Match running performance preceding scoring and conceding a goal in men's professional soccer.

This study aimed to investigate the potential differences in the match running performance of professional soccer players 5 min. before scoring and conceding a goal in the Polish Ekstraklasa. The sample consisted of 278 matches with 570 goals scored during official matches of the 2022/23 Polish Ekstraklasa season. All data was collected utilising the computerised multiple-camera optical tracking system TRACAB. Total distance covered (TD), standing distance (StD; < 0.72 km h-1), walking distance (WD; 0.73-7.2 km h-1), jogging distance (JG; 7.21-14.4 km h-1), running distance (RD; 14.41-19.8 km h-1), high-speed running distance (HSR; 19.81-25.2 km h-1) and sprinting distance (SprD; > 25.2 km h-1) were analysed in 5-min intervals prior to a goal scored for both teams. The employed linear mixed models showed that all examined match-running performance metrics were higher in teams that scored a goal compared to teams that conceded a goal. Within 5 min before scoring a goal in Polish Ekstraklasa matches, the scoring team produced significantly greater TD (∆ 95%CI 256.8-300.4 m; p = 0.001), WD (∆ 95%CI 52.3-95.8 m; p = 0.001), JG (∆ 95%CI 100.5-144.1 m; p = 0.001) and RD (∆ 95%CI 16.2-59.8 m; p = 0.001) compared to the conceding team, although no differences were found for HSR and SprD. These results demonstrate the enhanced identification potential of key physical performance factors influencing goal scoring in the Polish Ekstraklasa, thereby optimising the training process and improving overall performance. To enhance the effectiveness of soccer training, coaching and performance staff should consider this study's findings, that indicate an increase in the volume of medium- and low-intensity running efforts preceding a goal.

Epidemiological Characteristics of 101,471 Patients Hospitalized with Chronic Obstructive Pulmonary Disease (COPD) in Poland in 2019: Multimorbidity, Duration of Hospitalization, In-Hospital Mortality.

Chronic obstructive pulmonary disease (COPD) is a common lung disease. There is a limited amount of nationwide data on COPD patients in Poland. This study aimed to characterize patients hospitalized with COPD in Poland in 2019 as well as to identify factors associated with the risk of in-hospital death and prolonged hospitalization among patients with COPD. This study is a retrospective database analysis. Data on patients hospitalized with COPD in Poland were obtained from the Nationwide General Hospital Morbidity Dataset. Data on all adults aged ≥40 years with a diagnosis of COPD from a physician (J44 code) were included in the analysis. Data were analyzed separately for patients hospitalized due to COPD (primary diagnosis) and patients with COPD as a comorbidity (secondary diagnosis). Completed medical records were available for 101,471 patients hospitalized with COPD (36.9% were females). Of those, 32% were hospitalized due to COPD. The mean age was 71.4 ± 9.7 years. The mean duration of hospitalization was 9.4 ± 11.4 days (median 7 days). Most of the COPD patients (89.3%) had at least one comorbidity. The in-hospital mortality rate was 6.8%. Older age, presence of cardiovascular diseases, and diseases of the genitourinary system (p < 0.05) were the most important factors associated with the risk of in-hospital death among patients hospitalized due to COPD.

Cellular Automaton Mimicking Colliding Bodies for Topology Optimization.

Needs and demands of contemporary engineering stimulate continuous and intensive development of design methods. Topology optimization is a modern approach which has been successfully implemented in a daily engineering design practice. Decades of progress resulted in numerous applications of topology optimization to many research and engineering fields. Since the design process starts already at the conceptual stage, innovative, efficient, and versatile topology algorithms play a crucial role. In the present study, the concept of the original heuristic topology generator is proposed. The main idea that stands behind this proposal is to take advantage of the colliding bodies phenomenon and to use the governing laws to derive original Cellular Automata rules which can efficiently perform the process of optimal topologies generation. The derived algorithm has been successfully combined with ANSYS, a commercial finite element software package, to illustrate its versatility and to make a step toward engineering applications. Based on the results of the tests performed, it can be concluded that the proposed concept of the automaton mimicking colliding bodies may be an alternative algorithm to other existing topology generators oriented toward engineering applications.

Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence.

