Gender and surface effects on tiebreak tactics in Grand Slams: A machine learning–enhanced point-by-point analysis

The authors developed the computerized scorebook for tennis to analyze the time duration of shots. The purpose of this study was to analyze the time factors in Grand Slam singles matches using the computerized scorebook for tennis. Eighty-two players’ performances from forty-one matches in Grand Slam tournaments held in 2003 and 2004 were analyzed. The French Open, Wimbledon and the U.S. Open were selected to compare the effect of court surface on timing factors. The time duration of both 1st and 2nd service was longest at the French Open. Players need different service strategy between the French Open and the other tournaments because of the difference of strategy. Time duration of ground strokes showed no significant differences between the three tournaments. Ground strokes and rally play followed similar rhythms at all three tournaments. These results were obtained using the computerized scorebook for tennis. The usefulness of the scorebook for tennis coaching was demonstrated.

In this article, different modern machine learning and regression approaches for modeling and prediction of tennis matches in Grand Slam tournaments are investigated. Our data include information on 5013 matches in men's ssGrand Slam tournaments from the period 2011 to 2022. The investigated methods focus on modeling the probability of the first-named player to win the respective match. Moreover, different features are considered including the players' age, the ATP ranking and points, bookmakers' odds, Elo rating, and two additional age variables, which take into account the optimal age of a tennis player. We compare the different regression approaches to modern machine learning approaches with respect to various performance measures. Moreover, we also investigate different forecast strategies. First of all, a cross-validation-type strategy for all matches between 2011 and 2021. We also use an “expanding window” strategy by continuously updating the training data to analyze the predictive performance of the approaches on the tournaments from 2022. Finally, a “rolling window” strategy is used with only 3 years of tournaments as training data. We then select small subsets of best models with largest average ranks and investigate those in more detail by the help of interpretable machine learning techniques.

Public service organizations, such as the police, place great value on employee commitment because the public interest is at stake. While previous literature establishes a negative association between affective commitment and emotional exhaustion, the underlying mechanism remains insufficiently explored. Drawing on the perspective of emotional labor, this paper investigates whether surface acting, which refers to the feigning of expected emotions, mediates the impact of affective commitment on emotional exhaustion among police personnel. Furthermore, the study aims to explore whether this mediating effect is influenced by gender. The dataset utilized in this research comprises responses obtained from a survey administered to 465 police officers employed by the Taipei City Police Department. Our findings reveal a significant suppression effect of surface acting in the affective commitment-emotional exhaustion relationship, suggesting that surface acting, as a result of low affective commitment, has a detrimental impact on emotional well-being. Interestingly, the effects of affective commitment and surface acting on emotional exhaustion are stronger in male police officers compared to their female counterparts. In summary, the results of this study contribute to the existing literature and have broader implications for high-stress work environments. The findings provide insights into how organizations can better support the well-being of their employees by promoting commitment and addressing surface acting. Moreover, the study underscores the importance of considering gender differences in understanding the impact of these variables on emotional exhaustion among police personnel.

Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.

The emergence and rapid spread of multidrug-resistant bacterial strains is a growing concern of public health. Inspired by the natural bactericidal surfaces of lotus leaves and shark skin, increasing attention has been focused on the use of mechano-bactericidal methods to create surfaces with antibacterial and/or bactericidal effects. There have been several studies exploring the bactericidal effect of nanostructured surfaces under various combinations of parameters. However, the correlation and synergies between these factors still need to be clarified. Recently machine learning (ML), which enables prediction or decision-making based on data, has been used in the field of biomaterials with promising results. In this study, we explored ML in nanotechnology to investigate the antimicrobial potential of nanostructured surfaces. A dataset of nanostructured surfaces and their antimicrobial properties was built by extracting the published literature. Based on the literature review and the distribution of our dataset, 70% bactericidal efficiency was selected as a practical benchmark for our classification model that balances stringent bactericidal performance with achievable targets in diverse conditions. Subsequently, we developed an ML classification model, which demonstrated an 81% accuracy in its predictive capability. A regression model was further developed to predict the value of bactericidal efficiency for nanostructured surfaces. Feature importance analysis of the ML models suggested that nanotopographical features have a greater influence on bactericidal properties than material properties, thus providing insight into the principles of the mechano-bactericidal effect of nanostructured surfaces. Overall, this ML model tool could help researchers to effectively select and design the parameters of the surface structure prior to experimentation, thereby improving the timeliness and reducing the number of experiments and the associated costs.Graphical

