Search

Context: Sport-specific adaptations in shoulder rotation range of motion (ROM) and the relationship with humeral torsion have been described in overhead-throwing sports. However, information is lacking for other shoulder-loading sports such as field hockey. Therefore, this study's purpose was to evaluate humeral torsion in elite, male field hockey players and explore its association with shoulder ROM. Design: Cross-sectional study. Methods: Twenty-five male, elite field hockey players were included. Humeral torsion and shoulder external and internal rotation ROM were evaluated bilaterally by ultrasound and an inclinometer smartphone application, respectively. Results: Field hockey players showed a significantly increased humeral retroversion on the dominant compared with the nondominant side (P <.001; Cohen d = 1.75), along with a significantly increased external (P =.004; Cohen d = −0.64) and decreased internal rotation ROM (P =.003; Cohen d = 0.65). This finding illustrates a shift in total shoulder rotational ROM arc. Correlation analysis showed a significant moderate association between the increased humeral retroversion and decreased internal rotation ROM on the dominant side (r =.523). Conclusions: Elite male field hockey athletes show sport-specific adaptations regarding humeral torsion and shoulder rotation ROM, similar to throwing athletes. These findings increase our insight into the field hockey athlete's shoulder, which is essential to optimize performance and assist in correctly interpreting shoulder rotational ROM measurements. [ABSTRACT FROM AUTHOR]

Despite previous support for plyometric training, optimal dosing strategies remain unclear. Purpose: To investigate vertical and horizontal jump kinetic performance following a low-volume plyometric stimulus with progressively increased session jump volume. Methods: Sixteen academy rugby players (20.0 [2.0] y; 103.0 [17.6] kg; 184.3 [5.5] cm) volunteered for this study. Vertical and horizontal jump sessions were conducted 1 week apart and consisted of a 40-jump low-volume plyometric stimulus using 4 exercises, after which volume was progressively increased to 200 jumps, using countermovement jump (CMJ) for vertical sessions and horizontal broad jump (HBJ) for horizontal sessions. Jump performance was assessed via force-plate analysis at baseline (PRE-0), following the low-volume plyometric stimulus (P-40), and every subsequent 10 jumps until the end of the session (P-50, P-60, P-70,... P-200). Results: The low-volume stimulus was effective in potentiating HBJ (2% to 5%) but not CMJ (0% to −7%) performance (P <.001). The HBJ performance enhancements were maintained throughout the entire high-volume session, while CMJ realized small but significant decrements (−5% to −7%) in jump height P-50 to P-80 before recovering to presession values. Moreover, increases in eccentric impulse (5% to 24%; P <.001) in both sessions were associated with decreased or maintained concentric impulse, indicating a breakdown in performance-augmenting mechanisms and less effective power transfer concentrically after moderate volumes. Conclusion: Practitioners should consider kinetic differences between HBJ and CMJ with increasing volume to better inform and understand session dosing strategies. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a diagnostic method for identifying influential observations in the multivariate DCC-GARCH model. We employ the Bayesian local influence method by introducing small perturbations to the prior, variance, and data to assess their impact. Subsequently, through simulation studies and empirical analysis, we demonstrate the effectiveness of the Bayesian local influence method for multivariate GARCH models. In the empirical part, a bivariate GARCH model is established using the daily returns of the S&P 500 Index and IBM, and a comparative analysis is conducted to examine the differences in the influential points detected by the Bayesian method and traditional methods. [ABSTRACT FROM AUTHOR]

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small "many analyst" study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future. [ABSTRACT FROM AUTHOR]

Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease. [ABSTRACT FROM AUTHOR]

Introduction: Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop prognostic models for predicting renal graft survival and compare the performance of statistical and machine learning models. Methodology: The study utilized data from 278 renal transplant recipients at the Ethiopian National Kidney Transplantation Center between September 2015 and February 2022. To address the class imbalance of the data, SMOTE resampling was applied. Various models were evaluated, including Standard and penalized Cox models, Random Survival Forest, and Stochastic Gradient Boosting. Prognostic predictors were selected based on statistical significance and variable importance. Results: The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The 3-month, 1-year, and 3-year graft survival rates were found to be 0.979, 0.953, and 0.911, respectively. The Stochastic Gradient Boosting (SGB) model demonstrated the best discrimination and calibration performance, with a C-index of 0.943 and a Brier score of 0.000351. The Ridge-based Cox model closely followed the SGB model's prediction performance with better interpretability. The key prognostic predictors of graft survival included an episode of acute and chronic rejections, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, post-transplant regular exercise, and marital status. Conclusions: The Stochastic Gradient Boosting model demonstrated the highest predictive performance, while the Ridge-Cox model offered better interpretability with a comparable performance. Clinicians should consider the trade-off between prediction accuracy and interpretability when selecting a model. Incorporating these findings into the clinical practice can improve risk stratification and personalized management strategies for kidney transplant recipients. [ABSTRACT FROM AUTHOR]

Comorbidity networks have become a valuable tool to support data-driven biomedical research. Yet, studies often are severely hindered by the availability of the necessary comprehensive data, often due to the sensitivity of health care information. This study presents a population-wide comorbidity network dataset derived from 45 million hospital stays of 8.9 million patients over 17 years in Austria. We present co-occurrence networks of hospital diagnoses, stratified by age, sex, and observation period in a total of 96 different subgroups. For each of these groups we report a range of association measures (e.g., count data, and odds ratios) for all pairs of diagnoses. The dataset provides the possibility to researchers to create their own, tailor-made comorbidity networks from real patient data that can be used as a starting point in quantitative and machine learning methods. This data platform is intended to lead to deeper insights into a wide range of epidemiological, public health, and biomedical research questions. [ABSTRACT FROM AUTHOR]

Despite being a powerful tool to model ecological interactions, traditional evolutionary game theory can still be largely improved in the context of population dynamics. One of the current challenges is to devise a cohesive theoretical framework for ecological games with density-dependent (or concentration-dependent) evolution, especially one defined by individual-level events. In this work, I use the notation of reaction networks as a foundation to propose a framework and show that classic two-strategy games are a particular case of the theory. The framework exhibits a strong versatility and provides a standardized language for model design, and I demonstrate its use through a simple example of mating dynamics and parental care. In addition, reaction networks provide a natural connection between stochastic and deterministic dynamics and therefore are suitable to model noise effects on small populations, also allowing the use of stochastic simulation algorithms such as Gillespie's with game models. The methods I present can help to bring evolutionary game theory to new reaches in ecology, facilitate the process of model design, and put different models on a common ground. [ABSTRACT FROM AUTHOR]