Background: The prediction of the number of acute coronary syndromes (ACSs) based on the weather conditions in the individual climate zones is not effective. We sought to investigate whether an artificial intelligence system might be useful in this prediction. Methods: Between 2008 and 2018, a total of 105,934 patients with ACS were hospitalized in Lesser Poland Province, one covered by two meteorological stations. The predicted daily number of ACS has been estimated with the Random Forest machine learning system based on air temperature (°C), air pressure (hPa), dew point temperature (Td) (°C), relative humidity (RH) (%), wind speed (m/s), and precipitation (mm) and their daily extremes and ranges derived from the day of ACS and from 6 days before ACS. Results: Of 840 pairwise comparisons between individual weather parameters and the number of ACS, 128 (15.2%) were significant but weak with the correlation coefficients ranged from -0.16 to 0.16. None of weather parameters correlated with the number of ACS in all the seasons and stations. The number of ACS was higher in warm front days vs. days without any front [40 (29-50) vs. 38 (27-48), respectively, P < 0.05]. The correlation between the predicted and observed daily number of ACS derived from machine learning was 0.82 with 95% CI of 0.80-0.84 (P < 0.001). The greatest importance for machine learning (range 0-1.0) among the parameters reached Td daily range with 1.00, pressure daily range with 0.875, pressure maximum daily range with 0.864, and RH maximum daily range with 0.853, whereas among the clinical parameters reached hypertension daily range with 1.00 and diabetes mellitus daily range with 0.28. For individual seasons and meteorological stations, the correlations between the predicted and observed number of ACS have ranged for spring from 0.73 to 0.77 (95% CI 0.68-0.82), for summer from 0.72 to 0.76 (95% CI 0.66-0.81), for autumn from 0.72 to 0.83 (95% CI 0.67-0.87), and for winter from 0.76 to 0.79 (95% CI 0.71-0.83) (P < 0.001 for each). Conclusion: The weather parameters have proven useful in predicting the prevalence of ACS in a temperate climate zone for all the seasons, if analyzed with an artificial intelligence system. Simultaneously, the analysis of individual weather parameters or frontal scenarios has provided only weak univariate relationships. These findings will require validation in other climatic zones.

Weather as a potential cause of regional differences in the dynamics of COVID-19 transmission in Poland: implications for epidemic forecasting.

INTRODUCTION: COVID­19 is an infectious disease caused by SARS-CoV-2. Little is known on the impact of weather conditions on the transmission of COVID­19. OBJECTIVES: We aimed to assess correlations between 6 different meteorologic parameters and the transmission dynamics of the COVID­19 pandemic in 16 administrative regions (voivodeships) of Poland. PATIENTS AND METHODS: Data for analysis were obtained from epidemiologic reports of the Polish Ministry of Health. For each voivodeship, one synoptic station was selected to provide meteorologic data on daily maximum and minimum temperatures, variability of daily temperature, sunshine duration, relative humidity, and wind speed. The periods with significant weather impact were determined using multiple linear regression. Cross­correlation function (CCF) and random forest models were used to assess correlations between meteorologic parameters and the incidence of COVID­19 as well as the number of hospitalizations for COVID­19. RESULTS: In all voivodeships, the incidence of new COVID­19 cases correlated with relative humidity (CCF = 0.41), daily maximum temperature (CCF = -0.41), variability of daily temperature (CCF = -0.40), and sunshine duration (CCF = 0.35). For all parameters, a similar time lag of 10 to 14 days was noted. There were no significant correlations for wind speed in most voivodeships. The risk of hospitalization for COVID­19 correlated with daily maximum temperature (CCF = -0.48; time lag, 10 days) and sunshine duration (CCF = -0.45; time lag, 10 days). CONCLUSIONS: The delayed effects of the meteorologic factors on the incidence of COVID­19 and the risk of hospitalization for COVID­19 were observed. In each voivodeship, the dynamics of COVID­19 transmission was most strongly affected by relative humidity and daily maximum temperature.

Impact of Meteorological Conditions on the Dynamics of the COVID-19 Pandemic in Poland.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the novel coronavirus. The role of environmental factors in COVID-19 transmission is unclear. This study aimed to analyze the correlation between meteorological conditions (temperature, relative humidity, sunshine duration, wind speed) and dynamics of the COVID-19 pandemic in Poland. Data on a daily number of laboratory-confirmed COVID-19 cases and the number of COVID-19-related deaths were gatheredfrom the official governmental website. Meteorological observations from 55 synoptic stations in Poland were used. Moreover, reports on the movement of people across different categories of places were collected. A cross-correlation function, principal component analysis and random forest were applied. Maximum temperature, sunshine duration, relative humidity and variability of mean daily temperature affected the dynamics of the COVID-19 pandemic. An increase intemperature and sunshine hours decreased the number of confirmed COVID-19 cases. The occurrence of high humidity caused an increase in the number of COVID-19 cases 14 days later. Decreased sunshine duration and increased air humidity had a negative impact on the number of COVID-19-related deaths. Our study provides information that may be used by policymakers to support the decision-making process in nonpharmaceutical interventions against COVID-19.

GHOST-Gate to Hybrid Optimization of Structural Topologies.

Although well-recognized in the fields of structural and material design and widely present in engineering literature, topology optimization still arouses a high interest within research communities. Moreover, it is observed that the development of innovative, efficient and versatile methods is one of the most important issues stimulating progress within the topology optimization area. Following this activity, in the present study, a concept of a hybrid algorithm developed in order to generate optimal structural topologies of minimal compliance is presented. The hybrid algorithm is built based on two existing approaches. The first one makes use of the formal optimality criterion, whereas the second one utilizes a special heuristic rule of design variables updating. The main idea that stands behind the concept of the present proposal is to take the advantage of both algorithms capabilities. In a numerical implementation of the hybrid algorithm, the design variables are updated at each iteration step using both approaches, and the solution with a lower objective function value is selected for the next iteration. The numerical tests of the generation of minimal compliance structures have been performed for chosen structures including a real engineering one. It has been confirmed that the proposed hybrid technique based on switching between the considered rules allows the final structures having lower values of compliance as compared with the results of an application of basic algorithms running separately to be obtained. Moreover, based on promising results of the tests performed, one can consider the proposed concept of a hybrid algorithm as an alternative for other existing topology generators.