The SNARC effect indicates that numbers are mapped from left to right as in a mental number line. Accumulating evidence suggests that it could be attributed both to the magnitude or to the order of numbers, but the role of these two aspects has not yet been disambiguated since the two are tightly correlated. This study investigated the influence of order and magnitude in the SNARC effect using playing cards as stimuli. While most people organize cards in ascending order (AO), according to the Western reading-writing direction, a minority of people arrange them in descending order (DO). In this regard DO people should spontaneously associate low magnitude cards (e.g., 2) to the right, and high magnitude cards (e.g., 6) to the left. Therefore, in DO individuals, cards’ order would elicit a spatial mapping opposite to the canonical mapping of quantities (i.e., mental number line). In Experiment 1, DO participants performed magnitude classification on simple numerals and on playing cards, showing a regular SNARC effect when classifying numbers and no significant effect when classifying cards. Conversely, in Experiment 2, AO participants showed a regular SNARC effect when classifying both numbers and cards. In Experiment 3, a separate group of DO participants was recruited and tested online to replicate Experiment 1 and clarify the occurrence of spatial associations in card classification. Results indicated that DO participants showed regular SNARC effects both in number and card classification, suggesting that magnitude played a key role overruling the order of cards. This is apparently in contradiction with the view that specific experiences with ordered stimuli should determine the direction of an association.

In digital manufacturing education, integrating artificial intelligence with structured reflective learning may accelerate novice designers' creative development. The study introduces an AI-Enhanced Reflective Design Framework (AERDF), which integrates AI-driven feedback, scaffolded reflective journaling, and iterative digital fabrication tasks to support reflective learning in design education. To investigate this, fifty undergraduate students (18-24 years) participated in a two-week intensive pilot program featuring three iterative design cycles of 3D-printed chaotic attractors and 2D tessellation patterns. Each cycle combined automated AI-driven analysis of aesthetic and structural metrics with peer-led reflection sessions and prototype refinement. Artifact quality ratings increased from a mean of 3.2 to 3.9 (22 % improvement), design fluency rose by 30 % and originality by 25 %, while personalization scores correlated strongly with the proportion of peer suggestions incorporated (r = 0.62). Self-reported gains expanded from three core domains in Week 1 to eighteen distinct improvement categories in Week 2, and reflective challenges evolved from four broad barriers to eighteen nuanced metacognitive obstacles. Preliminary regression analyses suggest that initial peer-review prompts and journal entries in Week 1 yielded moderate gains in perceived effectiveness (slope ti 0.413, R2 ti 0.207), whereas the full two-week combination-adding AI feedback and advanced metacognitive prompts-produced stronger improvements in this pilot context (slope ti 0.612, R2 ti 0.923). These results demonstrate that embedding AI-augmented feedback within a scaffolded reflective framework yields statistically suggestive trends in creative performance and selfawareness, offering a scalable model for fostering innovation, adaptability, and critical thinking in design curricula.

This study examines the stylistic evolution of Pablo Picasso through a quantitative analysis of his artistic production. Using a corpus of 1170 digitised artworks sourced from WikiArt, we computed a set of image-based metrics grounded in aesthetic theory, including Shannon Entropy (EM), Fractal Dimension (FD), Birkhoff’s Aesthetic Measure (BAM), Euclidean Distance (ED) and Contextual Fit (CF). These metrics were used to characterise structural and compositional properties of the artworks and to examine their variation across Picasso’s major stylistic periods. By representing individual artworks as points in a multidimensional feature space defined by these metrics, the study reconstructs Picasso’s oeuvre as a trajectory through stylistic configurations over time. The analysis reveals consistent patterns in the distribution and temporal evolution of the metrics, highlighting phases of relative stability and periods of rapid stylistic transformation across the artist’s career. The computational framework is fully reproducible and scalable, enabling the application of the same analytical processes to other large visual corpora and artistic traditions. This trajectory-based approach provides a quantitative method for investigating artistic evolution and offers a complementary perspective to traditional art-historical interpretations of stylistic change.