Drought, a severe natural disaster, leads to significant water shortages in several parts of the globe. Therefore, analyzing accurate spatio-temporal characteristics of drought are essential for effective drought mitigation policies. In geo-statistics, variogram models are useful tools to evaluate the spatial correlation for interpolation. Due to complex structure of drought, the selection of optimal variogram model for spatial interpolation of drought characteristics is challenging. This research infers the ability various variogram models for the spatial interpolation of drought duration under various time scales of Multivariate Modified Water Drought Severity Index (MMWDSI) in three future scenario using Ordinary Kriging (OK) and Universal Kriging (UK). In our application, we used spatial data on drought duration characterized by the MMWDSI across three future scenarios at 94 locations in Pakistan. In this research, seventeen variogram models are evaluated for analyzing spatial patterns of drought duration. The optimal selection of the most appropriate variogram models is assessed by comparing error metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and mean bias) under Leave-One-Out Cross-Validation (LOOCV) and K-fold cross-validation methods. From the results of this research, we found that the Exponentially Correlated Cauchy (Exc) variogram model is optimal due to its minimal error metrics except in one month time scales at SSP3-8.5 future scenario. Furthermore, the Hole-effect (Hol) model is deemed suitable for handling complex data at the most of the remaining time scales. This research reveals that as time scales and scenarios change, they influence the effectiveness of the variogram. Overall, the study suggests that no single model is universally superior for the accurate spatial interpolation of drought duration. These findings emphasize the importance of selecting the appropriate variogram models during the interpolation stage to achieve accurate and precise predictions of drought duration. [ABSTRACT FROM AUTHOR]

Accurate short-term wind speed forecasts are essential for optimizing wind energy harvesting and maintaining grid reliability. This study evaluates the SARIMA, SARIMAX, VAR, and VARMA time series models, using hourly and sub-hourly wind speed and direction data from four Algerian sites with Mediterranean and Saharan climates. The forecasting process incorporated both simultaneous and rolling forecasts approaches, with LSTM serving as a benchmark. Simultaneous forecasts showed that LSTM outperformed the statistical models, by at least 46.95% in RMSE and 48.22% in MAE. However, the rolling forecast approach significantly improved the statistical models' performance, mitigating the plateau effect common in time series predictions, despite being computationally expensive. Notably, SARIMA emerged as the most reliable model overall, while VARMA offering a competitive alternative, whereas VAR often lagged behind. Despite LSTM's initial dominance, it was ultimately surpassed by these models by at least of 65.54% and 58.31% for RMSE and MAE respectively, highlighting the effectiveness of rolling forecasts approach. Moreover, we observed that incorporating the sine and cosine of hours alongside the wind direction as exogenous variables enhanced the performance of SARIMAX. Furthermore, VAR and VARMA excelled in locations exhibited significant correlation between wind features. Notably, The largest RMSE and MAE values occurred in areas with low data quality and high volatility. [ABSTRACT FROM AUTHOR]

Soccer is a popular sport, and there is a growing need for automated analysis of soccer videos, while the detection and tracking of the players is the indispensable prerequisite. In this paper, we first introduce and classify multi-object tracking and then present two mostly used multi-object tracking methods, DeepSort and TrackFormer. When multi-object tracking is applied to soccer scenarios, some preprocessing and post-processing are generally performed, with preprocessing including processing of the video, such as splicing and background removing, and post-processing including further applications, such as player mapping for a 2D stadium. By directly employing the two methods above, we test the real scene and train TrackFormer to get further results. Meanwhile, in order to facilitate researchers who are interested in multi-object tracking as well as in the direction of player tracking, recent advances in preprocessing and processing methods for soccer player tracking are given and future research directions are suggested. [ABSTRACT FROM AUTHOR]

Objectives: To develop a machine learning-based pipeline for multi-organ/tissue personalized radiation dosimetry in CT. Materials and methods: For the study, 95 chest CT scans and 85 abdominal CT scans were collected retrospectively. For each CT scan, a personalized Monte Carlo (MC) simulation was carried out. The produced 3D dose distributions and the respective CT examinations were utilized for the development of organ/tissue-specific dose prediction deep neural networks (DNNs). A pipeline that integrates a robust open-source organ segmentation tool with the dose prediction DNNs was developed for the automatic estimation of radiation doses for 30 organs/tissues including sub-volumes of the heart and lungs. The accuracy and time efficiency of the presented methodology was assessed. Statistical analysis (t-tests) was conducted to determine if the differences between the ground truth organ/tissue radiation dose estimates and the respective dose predictions were significant. Results: The lowest median percentage differences between MC-derived organ/tissue doses and DNN dose predictions were observed for the lung vessels (4.3%), small bowel (4.7%), pulmonary artery (4.7%), and colon (5.2%), while the highest differences were observed for the right lung's upper lobe (13.3%), spleen (13.1%), pancreas (12.1%), and stomach (11.6%). Statistical analysis showed that the differences were not significant (p-value > 0.18). Furthermore, the mean inference time, regarding the validation cohort, of the developed methodology was 77.0 ± 11.0 s. Conclusion: The proposed workflow enables fast and accurate organ/tissue radiation dose estimations. The developed algorithms and dose prediction DNNs are publicly available (https://github.com/eltzanis/multi-structure-CT-dosimetry). Clinical relevance statement: The accuracy and time efficiency of the developed pipeline compose a useful tool for personalized dosimetry in CT. By adopting the proposed workflow, institutions can utilize an automated pipeline for patient-specific dosimetry in CT. Key Points: Personalized dosimetry is ideal, but is time-consuming. The proposed pipeline composes a tool for facilitating patient-specific CT dosimetry in routine clinical practice. The developed workflow integrates a robust open-source segmentation tool with organ/tissue-specific dose prediction neural networks. [ABSTRACT FROM AUTHOR]

Distance covariance (Székely, Rizzo, and Bakirov) is a fascinating recent notion, which is popular as a test for dependence of any type between random variables X and Y. This approach deserves to be touched upon in modern courses on mathematical statistics. It makes use of distances of the type | X − X ′ | and | Y − Y ′ | , where (X ′ , Y ′) is an independent copy of (X, Y). This raises natural questions about independence of variables like X − X ′ and Y − Y ′ , about the connection between cov (| X − X ′ | , | Y − Y ′ |) and the covariance between doubly centered distances, and about necessary and sufficient conditions for independence. We show some basic results and present a new and nontechnical counterexample to a common fallacy, which provides more insight. We also show some motivating examples involving bivariate distributions and contingency tables, which can be used as didactic material for introducing distance correlation. [ABSTRACT FROM AUTHOR]