Age-related changes of arm movements in dual task condition when walking on different surfaces

This study aimed to explore variations in enamel thickness to provide guidelines for optimal interproximal enamel reduction in an untreated population using cone-beam computed tomography (CBCT). CBCT scans of 100 orthodontic patients (51 Caucasian, 49 patients of Somalian descent; aged (12-18) were analyzed retrospectively. Enamel thickness was measured at the mesial and distal contact points of teeth from the second molar to the central incisor in both the maxillary and mandibular arches. Linear mixed models were employed to assess the effects of ethnicity, gender, anterior-posterior region, and mesial-distal proximal surfaces on enamel thickness. Fixed effects were estimated using the Kenward-Roger method, and a random intercept with an unstructured covariance matrix was included to account for within-subject variability. Ethnicity-specific residual variances were also modeled. Statistical significance was set at p<0.05. Enamel thickness varied significantly between Caucasians and Somalians in both the maxilla and mandible (p<0.001), with greater thickness observed in Caucasians. Gender-related differences were minimal; however, in the maxilla, distal surfaces of posterior teeth had greater enamel thickness in females compared to males (p=0.0478). Enamel thickness was consistently greater on distal surfaces of posterior teeth (p<0.001), while no significant differences were observed between mesial and distal surfaces in anterior teeth (p>0.05). Posterior teeth, particularly distal proximal surfaces of premolars and molars hold a great potential for enamel reduction, offering clinicians the most optimal site in orthodontic interventions.

This study includes three chapters related to machine learning applications with focus on different empirical topics. The first chapter talks about a new method and its application. The second chapter focuses on young economics professors salary issues. While the third chapter discusses scientific paper publication values based on text analysis and gender bias. In the first Chapter, I give a discussion of Double/Debiased Machine Learning (DML) which is a causal estimation method recently created by Chernozhukov, Chetverikov, Demirer, Duo, Hansen, Newey, and Robins (2018) and apply it to an education empirical analysis. I explain why DML is practically useful and what it does; I also take a bootstrap procedure to improve the built-in DML standard errors in the curriculum adoption application. As an extension to the existing studies on how curriculum materials affect student achievement, my work compares the results of DML, kernel matching, and ordinary least squares (OLS). In my study, the DML estimators avoid the possible misspecification bias of linear models and obtain statistically significant results that improve upon the kernel matching results. In the second chapter, we analyze the effects of gender, PhD graduation school rank, and undergraduate major on young economics professors' salaries. The dataset used is novel, containing detailed and time-varying research productivity measures and other demographic information of young economics professors from 28 of the top 50 public research universities in the United States. We apply double/debiased machine learning (DML) to obtain consistent estimators under the high-dimensional control variable set. By tracking the first 10 years of their professional work experience, we find that there barely exist effects on young faculties' salaries from the above three factors in most of the experience years. However, the gender effect on salary in experience year 7 is both statistically significant and economically significant (large enough in magnitude to have a practical meaning). In experience years 5 to 7, which are also near most faculties' promotion years, the gender effects are obvious. For both PhD graduation school rank and undergraduate major, the estimates for experience years 7 to 9 are large in magnitude; however they do not possess statistical significance. Overall, the effects tend to expand with years of experience. We also discuss possible economic mechanisms and reasons. In the third chapter, we build machine learning and simple linear models to predict academic paper publication outcomes as measured by journal H-indices, and we discuss the gender bias associated with these outcomes. We use a novel dataset with paper text content and each paper's associated H-index, authors' genders, and other information, collected from recently published economics journals. We apply term frequency-inverse document frequency vectorization and other Natural Language Processing (NLP) tools to transfer text content into numerical values as model inputs. We find that when using paper text content to predict an H-index, the prediction power is around 60 [percent] in our classification model (4 tiers) and the root mean squared error is around 44 in our regression model. Moreover, when controlling for paper text, the gender causal effect hardly exists. As long as the paper contains similar text, gender does not influence the change in H-index. Additionally, we give real-world meanings associated with the models.

