University of Tartu

Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate the impact of adding one or more resources on the cycle time of a process. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and from empirical observations, or automatically discovered from historical execution data. Regardless of its provenance, a key question when using a BPS model is how to assess its quality. In particular, in a setting where we are able to produce multiple alternative BPS models of the same process, this question becomes: How to determine which model is better, to what extent, and in what respect? In this context, this article studies the question of how to measure the quality of a BPS model with respect to its ability to accurately replicate the observed behavior of a process. Rather than pursuing a one-size-fits-all approach, the article recognizes that a process covers multiple perspectives. Accordingly, the article outlines a framework that can be instantiated in different ways to yield quality measures that tackle different process perspectives. The article defines a number of concrete quality measures and evaluates these measures with respect to their ability to discern the impact of controlled perturbations on a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the proposed measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify the sources of discrepancies.

David Chapela-Campa; Ismail Benchekroun; Opher Baron; Marlon Dumas; Dmitry Krass; Arik Senderovich. A framework for measuring the quality of business process simulation models. Information Systems 2025, 127 .

David Chapela-Campa, Ismail Benchekroun, Opher Baron, Marlon Dumas, Dmitry Krass, Arik Senderovich. A framework for measuring the quality of business process simulation models. Information Systems. 2025; 127 ():.

David Chapela-Campa; Ismail Benchekroun; Opher Baron; Marlon Dumas; Dmitry Krass; Arik Senderovich. 2025. "A framework for measuring the quality of business process simulation models." Information Systems 127, no. : .

Ghulam Abbas Khan; Kaido Lätt; Magda Rebelo. A polynomial collocation method for a class of singular fractional differential equations. Applied Numerical Mathematics 2025, 207, 45 -57.

Ghulam Abbas Khan, Kaido Lätt, Magda Rebelo. A polynomial collocation method for a class of singular fractional differential equations. Applied Numerical Mathematics. 2025; 207 ():45-57.

Ghulam Abbas Khan; Kaido Lätt; Magda Rebelo. 2025. "A polynomial collocation method for a class of singular fractional differential equations." Applied Numerical Mathematics 207, no. : 45-57.

Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm² (2.56 mm² of epithelium and 24.87 mm² of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138− cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138− and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36–0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p < 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (p = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples. Conclusion: Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial CD138+ plasma cells, offering distinct advantages over manual inspection, such as rapid analysis of whole slide images, reduction of intra- and interobserver variations, sparing the valuable time of trained specialists, and consistent productivity. This supports the application of AI technology to help clinical decision-making, for example, in understanding endometrial cycle phase-related dynamics, as well as different reproductive disorders.

Seungbaek Lee; Riikka K. Arffman; Elina K. Komsi; Outi Lindgren; Janette A. Kemppainen; Hanna Metsola; Henna-Riikka Rossi; Anne Ahtikoski; Keiu Kask; Merli Saare; Andres Salumets; Terhi T. Piltonen. AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF). Journal of Pathology Informatics 2024, 15, 100380 .

Seungbaek Lee, Riikka K. Arffman, Elina K. Komsi, Outi Lindgren, Janette A. Kemppainen, Hanna Metsola, Henna-Riikka Rossi, Anne Ahtikoski, Keiu Kask, Merli Saare, Andres Salumets, Terhi T. Piltonen. AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF). Journal of Pathology Informatics. 2024; 15 ():100380.

Seungbaek Lee; Riikka K. Arffman; Elina K. Komsi; Outi Lindgren; Janette A. Kemppainen; Hanna Metsola; Henna-Riikka Rossi; Anne Ahtikoski; Keiu Kask; Merli Saare; Andres Salumets; Terhi T. Piltonen. 2024. "AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF)." Journal of Pathology Informatics 15, no. : 100380.