Artificial Intelligence (AI) integration into the Network of Extended Reality-Enabled Laboratories (EXTENDABLE) is a game changer in the field of STEM (Science, Technology, Engineering, and Mathematics) education. This novel and innovative framework utilizes Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) to create immersive and adaptable environments that enable remote, hands-on experimentation. These laboratories enable inclusive and sustainable learning by overcoming key challenges such as socio-economic limitations, crowded classrooms and mobility restrictions due to the pandemic. AI boosts the effectiveness of these networks since it can improve real time interaction, adaptable learning pathways and effective laboratory management. Key applications include dynamic scheduling for laboratory resource allocation, intelligent tutoring systems, behavior observation utilizing digital twins, and AI driven 3D instrument reconstruction. This study explores current uses of AI in the eXtended Reality (XR) laboratory and its potential to improve STEM education through collaboration, initiative, and rapid feedback to students. The paper also discusses difficulties such as latency, data security and inclusiveness and presents AI driven solutions to these limitations. This overview would stimulate the research in the application of AI in enhancing XR laboratories to expand access to STEM education, improve learning outcomes, and promote lifelong interdisciplinary learning.

This paper presents the development of a remote laboratory system for distance learning applications, designed to replicate hands-on experiences in a digital environment. The system, based on the “miniverse” concept, allows students to interact with real instruments, such as the Tektronix TDS210 oscilloscope, through a client-server platform. It communicates using the MQTT protocol for reliability and scalability, and uses a GPIB interface supported by an ESP32 card for wireless connectivity. On the software side, the lab uses modern technologies such as Node.js and TypeScript to ensure stability and ease of use. This innovative approach bridges the gap between theory and practice in distance learning, providing an immersive and interactive experience. The system is a scalable solution for improving technical education, expanding its accessibility and quality, and opening new perspectives for science and engineering education.

Integrating practical training into Science Technology Engineering and Mathematics (STEM) education is critical for bridging the gap between theoretical concepts and real-world applications. Minimal accessibility, high operational expenses, and logistical constraints are some of the recurrent issues that traditional laboratory facilities face, especially in settings with minimal resources. This study evaluates the Network of Extended Reality-Enabled Laboratories (XR-enabled), an innovative system to create scalable, interactive, and immersive educational settings. A regulated survey comprising 200 participants, including academics, administrators, researchers, and students, assessed XR-ENABLED labs concerning many aspects of STEM education. Although technology accessibility and collaborative learning suggest areas for improvement, ANOVA analysis revealed significant variations in perspectives across positions, hence underscoring benefits such as enhanced engagement, conceptual understanding, and motivation. The findings highlight the need for more research on long-term benefits, cost-effectiveness, and improvements in accessibility, demonstrating the transformative potential of XR-ENABLED laboratories in enhancing the inclusivity, flexibility, and practicality of STEM education.

Thanks to the application of the Industry 4.0 paradigm, contemporary factories consist of flexible production lines that can generate countless product variations without substantial increases in production costs. This study highlighted a scientific and technological gap between the flexible manufacturing system and the design system adopted to make products. In fact, commonly used CAD design technologies are static and do not allow the generation of dynamic and variable designs, causing the need to redesign models in whole or in part in order to realize variations in the generated shapes. In this paper, an algorithm-based generative design methodology oriented to the flexible manufacturing paradigm is proposed. This design approach, based on parametric modeling in Grasshopper, allows countless geometric variations of a product to be automatically generated while returning input CAD files for CNC machines. Specifically, the proposed design approach was tested by making two applications for the wood furniture industry; the output obtained in the case studies consists of a generative and parametric algorithm. The generative system provides a file for advanced manufacturing systems; in the case study, a numerically controlled laser cutter, one of the most popular machines for making flat panels from wood and metal, was chosen. The results obtained showed how the algorithmic design approach is of great importance in order to ensure customized production without substantial cost increases. This is made possible through algorithmic design automation and contemporary manufacturing technologies.