Introduction: Metabolomics is the comprehensive study of small molecules in biological systems. It has recently garnered attention for its wide variety of applications such as diseases, drug treatments, agriculture, and more. As the interest in metabolomics grow, meeting the demands of cutting-edge research requires software tools that not only advance analytical capabilities, but also prioritize user-friendly features. Objectives: In response to this need, we present two new computer programs, A-SIMA: Advanced-Software for Interactive Metabolite Analysis and A-MAP: A Multivariate Analysis Program. These tools aim to introduce new capabilities for metabolite identification and data analysis, and thereby advancing the computational methodology in NMR-based metabolomics. Methods: A-SIMA is designed with an easy-to-use graphical user interface which allows users to perform metabolite identification on 1D and 2D NMR data effortlessly with complete control over the identification process. Similarly, A-MAP facilitates multivariate statistical analysis of metabolite data through a straightforward process. It offers analysis options such as Principal Component Analysis and Orthogonal Partial Least Squares-Discriminant Analysis using regions of interests as inputs. Results: Both A-SIMA and A-MAP are pre-built in the POKY suite, available at https://poky.clas.ucdenver.edu, with tutorial videos on YouTube for guidance on not only the programs, but also installation. The POKY suite is a software program for NMR biomolecular analysis. With the addition of these programs in POKY, researchers and professionals can experience a fully integrated process for every step of their metabolite analysis. Data can also be easily exported from these programs to be applied elsewhere. Conclusion: The introduction of A-SIMA and A-MAP can be promising tools that can lead significant advancements in metabolomics research. These tools offer enhanced capabilities for metabolite analysis and statistical modelling in a user-friendly manner. Their integration into the POKY suite ensures accessibility, usability, and efficiency. [ABSTRACT FROM AUTHOR]

Seismic data analysis often faces the challenge of random noise contamination from various sources. To overcome this, innovative noise attenuation methods utilizing seismic signal properties are needed. This study focuses on efficiently suppressing random noise in the domain of time and frequency by accurately estimating instantaneous frequency using the single-valued group delay characteristic of seismic signals. The time-reassigned synchrosqueezing transform (TSST) and its second-order variant (TSST2) offer high-resolution time-frequency representations (TFRs) for noise suppression. Expanding on these advancements, we propose an efficient noise suppression method that integrates the adaptive thresholding model into the TSST2 framework and employs sparse representation of the TFR through low-rank estimation. This method effectively attenuates noise while preserving essential signal information. The proposed approach operates trace by trace on recorded data, initially transforming it into a sparse subspace using TSST2. The adaptive thresholding model then decomposes the resulting TFR into sparse and semi-low-rank components, achieving a high-resolution and sparse TFR for efficient separation of noise and signal. After noise suppression, the seismic data can be fully reconstructed by inversely transforming the semi-low-rank component data into the time domain. This method addresses previous limitations in noise attenuation techniques and provides a practical solution for enhancing seismic data quality. [ABSTRACT FROM AUTHOR]

At the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description. [ABSTRACT FROM AUTHOR]

Landslide-induced barrier dams pose a threat to the safety of humans, livestock and nearby infrastructures. The efficient assessment of landslide blocking river is crucial for disaster prevention and mitigation solutions. This study proposes a novel stochastic assessment framework to evaluate the landslide blocking river through the prediction of their deposition depths and considering the heterogeneity of shear strength parameters on the potential sliding surface. The depth-integrated continuum method (DICM) is used to simulate the landslide runout process. Using an enhanced Karhunen-Loève expansion (KLE) method, the spatial variations in soil's shear strength parameters are modeled by random fields to incorporate the effects of soil's spatial heterogeneity on the landslide deposition pattern. Subsequently, the multi-response surrogate model is constructed to relate the random field variables to the deposition depths based on extreme gradient boosting (XGBoost). To improve the performance of the surrogate model, principal component analysis (PCA) and sliced inverse regression (SIR) methods are employed for the dimension reduction of output and input variables, respectively. Furthermore, the algorithm for river blockage identification is developed to search for the deposition ridges. To demonstrate the capability of the stochastic assessment framework, an example of the first Baige landslide in Tibet, China is simulated, and the affected region and deposition depths of the landslide are predicted to calculate the probability of river damming. The presented methodology provides a practical means for improving the landslide blocking river prediction and new insights for early warning and risk mitigation. [ABSTRACT FROM AUTHOR]

Exploring more accurate estimates of financial value at risk (VaR) has always been an important issue in applied statistics. To this end either quantile or expectile regression methods are widely employed at present, but an accumulating body of research indicates that L p quantile regression outweighs both quantile and expectile regression in many aspects. In view of this, the paper extends L p quantile regression to a general classical nonlinear conditional autoregressive model and proposes a new model called the conditional L p quantile nonlinear autoregressive regression model (CAR- L p -quantile model for short). Limit theorems for regression estimators are proved in mild conditions, and algorithms are provided for obtaining parameter estimates and the optimal value of p. Simulation study of estimation's quality is given. Then, a CLVaR method calculating VaR based on the CAR- L p -quantile model is elaborated. Finally, a real data analysis is conducted to illustrate virtues of our proposed methods. [ABSTRACT FROM AUTHOR]

Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting, where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production. Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather, and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions. However, ensemble prediction systems are known to exhibit systematic errors, and thus require post-processing to obtain accurate and reliable probabilistic forecasts. The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy: not applying any post-processing at all; post-processing only the irradiance predictions before the conversion; post-processing only the solar power predictions obtained from the model chain; or applying post-processing in both steps. In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S., we develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance (GHI) and solar power generation. Further, we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain. Our results indicate that postprocessing substantially improves the solar power generation forecasts, in particular when post-processing is applied to the power predictions. The machine learning methods for post-processing slightly outperform the statistical methods, and the direct forecasting approach performs comparably to the post-processing strategies. [ABSTRACT FROM AUTHOR]

This paper examines the nature of instability in stochastic dynamical systems. Relying on a quantile approach, we propose to measure dynamic instability by the average rate of divergence ( A R D s ) of the state along a finite forward stochastic path. Under stochastic shocks, A R D s is a random variable with a given distribution function that depends on the nature of the underlying dynamic process as well as the nature of the shocks. We show how our approach can be made empirically tractable using a quantile autoregression (QAR) model. In an empirical application to futures price, the QAR estimates provide statistical evidence that futures price instability varies with market conditions: instability increases with the maturity of the futures contract as well as with higher quantiles (representing positive shocks located in the upper tail of the price distribution). We find that neglecting stochastic shocks (e.g., under a deterministic dynamic analysis) tends to overstate the presence of instability. The results stress the importance of evaluating the dynamic impacts of shocks across the whole distribution. [ABSTRACT FROM AUTHOR]

Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In this paper, we introduce the Long Short-Term Memory Network based Recurrent Autoencoder with Imputation Units and Temporal Attention Imputation Model (RATAI), tailored for multivariate time series. RATAI is designed to address certain limitations of traditional RNN-based imputation methods, which often focus on predictive modeling to estimate missing values, sometimes neglecting the contextual impact of observed data at and beyond the target time step. Drawing inspiration from Kalman smoothing, which effectively integrates past and future information to refine state estimations, RATAI aims to extract feature representations from time series data and use them to reconstruct a complete time series, thus overcoming the shortcomings of existing approaches. It employs a dual-stage imputation process: the encoder utilizes temporal information and attribute correlations to predict and impute missing values, and extract feature representation of imputed time series. Subsequently, the decoder reconstructs the series from the feature representation, and the reconstructed values are used as the final imputation values. Additionally, RATAI incorporates a temporal attention mechanism, allowing the decoder to focus on highly relevant inputs during reconstruction. This model can be trained directly using data that contains missing values, avoiding the misleading effects on model training that can arise from setting initial values for missing values. Our experiments demonstrate that RATAI outperforms benchmark models in multivariate time series imputation. [ABSTRACT FROM AUTHOR]

Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity of such data, recent endeavors have demonstrated successful integration of deep learning methodologies to address limitations in conventional statistical approaches. However, current methods typically involve cluttered probability distribution function (PDF), have lower sensitivity in censoring prediction, only model static datasets, or only rely on recurrent neural networks for dynamic modelling. In this paper, we propose a novel survival regression method capable of producing high-quality unimodal PDFs without any prior distribution assumption, by optimizing novel Margin-Mean-Variance loss and leveraging the flexibility of Transformer to handle both temporal and non-temporal data, coined UniSurv. Extensive experiments on several datasets demonstrate that UniSurv places a significantly higher emphasis on censoring compared to other methods. [ABSTRACT FROM AUTHOR]

Sparsity is a common characteristic for datasets used in the domain of sports forecasting, mainly derived from inconsistencies in data coverage. Typically, this issue is circumvented by cutting the number of features (depth-focused) or the sample size (breadth-focused) for analysis. The present study uses an experimental approach to analyse the effects of depth- or breadth-focused analyses and data imputation to enable usage of the full sample size and feature wealth. Two forecasting models following a hybrid (i.e., a combination of classical statistical and machine learning) and a full deep learning approach are introduced to perform experiments on a dataset of more than 300,000 soccer matches. In contrast to typical soccer forecasting studies, the analysis was not restricted to one-match-ahead forecasts but used a longer forecasting horizon of up to two months ahead. Systematic differences between the two types of models were identified. The hybrid model based on classical statistical rating models, performs strongly on depth-focused approaches while not or only marginally improving for approaches with high data breadth. The deep learning model, however, performs weakly in a depth-focused approach but profits strongly from data breadth. The improved prediction performance in cases of high data breadth suggests that a rich feature set offers better training opportunities than a less detailed set with a larger sample size. Additionally, we showcase that data imputation can be used to address data sparsity by enabling full data depth and breadth. The presented findings are relevant for advancing predictive accuracy and sports forecasting methodologies, emphasizing the viability of imputation techniques to increase data coverage in different analytical approaches. [ABSTRACT FROM AUTHOR]

Rockburst prediction significantly affects the development and utilization of underground resources. Currently, an increasing number of artificial intelligence algorithms are being applied for rockburst prediction. However, owing to the scarcity of data for certain rockburst grades, machine learning models have struggled to accurately train and learn their characteristics, resulting in bias or overfitting. In this study, 321 worldwide cases of rockbursts were collected. Seven indices considering both rock mechanics and stress conditions were selected as input parameters for the model. To address the issue of limited data for certain rockburst grades, the Synthetic Minority Over-sampling TEchnique (SMOTE) algorithm was used for comprehensive oversampling and synthesis of the rockburst data. The theoretical rationality of this method was corroborated by the Spearman's correlation coefficient. Additionally, the model hyperparameters were optimized using the Bayesian optimization method, and an improved eXtreme gradient boosting (XGBoost) rockburst prediction model (SM–BO–XGBoost) was established. The constructed SM–BO–XGBoost model was compared with decision tree, random forest, support vector machine, and k-nearest neighbor classification machine learning models. The results showed a significant improvement in the prediction accuracy for the None and Strong rockburst categories, which had limited data in the original rockburst dataset. To address the poor interpretability of the XGBoost model, the SHapley Additive exPlanations (SHAP) method was introduced to explain the constructed model, and to analyze the marginal contributions of different features to the model output across various rockburst grades. The SM-BO-XGBoost model was validated using field rockburst records from the Xincheng and Sanshandao gold mines. As indicated by the results, the model demonstrated favorable performance and applicability, with wide potential for predicting engineering rockbursts. [ABSTRACT FROM AUTHOR]

Permeability and porosity are key parameters in reservoir characterization for understanding hydrocarbon flow behavior. While traditional laboratory core analysis is time-consuming, machine learning has emerged as a valuable tool for more efficient and accurate estimation. This paper proposes an ensemble technique called adaptive boosting (AdaBoost) for porosity and permeability estimation, utilizing methods such as support vector machine (SVM), Gaussian process regression (GPR), multivariate analysis, and backpropagation neural network (BPNN) for prediction based on well logs. Performance evaluation metrics including root mean square error, mean square error, and coefficient of determination (R2) were used to compare the models. The results demonstrate that AdaBoost outperformed GPR, SVM, and BPNN models in terms of processing time and accuracy, achieving R2 values of 0.980 and 0.962 for permeability and porosity during training, respectively, and 0.960 and 0.951 during testing, respectively. This study highlights AdaBoost as a robust and accurate technique that can enhance reservoir characterization. [ABSTRACT FROM AUTHOR]

Optimizing athletic training programs with the support of predictive models is an active research topic, fuelled by a consistent data collection. The Fitness-Fatigue Model (FFM) is a pioneer for modelling responses to training on performance based on training load exclusively. It has been subject to several extensions and its methodology has been questioned. In this article, we leveraged a Bayesian framework involving biologically meaningful priors to diagnose the fit and predictive ability of the FFM. We used cross-validation to draw a clear distinction between goodness-of-fit and predictive ability. The FFM showed major statistical flaws. On the one hand, the model was ill-conditioned, and we illustrated the poor identifiability of fitness and fatigue parameters using Markov chains in the Bayesian framework. On the other hand, the model exhibited an overfitting pattern, as adding the fatigue-related parameters did not significantly improve the model's predictive ability (p-value > 0.40). We confirmed these results with 2 independent datasets. Both results question the relevance of the fatigue part of the model formulation, hence the biological relevance of the fatigue component of the FFM. Modelling sport performance through biologically meaningful and interpretable models remains a statistical challenge. [ABSTRACT FROM AUTHOR]

