We study competitions structured as hierarchically shaped single-elimination tournaments. We define optimal tournaments by maximizing attractiveness such that the topmost players will have the chance to meet in higher stages of the tournament. We propose a dynamic programming algorithm for computing optimal tournaments and we provide its sound complexity analysis. Based on the idea of the dynamic programming approach, we also develop more efficient deterministic and stochastic sub-optimal algorithms. We present experimental results obtained with the Python implementation of all the proposed algorithms regarding the optimality of solutions and the efficiency of the running time.

In addition to several societal and individual impacts, the pandemic of COVID-19 has greatly affected educational systems everywhere. The education in those uni In addition to several societal and individual impacts, the pandemic of COVID-19 has greatly affected educational systems everywhere. The education in those uni

Background/Objectives: Globally, healthcare systems face challenges in optimizing performance, particularly in the wake of the COVID-19 pandemic. This study focuses on the analysis and forecasting of key performance indicators (KPIs) for the County Emergency Clinical Hospital in Craiova, Romania. The study evaluates indicators such as average length of stay (ALoS), bed occupancy rate (BOR), number of cases (NC), case mix index (CMI), and average cost per hospitalization (ACH), providing insight into their dynamics and future trends. Methods: We performed statistical analyses on quarterly data from 2010 to 2023, employing descriptive statistics and stationarity tests (e.g., Dickey–Fuller), using ARIMA models to forecast each KPI, ensuring model validation through tests for autocorrelation, heteroscedasticity, and stationarity. The model selection prioritized Akaike and Schwarz criteria for robustness. Results: The findings reveal that ALoS and BOR demonstrate seasonality and are influenced by colder months, and it is expected that the ALoS will stabilize to around five days by 2025. Moreover, we predict that the BOR will range between 46 and 52%, reflecting these seasonal variations. The NC forecasts indicate a post-pandemic recovery but to below pre-pandemic levels, and we project the CMI to stabilize at around 1.54, suggesting a return to consistent case complexity. The ACH showed significant growth, particularly in the fourth quarter, driven by inflation and seasonal costs, and it is projected to reach more than RON 3000 by 2025. Conclusions: This study highlights the utility of ARIMA models in forecasting healthcare KPIs, enabling proactive resource planning and decision-making. The findings underscore the impact of seasonality and economic factors on hospital operations, offering valuable insights for improving efficiency and adapting to post-pandemic challenges.

Background/Objectives: Emergency Department (ED) attendance is characterized by substantial temporal variability, creating challenges for workforce scheduling, resource allocation and short-term operational planning. This study aimed to characterize the temporal patterns of daily ED presentations and to evaluate the short-term forecasting performance of SARIMA and Prophet models in a Romanian hospital setting. Methods: Daily ED visits recorded at the Craiova County Emergency Clinical Hospital between January 2025 and April 2026 were analyzed (485 consecutive days). The analytical framework combined descriptive statistics, normality assessment, the Kruskal–Wallis test, and post hoc Mann–Whitney U tests with Holm correction and time-series modeling. A SARIMA(1,0,5)(0,0,1)₇ model incorporating a Romanian legal-holiday dummy and weekly seasonality and a Prophet model incorporating Romanian holidays and weekly seasonality without yearly seasonality were evaluated using a common rolling-origin out-of-sample framework. Predictive accuracy was assessed using RMSE and MAPE, while differences in forecasting performance were examined using the Diebold–Mariano test with a HAC/Newey–West correction. Results: ED attendance differed significantly across weekdays (H(6) = 52.365, p < 0.001), with the highest mean attendance observed on Mondays (257.6 patients/day). Post hoc analysis confirmed significant differences between several weekday distributions after Holm adjustment, supporting a structured weekly pattern. Time-series analysis demonstrated serial dependence and recurrent weekly seasonality. Both forecasting models showed good short-term predictive performance across the four common validation windows. Over the complete out-of-sample period of 1–30 April 2026, SARIMA achieved an RMSE of 22.59 patients and a MAPE of 7.46%, compared with 23.54 patients and 7.56%, respectively, for Prophet. Although the aggregate results slightly favored SARIMA, the difference in predictive accuracy was not statistically significant (Diebold–Mariano test, p-value = 0.575). Conclusions: Daily ED attendance at SCJU Craiova exhibits systematic weekly variation, serial dependence and calendar-related effects that can be incorporated into short-term forecasting models. SARIMA and Prophet provided comparable out-of-sample predictive accuracy, with neither model demonstrating statistically significant superiority. These findings support the use of short-term forecasting as an operational planning tool for anticipating variations in ED demand and informing adaptive resource allocation.

This study examines the evolution, heterogeneity, and short-term prospects of export performance in seven Central and Eastern European (CEE) economies—Croatia, Czech Republic, Hungary, Poland, Romania, Bulgaria, and Slovakia—over the period 1995–2024. Using annual World Bank data, exports are modeled as a share of GDP to ensure cross-country comparability and to capture differences in trade dependence. The analysis combines descriptive and inferential statistics with Augmented Dickey–Fuller tests, non-parametric comparisons, Granger causality analysis, and country-specific ARIMA models to investigate export dynamics, the role of foreign direct investment (FDI), and future export trajectories. The results reveal a common long-term upward trend in export intensity across all countries, driven by European integration and structural transformation, but with pronounced cross-country differences in export dependence and volatility. Highly open economies such as Slovakia, Hungary, and the Czech Republic exhibit strong export performance alongside greater exposure to external shocks, while larger domestic markets such as Poland and Romania display lower export intensity and greater stabilization. Granger causality tests indicate that FDI contributes to export growth in several economies, often with multi-year lags, highlighting the importance of absorptive capacity and institutional quality in translating investment inflows into export competitiveness. ARIMA-based forecasts for 2025–2027 suggest continued export expansion and relative stabilization despite recent global disruptions. This study’s primary contribution lies in integrating comparative export analysis, causality testing, and short-term forecasting within a unified econometric framework, offering policy-relevant insights into export-led growth and economic convergence in post-transition European economies.