Nanotechnology refers to technology that studies the performance and usage of materials with a structure size of 0.1 to 100 nanometers. Due to the special size of this material, it determines that this material has many more special properties than ordinary materials, such as size effect, surface effect, etc., so its application market can be said to be very broad. At present, the application of nanomaterials in daily production and life has aroused widespread concern, especially the two major blocks of biology and medicine. How to combine different properties such as magnetism and catalytic properties to form multifunctional nanomaterials and how to use the material more efficiently in the field of biology has become a research direction of experts. Machine learning can realize the automatic identification of non-good wood, which effectively avoids the impact of visual fatigue or other subjective factors in the manual identification process, and machine learning has a more accurate positioning of the standard of non-good wood, thereby improving the wood's quality. Usage rate. In this paper, the fuzzy analytic hierarchy process is mainly used to study the catalytic performance of nanocomposites on glucose oxidation. Compared with glassy carbon bare electrode (GCE) and IL-GR / GCE, / IL-GR / GCE showed significant electrocatalytic activity for glucose oxidation. The linear range is 0 ∼ 1000 μM, and the detection limit is 0.162 μM (S / N = 4). The results showed that the current response of glucose did not change significantly, proving that / IL-GR / GCE has a strong anti-interference ability.

Machine learning and the labor market: A portrait of occupational and worker inequities in Canada.

Nowadays, with large amounts of data becoming available, solving biological quests is becoming more and more a data-driven activity. To support this, there is a need for tools that enable the integration of the many sources of data. This thesis presents several avenues that can be taken, showing how integration can support research in the life sciences. Biological data can be integrated at several levels. We present a categorization of these different strategies within the context of the Data-Information-Knowledge-Wisdom paradigm. We argue that bioinformatics research should not only concentrate on the individual levels, but also on the transitions between these domains. Throughout the thesis we present possible solutions for a number of these different transformations. One of these transformations bridges the gap that exists between the two lowest levels of data integration: data representations (e.g. databases) and data analysis (e.g. pattern recognition, statistical analysis). The wealth of different data sources, each describing a different aspect of the molecular system (such as gene expressions, locations on genome, physical binding partners etc.), have driven data representation approaches towards flat and flexible formats. In contrast, data analysis prefers structured multi-dimensional array-based data formats. We argue that this gap cannot be closed, but rather needs to be bridged through the use of novel query systems. As a solution, we introduce the tool IBIDAS, which allows one to easily handle not only tables, but also more complexly structured data, making it a flexible tool for the exploration and analysis of data. Another question concerns at what point different data sources need to be integrated. One strategy (`late' integration) is to analyse each data source separately, after which the results are integrated. This strategy however prevents the discovery of connections that transcend the individual data sources. The alternative `early' integration strategy, in which the data is first concatenated (i.e. as feature vector) before analysis, is however not always feasible when complex data types, such as DNA sequences, need to be taken into account. We advocate an `intermediate' data integration approach, in which each data source is first transformed into a suitable “kernel space”. In this space, data can be integrated in a straightforward manner. This can even be done in a non-linear fashion, after which the data can be analyzed together. We show the strength of such “kernel method” when combining data sources to predict interacting proteins. When combining similar data, we emphasize that one should consider this as a data integration problem too, instead of just concatenating the data. As an example, batch-effects can seriously affect the data distributions of gene expression experiments. These effects need to be resolved when analyzing these experiments jointly. One way to solve this is to normalize data before it is joined. We show that it is necessary to take into account as much information as possible about the way in which the data is created into a normalization scheme. By modeling the effects that deteriorate your data, seemingly uninformative data sets can become again a rich source of information. As an example we applied this to data sets that study the relationship between the transcriptome of stem cells and the effectivity of these cells in bone regeneration. Instead of integrating data for a single problem, one can also integrate data for a class of problems. We show that the machine learning concept can elegantly solve such integration problems. By making use of the similarities between the problem domains, learning parameters can be restricted. This approach was applied in the analysis of 'materiomics' data for the new TopoChip platform. Measurements that characterized the reactions of cells to individual material surfaces were noisy, making it difficult to adequately compare these surface effects. However, by taking into account the similarities between surfaces, and by integrating data across these similar surfaces, results were improved significantly. As an encompassing example of data integration we finally show how a combination of integration methods can be put together to link two other integration levels: pattern recognition and causal model inference. In this example, numerous data sources are being used to predict cause-effect relationships between genes in perturbation experiments. The used data sources describe various aspects of proteins, protein-protein interactions and protein-DNA interactions. These descriptions of the physical components of a cell are related to cause-effect interactions between the genes, in such a way that data from perturbation experiments is explained. We combine kernel-based integration methods with a method that constructs a causal model, showing that cause-effect relationships can be accurately predicted. Taken together, this thesis explores several data integration levels and approaches. Given the complexity of biology, we believe that data integration will become more and more essential in bioinformatics and that this dissertation only has set the first steps on this road.

Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery

Surface structures with excellent aerodynamic performance can reduce the flow loss of aero-engine caused by the harsh working environment to a certain extent. Inspired by the dentate micro-texture over shark skin, micro-texture surface has been proposed as an emerging and effective means to reduce the flow loss of aero-engine. However, for a realistic compressor cascade configuration with micro-texture, using massive grids to describe the flow within the boundary layer makes the simulation of multi-scale flow field unfeasible. This paper presents an innovative numerical simulation method for compressor cascade with micro-textured surface using a machine learning wall modification model. The wall modification model is trained based on the near-wall microflow data obtained by LBM, which can reproduce the flow effects of micro-textured surface. The mechanism of reducing the total pressure loss of the compressor cascade is that the micro-textured surface structure delays the laminar transition and decreases the intensity of the turbulent region. In addition, wake measurements through experiment are performed to validate the proposed multi-scale numerical simulation method in the high-speed linear cascade wind tunnel. This paper demonstrates the flow loss reduction effect of micro-textured surface on compressor cascade through numerical and experimental study.

The presence of strong anharmonic effects in surface functionalized MXenes greatly challenges the use of harmonic lattice dynamics calculations to predict their phonon spectra and lattice thermal conductivity at finite temperatures. Herein, we demonstrate the workflow for training and validating machine learning potentials in terms of moment tensor potential (MTP) for MXenes including Mo2TiC2, Mo2TiC2O2, Mo2TiC2F2 and Janus-Mo2TiC2OF monolayers. Then, the MTPs of MXenes are successfully combined with the harmonic lattice dynamics calculations to obtain the temperature renormalized phonon spectra, three-phonon scattering rates, phonon relaxation times and lattice thermal conductivity at finite temperatures. Furthermore, combining MTPs with classic molecular dynamics simulations at finite temperatures directly enables the calculation of phonon quasi-particle spectral energy density with a full inclusion of all anharmonic effects in MXenes. Our current results indicate that anharmonic effects are found to be relatively weak in Mo2TiC2 and Mo2TiC2O2 monolayers, whereas the phonon quasi-particle spectral energy densities largely resemble those of harmonic or renormalized lattice dynamics calculations. Significant broadening of spectral energy density at finite temperature is predicted for Mo2TiC2F2 and Janus-Mo2TiC2OF monolayers, implying strong anharmonic effects in those MXenes. Our work paves a new way for fast and reliable calculation of the phonon scattering process and lattice thermal conductivity of MXenes within MTPs trained from first-principles molecular dynamics simulations in the future.

This study investigates the potential role of machine learning (ML) technology for predicting a match, or mutual interest, in the context of speed dating. Modern machine learning technologies (light gradient boosting machine - lgbm, random forest, logistic regression, stochastic gradient descent, k nearest neighbour), exhaustively combined with feature selection methods (filter-based association, filter-based prediction, embedded lgbm, embedded linear, redundancy aware step up wrapper), were applied to a speed dating dataset, and tasked with predicting a match (mutual interest from speed dating participants). Our analysis employed public-domain ML software combined with a public-domain dataset, supporting reproducibility of study findings. Results indicate that ML models can predict a match with 85.4 to 86.4% accuracy. The creation of ethical ML applications in this domain, including those blinded to issues of race, and specific to each gender, are explored as part of this analysis. Results also demonstrate that it is possible to create race-blinded ML models with approximately equal performance to those biased by racial information, thus supporting the creation of more ethical, inclusive, and behavior-focused technologies.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-25028-x.