Background: Due to climate change and rapid urbanization, the frequency of heatwave events in East China has increased considerably since the 21st century, which has a considerable influence on human health, such as heatstroke. However, few studies have been conducted in this region on the relationship between heatstroke and meteorological conditions. To address this point, this study aimed to analyze the characteristics of heatstroke and their relationship with meteorological conditions in Hefei, China. Methods: The 2008–2022 heatstroke data from Hefei Center for Disease Control and Prevention was used. The relationship between heatstroke and meteorological conditions was discussed by statistical methods, such as correlation analysis, cluster analysis and linear regression analysis. Results: The number of heatstroke cases fluctuated upward from 36 cases in 2008 to 1051 cases in 2022, with 71.5% of all cases for males and females accounting for 28.5%. The highest frequency of heatstroke occurrence was found to be concentrated in the middle age group (40–59 years old). According to the statistical analysis, air temperature and relative humidity were the most important meteorological factors that influenced the occurrence of heatstroke. Then a threshold system of meteorological factors for heatstroke was established by utilizing the relationship: daily average temperature (T) ≥ 30 °C & daily average relative humidity (RH) ≤ 80% and daily maximum temperature (Tmax) ≥ 35 °C & daily minimum relative humidity (RHmin) ≤ 65%. The threshold in group outbreak areas was stricter than it in high incidence areas. Furthermore, the Pacific Subtropical High (PSH) was found to be the primary climatic factor that determined the occurrence of heatstroke occurrence on a seasonal scale. In addition, significant differences in heatstroke risk were found among different groups of people. Heatstroke risk was substantially higher in males than in females due to larger opportunities for outdoor labor. The reduced physical fitness of elderly people raised the risk of heatstroke more than other age groups in extremely high temperatures. Conclusions: A meteorological threshold system had been established to forecast heatstroke occurrence in a short-term time, and a key climate driving factor of heatstroke was found for long-term heatstroke prediction in Hefei. These findings could facilitate the disease control department to take preventive and control measures to reduce the impact of heatstroke on human health and society. [ABSTRACT FROM AUTHOR]

Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets. We propose a new prediction model for STLF that combines data clustering and dimensionality reduction schemes to handle large-scale electricity usage data effectively. Here, we adapt k-means clustering for data clustering, kernel principal component analysis (kernel PCA), universal manifold approximation and projection (UMAP), and t-stochastic nearest neighbor (t-SNE) for dimensionality reduction. To verify the effectiveness of the proposed model, we extensively apply it to neural network-based models. We compare and analyze the performance of the proposed model with the comparisons using actual electricity usage data for 4710 households. Experimental results demonstrate that data clustering with dimensionality reduction can improve the performance of baseline models. As a result, the prediction accuracy of the proposed method outperforms those of the existing methods by 1.01–1.76 times for summer data and by 1.03–1.36 times for winter data in terms of mean absolute percentage error (MAPE). [ABSTRACT FROM AUTHOR]

Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them. The construction and comparison methods of predictive models by machine learning algorithms are investigated to evaluate the level of predictive models in order to determine the optimal predictive model. The results show that the PSO-SVR model performs best in predicting athlete engagement, with a prediction accuracy of 0.9262, along with low RMSE (0.1227), MSE (0.0146) and MAE (0.0656). The prediction accuracy of the PSO-SVR model exhibits an obvious advantage. This advantage is mainly attributed to its strong generalization ability, nonlinear processing ability, and the ability to optimize and adapt to the feature space. Particularly noteworthy is that the PSO-SVR model reduces the RMSE (7.54%), MSE (17.05%), and MAE (3.53%) significantly, while improves the R2 (1.69%), when compared to advanced algorithms such as SWO. These results indicate that the PSO-SVR model not only improves the accuracy of prediction, but also enhances the reliability of the model, making it a powerful tool for predicting athlete engagement. In summary, this study not only provides a new perspective for understanding athlete engagement, but also provides important practical guidance for improving athlete engagement and overall performance. By adopting the PSO-SVR model, we can more accurately identify and optimise the key factors affecting athlete engagement, thus bringing far-reaching implications for research and practice in sport science and related fields. [ABSTRACT FROM AUTHOR]

Background: Cannabis is the third most widely used psychoactive substance globally, and its consumption has been increasing, particularly with the growing trend of legalization for medicinal and recreational use. Recent studies have raised concerns about the potential impact of cannabis on respiratory health, specifically the risk of asthma, a significant public health concern. This systematic review aimed to consolidate research on the association between cannabis use and the risk of asthma. Methods: A comprehensive search was conducted across PubMed, Embase, and Web of Science, covering studies published up to September 30, 2024. We included peer-reviewed observational studies evaluating the link between cannabis consumption and the risk of asthma diagnosis. Data synthesis employed a random-effects meta-analysis to account for heterogeneity. R statistical software (version 4.4) was used for statistical analyses. Results: The search yielded 8 relevant studies after screening 1,887 records. The pooled odds ratio (OR) for the association between cannabis consumption and the risk of asthma diagnosis was 1.31, 95% confidence interval (CI): 1.19–1.44, indicating greater odds of having asthma compared to non-users. Moderate heterogeneity was observed (I² = 46%), and sensitivity analysis confirmed the robustness of the findings. Conclusion: This systematic review and meta-analysis identifies a significant association between cannabis use and greater odds of having asthma. These findings emphasize the importance of raising awareness about the potential respiratory risks associated with cannabis use. Future research should prioritize identifying moderating factors, such as the frequency and mode of cannabis consumption, to enhance understanding of this association and provide a stronger evidence base for potential public health interventions. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

Incorporating stochastic processes into biological models is crucial for capturing the inherent variability and uncertainties within biological systems. This paper explores the benefits of introducing Black–Karasinski process into chikungunya virus infection modeling. By utilizing the Black–Karasinski process researchers can capture the inherent variability in biological processes and account for uncertainties. This paper highlights the advantages of Black–Karasinski processes in biological modeling. We investigate the dynamical behavior of a stochastic model for chikungunya virus infection incorporating a Black–Karasinski process. Firstly, we establish sufficient conditions for the existence of a stationary distribution in the model. By solving the corresponding Fokker–Planck equation we obtain the local probability density function near the quasi-endemic equilibrium, which provides insights into the statistical characteristics of the stochastic system. Additionally, we present sufficient conditions for the extinction of infected host cells and chikungunya virus particles. Finally, we supplement the analytical results with numerical simulations to investigate the impact of random noise. [ABSTRACT FROM AUTHOR]

