44.6% were women. A significantly smaller proportion of women matched into the most competitive specialties, despite no significant difference in US Medical Licensing Examination Step 1 scores between sexes. Furthermore, there was an overall significant trend of women matching into more competitive programs for any given specialty. In this study, we found that men matched into more highly competitive specialties, whereas women matched into more competitive residency program locations. Further research is needed to determine why women matched into specific specialties at lower rates than their male peers and seek to understand how sex affects the narrative of specialty choice.

Novel algorithm machine translation for language translation tool

AbstractFuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.

Research on the assignment of elderly care service personnel with time window constraints

As the population continues to age, there is a growing need for elderly care services. In China, home care is widely embraced as a preferred method of caring for the elderly, and it has a promising commercial outlook. The strategic allocation of Elderly Care Service Personnel (ECSP) is a crucial component of the operational procedures for home care services. By strategically assigning elderly service personnel, it is possible to enhance the satisfaction and simultaneously minimize expenses. The issue of rationalizing the assignment of ECSP in the face of restricted resources is a genuine challenge that requires a solution, given the many types of elderly service demands and the skill requirements and time limits of certain projects. This study presents a strategy for assigning personnel on an hourly basis, taking into account time frame limits. This method involves several steps. Firstly, it involves assessing the specific service needs of the elderly, including the required door-to-door service time, service level, and gender preferences. Secondly, it calculates the service satisfaction and service operation cost of the Home Care Service Platform (HCSP) separately. Finally, it constructs a multi-objective elderly service personnel assignment method. This method aims to minimize the ECSP 's travel time and wasted time, maximize the satisfaction of the elderly by considering priority levels and ECSP scores, and minimize the operation cost of the HCSP. A model is developed to assign ECSP for elderly individuals, with the goal of minimizing travel time and wasted time, maximizing elderly satisfaction by considering priority and service personnel rating, and minimizing operating costs for the HCSP. Additionally, if there are unserved elderly individuals, an optimized path model is constructed using a cross-modified genetic algorithm to obtain the optimal matching result. Ultimately, an arithmetic example is employed to demonstrate the practicality and efficiency of the strategy put forth in this research. The findings demonstrate that the model presented in this research possesses distinct advantages in comparison to other conventional models.

A Comparative Study of Feature Selection Technique for Predicting the Professional Tennis Matches Outcome in a Grand Slam Tournament

Tennis is one of the world's most played sports, attracting many spectators to participate in the game. One of the most essential strokes in a tennis match is serve performance. This research is intended to determine the most critical strokes in tennis serve performance in predicting the tennis match outcome. This research focuses on the Grand Slam Tournaments of the Australian Open, French Open, Wimbledon, and US Open. The data are collected on the tennis serve performances such as Percentage First Serve In (PFSI), Percentage First Serve Won (PFSW), Percentage First Serve Return Won (PFSRW), Aces, and many more. For one tournament, it consists of 254 observations. This study applied feature selection methods available in R programming, such as Correlation Matrix, Relative Importance Metrics, Boruta, MARS, and cForest. Selecting the most essential and correlated variables with the match status can improve the model and help produce better results. This might help the practitioners to apply this method to obtain the closest result to the actual outcome when we include the most correlated variables in the model. From the result obtained, variables of first and second serve, either win on serve or return serve, are identified as the most critical attributes in the tennis match. As a future implication, we suggest that these are all the factors the players need to pay extra attention to in winning the tennis match.

The effect of recycled fibers on shear properties of concrete at different ages and damage constitutive model based on 3-dimensional morphology analysis

To study the shear properties and damage constitutive model of plain concrete (PC) and nylon fiber recycled from waste reinforced concrete (RNFRC), the direct shear test under different normal stresses (σn, from 1 to 5 MPa) with a continuous horizontal rate of 0.5 mm/min is conducted on the cube specimens at different ages. The 3D roughness parameters of shear surface and the evolution of shear strength components are studied. Results show that the peak shear strength of RNFRC at 3, 7 and 28 d ranges from 6.65 to 12.09, 8.07–13.94 and 8.93–15.27 MPa under different σn, exhibiting varying degrees of enhancement compared to those of PC. The values of the 3D roughness parameter C gradually increase while Rs and the joint roughness coefficient (JRC) decrease with increasing σn. The evolution of shear strength components is affected clearly by σn. The calculation results of shear damage constitutive model match well with the experimental results.

Nature versus nurture: distinguishing effects from stellar processing and chemical evolution on carbon and nitrogen in red giant stars

ABSTRACT The surface [C/N] ratios of evolved giants are strongly affected by the first dredge-up (FDU) of nuclear-processed material from stellar cores. C and N also have distinct nucleosynthetic origins and serve as diagnostics of mixing and mass-loss. We use subgiants to find strong trends in the birth [C/N] with [Fe/H], which differ between the low-α and high-α populations. We demonstrate that these birth trends have a strong impact on the surface abundances after the FDU. This effect is neglected in current stellar models, which use solar-scaled C and N. We map out the FDU as a function of evolutionary state, mass, and composition using a large and precisely measured asteroseismic data set in first-ascent red giant branch (RGB) and core He-burning, or red clump (RC), stars. We describe the domains where [C/N] is a useful mass diagnostic and find that the RC complements the RGB and extends the range of validity to higher mass. We find evidence for extra mixing on the RGB below [Fe/H] = −0.4, matching literature results, for high-α giants, but there is no clear evidence of mixing in the low-α giants. The predicted signal of mass-loss is weak and difficult to detect in our sample. We discuss implications for stellar physics and stellar population applications.

Subtype Identification of Surgically Curable Primary Aldosteronism During Treatment With Mineralocorticoid Receptor Blockade.

Current guidelines and consensus documents recommend withdrawal of mineralocorticoid receptor antagonists (MRAs) before primary aldosteronism (PA) subtyping by adrenal vein sampling (AVS), but this practice can cause severe hypokalemia and uncontrolled high blood pressure. Our aim was to investigate if unilateral PA can be identified by AVS during MRA treatment. We compared the rate of unilateral PA identification between patients with and without MRA treatment in large data sets of patients submitted to AVS while off renin-angiotensin system blockers and β-blockers. In sensitivity analyses, the between-group differences of lateralization index values after propensity score matching and the rate of unilateral PA identification in subgroups with undetectable (≤2 mUI/L), suppressed (<8.2 mUI/L), and unsuppressed (≥8.2 mUI/L) direct renin concentration levels were also evaluated. Plasma aldosterone concentration, direct renin concentration, and blood pressure values were similar in non-MRA-treated (n=779) and MRA-treated (n=61) patients with PA, but the latter required more antihypertensive agents (P=0.001) and showed a higher rate of adrenal nodules (82% versus 67%; P=0.022) and adrenalectomy (72% versus 54%; P=0.01). However, they exhibited no significant differences in commonly used AVS indices and the area under the receiving operating characteristic curve of lateralization index, both under unstimulated conditions and postcosyntropin. Several sensitivity analyses confirmed these results in propensity score matching adjusted models and in patients with undetectable, or suppressed or unsuppressed renin levels. At doses that controlled blood pressure and potassium levels, MRAs did not preclude the identification of unilateral PA at AVS. URL: https://www.clinicaltrials.gov; Unique identifier: NCT01234220.