This paper presents a comprehensive laboratory project aimed at reconstructing and digitizing nearly 400 historical psychotechnical instruments as digital twins. These digital replicas will be integrated into an IoT-based medical system, enabling remote data acquisition, analysis, and reporting. The project involves the 3D virtual reconstruction of the instruments, followed by their physical realization through 3D printing, sensor integration, and circuit design. The digital twins will be optimized for computational use, allowing for remote interaction with human subjects wearing sensors. The system will simulate psychological processes, analyze collected data, and generate reports based on the specific metrics of each instrument. Additionally, the laboratory will develop a data platform that aggregates existing experimental data and new data from the digital twins. This platform will be integrated into a broader multi-omics medical project, facilitating cross-disciplinary research and enhancing the understanding of human cognitive and physiological processes. Furthermore, the digital twins will be made accessible to a wide audience, including students and the general public, through an interactive 3D environment. This environment will feature technical sheets detailing the history, constructors, and experiments associated with each instrument, offering an educational resource for understanding the historical roots of psychological and medical technology. This innovative approach bridges historical psychotechnical tools with modern IoT technology, offering new possibilities for psychological assessment, research, and education.

This paper presents E-MOTE (Emotion-aware Teacher Education Framework), a conceptual framework designed to enhance teacher education through the integration of the Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR). Grounded in neuroscientific and educational research, the proposed framework aims to strengthen teachers' emotional awareness, teacher noticing, and social and emotional learning (SEL) competencies. E- MOTE outlines a design-based approach to addressing persistent gaps in current teacher preparation programs, which often lack tools for practicing real-time recognition of subtle emotional cues. As a theoretical and design-oriented proposal, it provides a structured foundation for developing emotionally responsive teaching and inclusive classroom management, while outlining essential ethical safeguards and scalable validation strategies for diverse educational contexts.

Transmission lines, devices employed for the transmission of electrical signals, can be used for the approximation of non-linear partial differential equations (PDEs) also in the nonlinear case. To this end, transmission lines are used to spatially discretize PDEs allowing the problem to be solved numerically even for intricate boundary conditions - e.g. at intersection of many wires in the system. Using transmission lines to solve the Korteweg-de Vries Equation (KdV) allows for efficient and accurate numerical solutions, as it provides a method to investigate the propagation of wave-like information in nonlinear and dispersive media with multiple dimensions. In the present paper a software developed in C# is presented, the latter employs discretization by transmission lines and the Runge-Kutta 4–5 integration algorithm to numerically solve the KdV equation in the one-dimensional case. The implemented program, named “WireExplorer," is able to simulate the propagation of solitonic pulses on different types of circuits composed of one or more wires, even in the case of intersections. Such conditions are not canonically solvable by resolution of the KdV equations, while approximate numerical resolution allowed the evaluation of these intricate cases. In particular, the obtained results showed the subdivision of the wave into smaller components, at intersections between wires, which propagate in different directions showing also the formation of dispersive tails propagating in the direction opposite to that of the main wave.

The integration of extended reality (XR) technologies in remote practical training offers immersive learning experiences through virtual simulations. In this paper, the first step to offer a new virtual living environment implementing a measurement laboratory is proposed. Differently from a pure simulative environment, the proposal goes further allowing the design of a real didactic experience by using real measurement instruments. The proposed XR-enabled measurement laboratory provides a blend of virtual and real-world environments, fostering practical skills and critical thinking abilities that can be acquired only with “first hand” experiences. Despite technical obstacles, and open didactical questions, XR presents opportunities for innovation and collaborative learning experiences. Enhancing interactivity through multiplayer and social engagement features, the proposed laboratory will foster a sense of community among learners and researchers.