Missing data widely exist in industrial problems and lead to difficulties in further modeling and analysis. Recently, a number of deep learning methods have been proposed for missing data imputation and have shown promising performance in various scenarios. Nevertheless, the inputs of imputation networks of these methods are usually incomplete data filled with zeros, which means the missing values always affect the output of the network and the network should be sufficiently large to have a strong denoising ability. Thus, these methods may not provide satisfactory imputation when the missing rate is high. In this work, we present a novel method called GANImputer for missing data imputation. The method is based on the generative adversarial network, and the input of the imputation network, namely a generator, is not incomplete data but a low-dimensional latent variable that can be optimized. The optimization process is composed of three stages. First, we optimize a generator via adversarial training. Second, the latent variable is optimized, while the generator is fixed. Finally, we fine-tune the generator and latent variable jointly. To analyze the theoretical mechanism of our GANImputer, we provide a generalization error bound with respect to missing not at random, which is practical and meaningful. Our method is tested on five diverse benchmark datasets and the Tennessee Eastman process and outperforms a few deep learning based imputation methods. [ABSTRACT FROM AUTHOR]

Objectives: The interrupted time series (ITS) design is commonly used to investigate the impact of an intervention or exposure in public health. There are many statistical methods that can be used to analyse ITS data and to meta-analyse their results. We undertook two empirical studies to investigate: (i) how effect estimates (and associated statistics) compared when six statistical methods were applied to 190 real-world datasets; and (ii) how meta-analysis effect estimates (and associated statistics) compared when the combinations of two ITS analysis methods and five meta-analysis methods were applied to 17 real-world meta-analyses including 283 ITS datasets. Here we present a curated repository of a subset of ITS datasets from these studies. Data description: The repository includes 430 ITS datasets curated from the two empirical studies. The datasets are diverse in the populations, interruptions and outcomes examined, and are methodologically diverse in the outcome types, aggregation time intervals, number of timepoints and segments. Most of the datasets are from public health. For each dataset, we provide the outcome value at each timepoint and the segment (indicating different interruptions), along with characteristics of the dataset. This repository may be of value for future research of ITS studies, and as a source of examples of ITS for use in teaching. [ABSTRACT FROM AUTHOR]

Background: Logistic regression is a useful statistical technique commonly used in many fields like healthcare, marketing, or finance to generate insights from binary outcomes (e.g., sick vs. not sick). However, when applying logistic regression to complex survey data, which includes complex sampling designs, specific methodological issues are often overlooked. Methods: The systematic review extensively searched the PubMed and ScienceDirect databases from January 2015 to December 2021, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, focusing primarily on the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). 810 articles met the inclusion criteria and were included in the analysis. When discussing logistic regression, the review considered multiple methodological problems such as the model adequacy assessment, handling dependence of observations, utilization of complex survey design, dealing with missing values, outliers, and more. Results: Among the selected articles, the DHS database was used the most (96%), with MICS accounting for only 3%, and both DHS and MICS accounting for 1%. Of these, it was found that only 19.7% of the studies employed multilevel mixed-effects logistic regression to account for data dependencies. Model validation techniques were not reported in 94.8% of the studies with limited uses of the bootstrap, jackknife, and other resampling methods. Moreover, sample weights, PSUs, and strata variables were used together in 40.4% of the articles, and 41.7% of the studies did not use any of these variables, which could have produced biased results. Goodness-of-fit assessments were not mentioned in 75.3% of the articles, and the Hosmer–Lemeshow and likelihood ratio test were the most common among those reported. Furthermore, 95.8% of studies did not mention outliers, and only 41.0% of studies corrected for missing information, while only 2.7% applied imputation techniques. Conclusions: This systematic review highlights important gaps in the use of logistic regression with complex survey data, such as overlooking data dependencies, survey design, and proper validation techniques, along with neglecting outliers, missing data, and goodness-of-fit assessments, all of which point to the need for clearer methodological standards and more thorough reporting to improve the reliability of results. Future research should focus on consistently following these standards to ensure stronger and more dependable findings. [ABSTRACT FROM AUTHOR]

Networks where each node has one or more associated numerical values are common in applications. This work studies how summary statistics used for the analysis of spatial data can be applied to non-spatial networks for the purposes of exploratory data analysis. We focus primarily on Moran-type statistics and discuss measures of global autocorrelation, local autocorrelation and global correlation. We introduce null models based on fixing edges and permuting the data or fixing the data and permuting the edges. We demonstrate the use of these statistics on real and synthetic node-valued networks. [ABSTRACT FROM AUTHOR]

A large body of literature is available quantifying microplastic contamination in freshwater and marine systems across the globe. "Microplastics" do not represent a single analyte. Rather, they are usually operationally defined based on their size, polymer and shape, dependent on the sample collection method and the analytical range of the measurement technique. In the absence of standardised methods, significant variability and uncertainty remains as to how to compare data from different sources, and so consider exposure correctly. To examine this issue, a previously compiled database containing 1603 marine observations and 208 freshwater observations of microplastic concentrations from across the globe between 1971 and 2020 was analysed. Reported concentrations span nine orders of magnitude. Investigating the relationship between sampling methods and reported concentrations, a striking correlation between smaller sample unit volumes and higher microplastic concentrations was observed. Close to half of the studies reviewed scored poorly in quality scoring protocols according to the sample volume taken. It is critical that sufficient particles are measured in a sample to reduce the errors from random chance. Given the inverse relationship with particle size and abundance, the volume required for a representative sample should be calculated case-by-case, based on what size microplastics are under investigation and where they are being measured. We have developed the Representative Sample Volume Predictor (RSVP) tool, which standardises statistical prediction of sufficient sample volumes, to ensure microplastics are detected with a given level of confidence. Reviewing reports in freshwater, we found ~ 12% of observations reported sample volumes which would have a false negative error rate > 5%. Such sample volumes run the risk of wrongly concluding that microplastics are absent in samples and are not sufficient to be quantitative. The RSVP tool also provides a harmonised Poisson point process estimation of confidence intervals to test whether two observations are likely to be significantly different, even in the absence of replication. In this way, we demonstrate application of the tool to evaluate historic data, but also to assist in new study designs to ensure that environmental microplastic exposure data is relevant and reliable. The tool can also be applied to other data for randomly dispersed events in space or time, and so has potential for transdisciplinary use. [ABSTRACT FROM AUTHOR]

Background: This study aimed to assess possible changes in mineral intake correlation between family pairs over time. Mineral intake was measured using a food frequency questionnaire. Methods: FCOR command of the Statistical Analysis for Genetic Epidemiology software was used to determine the correlation coefficients of minerals in relative pairs. Our first analysis was conducted on adults (n = 748) who had four complete dietary and familial data on the third to sixth surveys of the Tehran Lipid and Glucose Study. In addition, we assessed respectively the 3-year, 6-year, and 9-year changes in mineral correlations among populations with n = 1773, n = 1672, and n = 1352 samples with two mineral intake measurements. Results: Most of the mineral correlations among various pairs were found to be in the same direction in both four and two measurement analyses. Among the mother-daughter and brother-sister pairs, there was a decreasing correlation for all minerals over time. In father-daughter, we observed decreasing or regression to the mean of correlations. For father-son and spouse pairs, the correlations between minerals showed a decreasing trend or regression to the mean over time, whereas increased for sodium in the father-son and phosphorus in the spouse pair. For mother-son and brother-brother pairs, iron intake correlation increased and other minerals correlations generally showed a decreasing trend or regression to the mean. Among sister-sister pairs, the correlation of calcium and phosphorus decreased, however, the correlation of chromium, selenium, sodium, and potassium increased. Conclusion: Our study revealed a decreasing trend in correlation or regression towards the mean over time for most minerals in family pairs. [ABSTRACT FROM AUTHOR]

