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Previous research emphasizes the significance of key performance metrics in determining match outcomes. The purpose of this study is to enhance the understanding of success in professional soccer by analyzing the relationship between match outcomes (win, lose, draw) and various Performance Indicators extracted from the Greek soccer league, as well as to develop a regression model of success in soccer. The sample consisted of all 91 matches from the first round of the 2020-2021 season of the Greek Football League. Utilizing Kruskal-Wallis tests, significant differences were found in goals scored, shots, and shots on target, ball possession, passing metrics, touches in the penalty area, and average shot distance (p < 0.05), with winning teams having demonstrated superior performance metrics. Moreover, winning teams engaged more in positional attacks and counterattacks with shots (p < 0.05). The binary logistic regression model applied to predict match outcomes identified shots on target, counterattacks, passes metrics, offensive duels and set pieces (penalties, free kicks) as key factors influencing the likelihood of winning (p < 0.05). These findings collectively highlight the importance of effective offensive play, including goal scoring, shooting accuracy, and ball possession, in determining the outcomes of soccer matches, with the regression model offering a nuanced understanding of these relationships.

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The aim of the study was to describe the relationship between success in junior and senior categories in sprint events. An observational and longitudinal analysis was carried out using rankings of the Royal Spanish Athletics Federation database. We analysed 547 sprinters (238 women and 309 men) from their U14 to senior stage who ranked in top-20 on at least one occasion during the period 2004 to 2021. The first entry in top-20 occurred mostly in U14 (44.4%, 243), and the frequency was progressively lower: 26.7% (146) in U16; 20.8% (114) in U18; 5.1% (28) in U20; 2% (11) in U23; and 0.9% (5) in the senior category. A similar tendency was observed in male and female athletes. Only 3.8% (9) of top-20 U14 athletes reached the senior elite stage, increasing this percentage in subsequent categories: 7.4% (15) in U16; 10.6% (24) in U18; 20.9% (32) in U20, and 31.4% (32) from U23 to the senior category. Data from female athletes showed higher maintenance of top-20 status from early categories to senior age. We conclude that even though the first entry into the national top-20 in sprint events occurs early in most cases, success in these initial stages is not a prerequisite for reaching top-20 positions in the senior category.

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The success of the business directly contributes towards the growth of the nation. Hence it is important to evaluate and predict whether the business will be successful or not. In this study, we use the company's dataset which contains information from startups to Fortune 1000 companies to create a machine learning model for predicting business success. The main challenge of business success prediction is twofold: (1) Identifying variables for defining business success; (2) Feature selection and feature engineering based on Investor-Business-Market interrelation to provide a successful outcome of the predictive modeling. Many studies have been carried out using only the available features to predict business success, however, there is still a challenge to identify the most important features in different business angles and their interrelation with business success. Motivated by the above challenge, we propose a new approach by defining a new business target based on the definition of business success used in this study and develop additional features by carrying out statistical analysis on the training data which highlights the importance of investments, business, and market features in forecasting business success instead of using only the available features for modeling. Ensemble machine learning methods as well as existing supervised learning methods were applied to predict business success. The results demonstrated a significant improvement in the overall accuracy and AUC score using ensemble methods. By adding new features related to the Investor-Business-Market entity demonstrated good performance in predicting business success and proved how important it is to identify significant relationships between these features to cover different business angles when predicting business success.

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Mountaineering is a sport of contrary forces: teamwork plays a large role in mental fortitude and skills, but the actual act of climbing, and indeed survival, is largely individualistic. This work studies the effects of the structure and topology of relationships within climbers on the level of cooperation and success. It does so using simplicial complexes, where relationships between climbers are captured through simplices that correspond to joint previous expeditions with dimension given by the number of climbers minus one and weight given by the number of occurrences of the simplex. First, this analysis establishes the importance of relationships in mountaineering and shows that chances of failure to summit reduce drastically when climbing with repeated partners. From a climber-centric perspective, it finds that climbers that belong to simplices with large dimension were more likely to be successful, across all experience levels. Then, the distribution of relationships within a group is explored to categorize collective human behavior in expeditions, on a spectrum from polarized to cooperative. Expeditions containing simplices with large dimension, and usually low weight (weak relationships), implying that a large number of people participated in a small number of joint expeditions, tended to be more cooperative, improving chances of success of all members of the group, not just those that were part of the simplex. On the other hand, the existence of small, usually high weight (i.e., strong relationships) simplices, subgroups lead to a polarized style where climbers that were not a part of the subgroup were less likely to succeed. Lastly, this work examines the effects of individual features (such as age, gender, climber experience etc.) and expedition-wide factors (number of camps, total number of days etc.) that are more important determiners of success in individualistic and cooperative expeditions respectively. Centrality indicates that individual features of youth and oxygen use while ascending are the most important predictors of success. Of expedition-wide factors, the expedition size and number of expedition days are found to be strongly correlated with success rate.

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A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learning experience and widening their educational prospects, but also an opportunity to gain insights into students' learning processes with learning analytics. This study contributes to the topic of improving and understanding e-learning processes in the following ways. First, we demonstrate that accurate predictive models can be built based on sequential patterns derived from students' behavioral data, which are able to identify underperforming students early in the course. Second, we investigate the specificity-generalizability trade-off in building such predictive models by investigating whether predictive models should be built for every course individually based on course-specific sequential patterns, or across several courses based on more general behavioral patterns. Finally, we present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable to predict student performance with high levels of accuracy, reaching 90% for course-specific models.

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BACKGROUND: Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice. OBJECTIVES: This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible. METHODS: This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital's Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., "patients weaned within 24 h" (n = 1042) and "patients not weaned within 24 h" (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model. RESULTS: The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables. CONCLUSIONS: The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment.

