Classrooms are becoming increasingly diverse, with students spanning a range of linguistic backgrounds and abilities. AI offers innovative tools to help educators create more inclusive learning environments by breaking down language barriers and providing tailored support for children with special needs. This paper provides a comprehensive review of how AI technologies can facilitate inclusion in education. It is discussed AI-driven language assistance tools that enable real-time translation and communication in multilingual classrooms, and it is explored assistive technologies powered by AI that personalize learning for students with disabilities. The implications of these technologies for teachers are examined, including shifts in educator roles and workloads. General outcomes observed with AI integration—such as improved student engagement and performance—as well as challenges related to equitable access and the need for ethical implementation are highlighted. Finally, practical recommendations for educators, policymakers, and developers are offered to collaboratively harness AI in a responsible manner, ensuring that its benefits reach all learners.

This paper presents a comprehensive review of optimization practices in the corrugated board industry, which has recently experienced significant interest in using optimization methodologies driven by sustainable demands and increasing computational capabilities. The authors cover different review perspectives, including historical context, manufacturing applications, design optimization, and numerical optimization algorithms used. The main findings of this study indicate that the corrugated board industry has experienced a shift from trial-and-error and expert-driven approaches to data-centric strategies, particularly since the beginning of the 21st century. Interestingly, the industry has also adopted Multi-Disciplinary Optimization techniques from other fields, which demonstrates the importance of knowledge convergence across sectors. However, due to the complex nature of corrugated boards—including materials, design, and manufacturing processes—there is still much research to be done in this area. This work provides guidance for future research directions and encourages innovation and improvement in corrugated board optimization practices. In particular, the strong developments of material models for paper in recent years will boost the use of optimization tools in this field.

Neuro-Symbolic Artificial Intelligence (NeSyAI) is a paradigm that combines neural networks with symbolic reasoning, building upon foundations laid in the 1990s and early 2000s, yet gaining increased attention through practical applications. This paper provides a comprehensive review of NeSyAI techniques and their applications in advanced signal and image processing. The paper begins by introducing the fundamentals of NeSyAI and a taxonometry analysis, highlighting how it bridges the gap between data-driven learning and knowledge-based reasoning. It then surveys several key application domains, such as biomedical, autonomous robotics, and IoT, in order to understand the benefits and challenges of that integration. For each domain, how NeSyAI methods improve upon traditional purely neural or purely symbolic approaches is illustrated. A comparative analysis with conventional AI techniques is presented, underscoring the advantages of NeSyAI in terms of interpretability, generalization, and flexibility. The challenges that arise in developing NeSyAI systems, such as computational complexity, integration of heterogeneous models, and ethical considerations, are also discussed. Finally, future research trends in NeSyAI for signal and image processing, as well as the path toward more explainable and generalizable AI, are synthesized.

This paper proposes a novel approach to represent the geometry of the corrugated board profile during compression using graphs. Graphs are lighter than images, and the computational time of compression analysis is then significantly reduced compared to using the original image data for the same analysis. The main goal of using such graphs is to gain more knowledge about the mechanical behavior of corrugated boards under compression compared to the current load–deformation curve approach. A node tracking algorithm is applied to characterize the different phases occurring during the compression test in order to predict physical phenomena, including buckling and contact. The main results show that analyzing the nodes provides significant insights into the compression phases, which has not been achieved in the current state of the art. The authors believe that the objective of this research is crucial to better understanding the physics of corrugated boards under compression, and it can also be extended to other engineering structures.