The document discusses the significance of stochastic processes in various scientific disciplines and their mathematical interpretation of randomness and probabilistic analysis. It highlights the historical development of stochastic processes, starting with Brownian motion and the contributions of key figures like Louis Bachelier, Andrei Kolmogorov, Joseph Doob, and William Feller. The text also honors the distinguished mathematician Prof. Sally McClean for her significant contributions to mathematical modeling and health care planning, showcasing her research achievements and recognition in the field. Additionally, the document provides an overview of articles in a special volume dedicated to stochastic processes, categorized into key thematic areas like Markov Chains, Semi-Markov Chains, Mathematical Optimization, and General Stochastic Processes, with brief descriptions of selected articles in each category. [Extracted from the article]

Forecasting the stock market is one of the most challenging things for investors to do to increase their profits. The objective of this study is to predict the closing price of the stock using Long-Short-Term Memory (LSTM) network modified by Sin-Cosine Algorithm (SCA), Autoregressive Integrated Moving Average (ARIMA) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) statistical models which is called LSTM-SCA-ARIMA-GARCH model. The proposed model uniquely integrates advanced machine learning and statistical techniques to enhance stock price prediction accuracy, setting it apart from traditional models. Unlike existing methods, this hybrid model captures both linear and nonlinear patterns, effectively managing volatility and optimizing prediction accuracy through its innovative structure. This study considers data from 12 different stocks: State Bank of India Network, Oracle Corporation, Microsoft Corporation, Halliburton Company, Goldman Sachs Group Inc, Cognizant Technology Solutions Corporation, Bank of America Corp, Amazon, Badische Anilin- und Sodafabrik, Bayer AG, Deutsche Bank AG, and Saipa Automobile Company (Khodro sazi Saipa) (Khsapa). There are both daily and weekly predictions, but daily predictions are more accurate. Additionally, we utilize and evaluate four datasets from UCI (AI4I, Garment, S&P, and RUSSELL) using RMSE and R2 metrics, and also extended the prediction to the next 15 days. The proposed model demonstrates substantial improvements in daily predictions, outperforming the traditional LSTM, LSTM combined with Particle Swarm Optimization, and LSTM-ARIMA-GARCH models by 83.37%, 84.05%, and 55.8%, respectively. Additionally, LSTM-SCA-ARIMA-GARCH outperforms LSTMNP, AHMPSO-LSTM, and DLWR-LSTM. This innovation holds promise for more reliable and precise stock market forecasts. [ABSTRACT FROM AUTHOR]

Thermal errors have become the main factor affecting the machine tool accuracy. Statistical prediction and compensation models are commonly established based on the measured temperature data to reduce thermal errors. Current literature typically focuses on first selecting temperature-sensitive points (TSPs) to reduce multicollinearity and then building thermal error models for CNC machines. Thus, this two-step approach loses useful information after the variable selection and prevents thermal error models from using this lost information. In addition, there are few approaches that can simultaneously reduce multicollinearity through variable selection and model thermal errors in one single step. Therefore, to fill the research gap, a one-shot thermal error modeling and prediction approach is proposed based on the individually penalized ridge regression (IPRR). Specifically, the traditional ridge regression method, which intrinsically fails to select TSPs, is adopted and modified by setting up individually penalized ridge parameters for each variable, thereby achieving both variable selection and thermal error modeling simultaneously. Then, the proposed IPRR algorithm is compared using the experimental data with the existing methods. The comparison results show that the IPRR algorithm can significantly improve the prediction accuracy by 10% and robustness by 40% on thermal errors. Finally, the thermal error compensation experiments are conducted on the experimental object to show the practicability of the IPRR algorithm. [ABSTRACT FROM AUTHOR]

This study proposes a multi-stage stochastic production planning approach for a joint lot sizing and workforce scheduling problem under demand uncertainty. Scenario trees are used to model uncertainty in demand, and a multi-stage scenario-based stochastic linear program is developed. This model allows for both here-and-now and wait-and-see decisions providing flexibility for decision-makers to adjust production quantities according to the realized portion of demand and improve the overall effectiveness of production planning by better managing the number of active lines, workforce, and inventory levels. A matheuristic is developed for large-sized instances, which yields near-optimal solutions in practicable computation times. The proposed methods are demonstrated over a real data set taken from a Turkish home and professional appliances company, Vestel. The results show significant improvements in cost and CPU time performances for benchmark approaches, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: Data from observational and clinical studies indicate an association between skin microbiota and hidradenitis suppurativa (HS). However, the causal relationship between skin microbiota and HS remains to be elucidated. Methods: We obtained data on skin microbiota and HS from summary statistics of genome-wide association studies and applied Mendelian randomization (MR) statistical methods to assess causality. Specifically, we employed both full MR and inverse MR methods, utilizing five statistical approaches, including inverse variance weighting, to evaluate causality. Furthermore, sensitivity analyses were conducted to validate the MR findings. Results: Inverse variance weighted analysis revealed that the order Burkholderiales (OR = 0.922, 95% CI 0.855–0.994, P = 0.033), the genus Enhydrobacter (OR = 0.897, 95% CI 0.815–0.986, P = 0.025), ASV037 (OR = 0.926, 95% CI 0.863–0.995, P = 0.036), and ASV016 (OR = 0.932, 95% CI 0.870–0.998, P = 0.043) exhibited a protective effect against HS. Conversely, ASV022 (OR = 1.098, 95% CI 1.027–1.175, P = 0.007) was identified as a risk factor. Conclusion: Our analysis suggests a possible causal relationship between specific skin microbiota and HS. These findings shed light on the potential pathogenesis and therapeutic interventions. [ABSTRACT FROM AUTHOR]

In the past decade, financial institutions have invested significant efforts in the development of accurate analytical credit scoring models. The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure. Despite continuing efforts, the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution's internal data. We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction. To contribute to the field of financial innovation, this study proposes a Dimension Reduction Assisted Credit Scoring (DRA-CS) method via distance covariance-based sufficient dimension reduction (DCOV-SDR) in Majorization-Minimization (MM) algorithm. First, in the presence of a large number of variables, the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction. Second, when the DRA-CS method is employed with logistic regression, it outperforms existing methods based on different variable selection techniques. This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods. [ABSTRACT FROM AUTHOR]