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INTRODUCTION: A successful arteriovenous fistula is essential for effective haemodialysis. We aim to validate the existing failure to maturation equation and to propose a new clinical scoring system by evaluating arteriovenous fistula success predictors. METHODS: Data of end-stage renal disease patients initiated on haemodialysis from January 2010 to December 2012 were retrospectively obtained from medical records with follow-up until 1 January 2014. Application of the failure to maturation equation was evaluated. A nomogram was developed using arteriovenous fistula success predictors and was calibrated with a bootstrapping technique. RESULTS: A total of 694 patients were included with mean duration of follow-up of 2.3 years. Arteriovenous fistula maturation was achieved by 542 patients (78%). Comparing our cohort with the failure to maturation cohort, there were statistically significant differences in mean age, ethnicity and presence of diabetes mellitus. The failure to maturation equation failed to predict arteriovenous fistula outcomes with area under the curve performance of 0.519 on a receiver operating characteristic curve. Multivariate logistic regression showed that Malay patients (odds ratio = 0.628; 95% confidence interval = 0.403-0.978; p < 0.05) and patients requiring preoperative vein mapping (odds ratio = 0.601; 95% confidence interval = 0.410-0.883; p < 0.01) had a lower chance of arteriovenous fistula success, whereas male gender (odds ratio = 1.526; 95% confidence interval = 1.040-2.241; p < 0.05) and presence of postoperative good thrill (odds ratio = 3.137; 95% confidence interval = 2.127-4.625; p < 0.0001) had a higher chance of arteriovenous fistula success. The derived nomogram predicted arteriovenous fistula success (odds ratio = 1.030; 95% confidence interval = 1.022-1.038; p < 0.0001) with the area under the curve of 0.695 on a receiver operating characteristic curve and an adequacy index of 99.86% (p < 0.0001). CONCLUSION: The failure to maturation equation was not validated in our cohort. The clinical utility of our proposed arteriovenous fistula scoring system requires external validation in larger studies.

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BACKGROUND: The risk factors for post-ERCP cholecystitis (PEC) have not been characterized. Hence, this study aimed to identify the potential risk factors for PEC. METHODS: The medical records of 4238 patients undergoing the first ERCP in a single center from January 2012 to December 2016 were analyzed in this study. A multivariate analysis was used to identify the risk factors. RESULTS: This study included 2672 patients who met the enrollment criteria. Of these, 36 patients (incidence rate of 1.35%) developed PEC within 2 weeks of the procedure. Univariate and multivariate analyses identified the following factors associated with PEC: history of acute pancreatitis [odds ratio (OR) = 2.60; 95% confidence interval (CI): 1.29-5.23], history of chronic cholecystitis (OR = 8.47; 95% CI: 2.54-28.24), gallbladder opacification (OR = 2.79; 95% CI: 1.37-5.70), biliary duct metallic stent placement (OR = 3.66; 95% CI: 1.78-7.54), and high leukocyte count before ERCP (OR = 1.10; 95% CI: 1.04-1.17). The prediction model incorporating these factors demonstrated an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.80-0.91). A prognostic nomogram was developed using the aforementioned variables to estimate the probability of PEC. CONCLUSIONS: The risk factors, including the history of acute pancreatitis, history of chronic cholecystitis, gallbladder opacification, biliary duct metallic stent placement, and high leucocyte counts before ERCP, increased the occurrence of PEC and were positive predictors for PEC. The constructed nomogram was used to estimate the risk of PEC, guiding the implementation of prophylactic measures to prevent PEC in clinical practice.

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Brainstem and midbrain nuclei are closely linked to effective cognitive performance and autonomic function. In the present study, we aimed to investigate indices of successful and unsuccessful response inhibition paying particular attention to the interplay between locus coeruleus (LC), ventral tegmental area (VTA)/substantia nigra (SN) and, most importantly, peripheral markers. We aimed to get insight in the predictive value of neural and physiological signals in response inhibition. A total of 35 healthy controls were recruited from the local community and a typical task of behavioral response inhibition (Go/No-Go paradigm) was applied. We used high-resolution fMRI, advanced brainstem analyses and specifically corrected for respiratory signal and cardiac noise. Our main results characterize specific neural activation patterns during successful and unsuccessful response inhibition especially comprising the anterior cingulate as well as the medial and lateral prefrontal cortex. A significant activation of the dopaminergic nuclei (VTA/SN) was found during error processing, but not during response inhibition. Most remarkably, specific neural activation patterns (i.e., dorsal anterior cingulate cortex) as well as accompanying autonomic indices (i.e., skin conductance response (SCR)) were identified to hold predictive information on an individual's performance. In summary, the importance of the VTA/SN during error processing was shown. Furthermore, autonomic indices and specific neural activation patterns may contain valuable information to predict task performance.

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Access to the circulation is an "Achilles' heel" of chronic hemodialysis. According to the current guidelines, autologous arteriovenous fistula is the best choice available. However, the impossibility of immediate use and the high rate of non-matured fistulas place fistula far from an ideal hemodialysis vascular access. The first attempt at constructing an angioaccess should result in functional access as much as possible. After failed attempts, patients and nephrologists lose their patience and confidence, which results in high percentage of central venous catheter use. Predictive models could help, but clinical judgment still remains crucial. Early referral to the nephrologist and vascular access surgeon, careful preoperative examinations, preparation of patients and duplex sonography mapping of the vessels are very important in the preoperative stage. In the operative stage, it is crucial to understand that angioaccess procedures should not be considered as minor procedures and these operations must be performed by surgeons with demonstrable interest and experience. In the postoperative stage, appropriate surveillance of the maturation process is also important, as well as good cannulation skills of the dialysis staff. The purpose of this review article is to stress the importance of success prediction in order to avoid unsuccessful attempts in angioaccess surgery.

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