Université Constantine 2

Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information. • A requirement-driven meta-model method for standardized process-mining assessment. • An iterative approach for low-cost development of tailored in-house tools. • Improves decisions through focused data extraction and need-aligned analysis.

Tragedy, in visual arts, has existed since ancient times, as seen by Pharaonic paintings depicting grave ceremonies. However, sad expressiveness took on a new dimension in contemporary art, particularly during the First and Second World Wars. The major setbacks experienced by the world turned visual art into a means of expressing various clashes and crises, whether political, social, or even psychological. This is evident in artistic icons that reflected political conflict and its consequences, such as the painting "Guernica" by the Spanish artist Pablo Picasso, or inner conflict as seen in "The Scream" by Edvard Munch. Accordingly, this paper aims to introduce the concept of the visual art painting and its formal and implicit elements, thereby highlighting the tragic character in the artwork. Throughout its history, visual art has included many masterpieces whose contents vary some artworks reflect the prosperity of society, while others provide us with a representation of the political situation. The artistic painting, composed of a collection of lines and symbols, is a visual message that contains many concepts presented by the artist to the viewer. The visual artwork serves as the artist’s notebook, requiring us to analyze and interpret it. The tragic images presented by the artist are nothing but a simulation of the human wound, confronting us with a visual tragedy that speaks to the viewer in a visual language, in which the emotional state is intertwined with the artist’s philosophical vision. Thus, the artwork becomes a space for contemplation, allowing us to discern the artist's intention and purpose behind their artistic creation.

• Optimized concrete mix design using recycled coarse aggregates through DNN and IGWO. • Multi-objective optimization framework reveals trade-offs between density and ultrasonic pulse velocity. • Innovative use of machine learning for modeling water content, density, and UPV in recycled aggregate concrete. • Hybrid approach of Grey Wolf Optimizer and Deep Neural Network achieves superior predictive accuracy. • Validated models offer a sustainable framework for concrete mix design in eco-friendly construction. This study presents a thorough methodology for forecasting and enhancing the essential performance characteristics of concrete with Recycled Coarse Aggregates (RCA), including water content (Wt), density, and Ultrasonic Pulse Velocity (UPV). The attributes are represented as functions of cement content (300–400 kg/m³), RCA percentage (0–100 %), and slump (5–12 ± 1 cm). An experimental database was created using a three-factor, three-level design, followed by exploratory analysis using correlation matrices and three-dimensional response surface plots to identify significant input-output associations. Various modeling techniques, such as Response Surface Methodology (RSM), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and a Deep Neural Network (DNN) enhanced by the Improved Grey Wolf Optimizer (IGWO), were assessed. The accuracy of the model was meticulously evaluated by 4-fold cross-validation, Taylor diagrams, and radar plots, demonstrating that the DNN-IGWO hybrid surpassed all other models, achieving the minimal prediction errors and the maximal correlation coefficient (R 2 ) values for all target responses. The validated models were later employed as surrogates inside a multi-objective optimization framework employing the Multi-Objective Grey Wolf Optimizer (MOGWO). The optimization produced well-distributed Pareto fronts, emphasizing the intrinsic trade-off between density and UPV. The resultant "knee" solutions identify specific concrete compositions that optimize UPV (3.6–3.7) while ensuring a high density (≥2400 kg/m³) and moderate water content. This study provides a scientifically robust, data-driven paradigm for the sustainable design of concrete mixtures, facilitating the appropriate incorporation of recycled aggregates while enhancing material qualities.