Financial time series have been analyzed with a wide variety of models and approaches, some of which can forecast with great accuracy. However, most of these models, especially the machine learning ones, cannot show additional information for the decision maker or the financial analyst. The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts. In this study, we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series. Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk, as well as machine learning approaches without the proposed methodology. This improvement is statistically significant, as indicated by the Model Confidence Set test in the complete test set and quarterly analysis. [ABSTRACT FROM AUTHOR]

This systematic review presents the first synthesis of the scientific literature on estimating school and teacher/class effects on student academic performance using random-effects (RE) models with three or more levels. The review delves into the theoretical framework underpinning the estimation of educational effects, the associated statistical methods, and the empirical findings from multilevel analyses to date. It includes 94 analyses in 32 papers from diverse geographical regions, focusing on both primary and secondary education levels. The findings reveal a broad spectrum of Variance Partition Coefficients (VPCs) from RE models, highlighting the variability in the impact of school and teacher/class levels on student outcomes. The review emphasizes the necessity for comprehensive and diverse datasets, particularly the inclusion of detailed teacher-level data in African studies and the utilization of robust official educational databases in Chinese research. It also acknowledges the contributions of Latin American countries in advancing multilevel modeling and the holistic approach to educational analysis demonstrated in Cyprus. The conclusions underscore the importance of contextualizing educational policies and practices based on the multifaceted influences on student performance. The review calls for future research to expand data collection efforts, adopt standardized reporting practices, and explore advanced statistical techniques to address methodological challenges and approximate causal relationships more accurately. [ABSTRACT FROM AUTHOR]

A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~108 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤102 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale "strength-on-predictors" regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study. [ABSTRACT FROM AUTHOR]

Background: Multiple trauma can cause an increase in creatine phosphokinase (CPK) and subsequently rhabdomyolysis and acute kidney injury (AKI). This study was designed to evaluate the effect of vitamin D3 on the serum CPK level and the incidence of rhabdomyolysis-induced AKI in patients with multiple trauma. Methods: Patients with serum CPK levels <1000 IU/L were followed as the control 1 group. Subjects with serum CPK levels ≥1000 IU/L were randomly allocated to the control 2 or intervention group at Imam Hossein Medical Center, Tehran, Iran in 2020. Patients in the intervention group received a single dose of vitamin D3 (300,000 units) on the recruitment day. The serum level of CPK was recorded every 3 days for 14 days. Parametric and non-parametric tests were used to compare the CPK values between groups. Results: Forty-six patients, consisting of 16, 15, and 15 in control 1, control 2, and intervention arms of the study were recruited, respectively. Unlike control groups, the significant steadily decreasing trend was seen only in the intervention group (P<0.001). This significant decrease in the intervention arm was observed on days 5 to 7 (P=0.001) and on days 8 to 10 (P<0.001) compared to the baseline. Patients in the intervention group had a lower number of AKI or need for dialysis (P=0.869 and P=0.670 for AKI and dialysis, respectively) than control group 2, although the differences were not significant. Conclusion: The current study revealed that vitamin D3, could prevent the increasing trend of CPK during the first days and accelerate the normalization of CPK in patients with elevated CPK due to multiple trauma. [ABSTRACT FROM AUTHOR]

The Progressive Masked Voice Exercises (PMVE) with an innovative semioccluded ventilation mask fitted and adjustable positive end-expiratory pressure (PEEP) valve was evaluated. This study aimed to compare the effectiveness of the PMVE with the PEEP device and the Vocal Function Exercise (VFE) program on acoustic, auditory-perceptual, aerodynamic, and self-report measures. Twenty-five participants diagnosed with voice disorders met the criteria. Participants were randomly assigned to either the PMVE or the VFE group for a 6-week home therapy program. Pre- and post-data were analyzed with parametric and nonparametric statistics. Acoustic and aerodynamic measures showed no between-group or interaction group × time effects; however, a main effect of time was observed for all but one of the eight acoustic variables, indicating that both groups improved. Additional within-group analysis showed improvements in two of the eight variables for the PMVE program and four for the VFE program. No between-group differences were observed for the auditory-perceptual judgments using the GRBASI scale; however, the strain was improved for the VFE group. No differences in self-report measures were also seen, except for the VFE group. This study provides preliminary evidence for the PMVE therapy program. Further research is needed in large and diverse samples and clinical application is invited. [ABSTRACT FROM AUTHOR]

In this paper, we develop a stochastic multi-molecule chemical reaction model with reaction rate perturbed by log-normal O r n s t e i n - U h l e n b e c k process in order to consider the effects of random factors on chemical reaction dynamics. Firstly, we prove the existence and uniqueness of the global positive solution for the stochastic model. In addition, we obtain the conditions under which the corresponding stochastic system exist a stationary distribution. Then, we derive a sufficient condition to end the reaction. Furthermore, the stochastic system has been transformed into a linearized system, by solving F o k k e r - P l a n c k equation, we obtain the exact expression of the density function around the quasi-equilibrium of this system. Finally, we draw a conclusion that the dynamical behaviors of the stochastic system will be affected by random factor, O r n s t e i n - U h l e n b e c k process respectively [ABSTRACT FROM AUTHOR]

Rainfall disaggregation techniques are employed to convert low-resolution rainfall data into high-resolution rainfall series when high-resolution data is not readily available, enabling a more detailed analysis of rainfall patterns and intensities. The Microcanonical Multiplicative Random Cascade (MMRC) model is a commonly used stochastic method for disaggregating rainfall data into finer temporal or spatial scales. The traditional MMRC model overestimates extreme rainfall values due to its reliance on predefined classes. To mitigate this issue, clustering techniques are employed to classify rainfall data based on similar characteristics. The integration of K-means clustering into the traditional MMRC model has led to notable enhancements in capturing extreme rainfall characteristics. Temperature, wind speed, and humidity, among other factors, play crucial roles in influencing rainfall patterns. By incorporating these data into the clustering process, this study aims to classify rainfall more accurately, resulting in improved preservation of extreme rainfall characteristics. The integration of additional weather parameters into K-means clustering enhanced the statistical properties of the rainfall series generated for German cities. In contrast, the IDF curves produced by this model for the Indian cities exhibit greater reliability compared to those generated by the conventional MMRC model. However, the comparison of the modified MMRC-K models using the weather parameters with the MMRC-K model, did not show improvement in preserving extreme rainfall characteristics. Since the dataset required for this modified model is more extensive, the MMRC-K model becomes the more suitable option. [ABSTRACT FROM AUTHOR]