Our work explores the blend of science, art, and technology in chaos physicalization, translating complex mathematical models into tangible designs. It is closely aligned with research on data physicalization, drawing from design creativity and innovation models. Through interdisciplinary collaboration and innovative tools, we bridge theory and practice, fostering a culture of creativity. Industry 4.0 technologies and education enhance practicality and inspiration, blurring boundaries between art, science, and technology. Our methodology follows a comprehensive six-step process: Selection of the Chaotic Model, Computational Manipulation of forms to be physicalized, Material Choice, Manufacturing, Post-Processing, and Quality Control. This systematic approach has successfully resulted in an exhibition, showcasing tangible objects that serve as representations of various chaotic systems. Demonstrated through this art museum exhibition, we prove that chaos generates artistic and scientific novelty, transforming data into a source of creativity and design innovation. Furthermore, our project emphasizes the significance of cross-disciplinary synergy, demonstrating how the physicalization of chaos can act as a catalyst for new educational and professional perspectives, opening avenues for future collaborations among artists, scientists, and technologists.

Providing a product with characteristics of beauty and creative innovation is one of the aims of any contemporary industry to meet the needs of customers and satisfy their aesthetic and functional requirements. To study the production process and the realisation of creative products with aesthetically pleasing attributes, we created an environment that allows the construction of simple textured polygons (triangles, squares, rhombuses and pentagons) through a software application. To overcome traditional ways of assessing creative products, a Birkhoff aesthetic measure, Euclidean distance and entropy measure have been developed and applied to a sample of smart products, manufactured in the implemented environment, to analyse their aesthetic, creative and order-complexity-chaos features, respectively. The results provided us with measurable quantities of the required qualities for the sample and for each distinct product, combining the metrics to visualise the creative products in 2D and 3D spaces. Further mathematical methods, such as the point process and density probability map applied on the same sample, allowed us to move from a statistical space to probabilistic landscapes, with enhanced forecasting capability on creative features for all future products. Industrial applications are expected in the production and evaluation of products from a creative point of view.

The Spatial-Numerical Association of Response Codes (SNARC) effect consists in faster left-/right-key responses to small/large numbers. (Bächtold et al., Neuropsychologia 36:731–735, 1998) reported the reversal of this effect after eliciting the context of a clockface—where small numbers are represented on the right and large numbers on the left. The present study investigates how the salience of a particular spatial-numerical context, which reflects the level of activation of the context in working memory, can alter Spatial Numerical Associations (SNAs). Four experiments presented the clockface as context and gradually increased its salience using different tasks. In the first two experiments (low salience), the context was presented at the beginning of the experiment and its retrieval was not required to perform the tasks (i.e., random number generation in Experiment 1, magnitude classification and parity judgement in Experiment 2). Results revealed regular left-to-right SNAs, unaffected by the context. In Experiment 3 (medium salience), participants performed magnitude classification and parity judgement (primary task), and a Go/No-go (secondary task) which required the retrieval of the context. Neither the SNARC effect nor a reversed-SNARC emerged, suggesting that performance was affected by the context. Finally, in Experiment 4 (high salience), the primary task required participants to classify numbers based on their position on the clockface. Results revealed a reversed SNARC, as in (Bächtold et al., Neuropsychologia 36:731–735, 1998). In conclusion, SNARC is disrupted when the context is retrieved in a secondary task, but its reversal is observed only when the context is relevant for the primary task.

Neurodevelopmental Disorders (NDDs) represent a significant healthcare and economic burden for families and society. Technology, including AI and digital technologies, offers potential solutions for the assessment, monitoring, and treatment of NDDs. However, further research is needed to determine the effectiveness, feasibility, and acceptability of these technologies in NDDs, and to address the challenges associated with their implementation. In this work, we present the application of social robotics using a Pepper robot connected to the OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After describing the general architecture of the system, we present two possible simulated interaction scenarios of a subject with Autism Spectrum Disorder in two different situations. Limitations and future implementations are also provided to provide an overview of the potential developments of interconnected systems that could greatly contribute to technological advancements for Neurodevelopmental Disorders (NDD).

An intellectual journey that began with the discovery of strange attractors derived from Chua's circuit, their translation into physical shapes by means of 3D printers, and finally, to the production of jewelry is presented. After giving the mathematical characteristics of Chua's circuit, we explain the chaotic design process, used for creating jewels, providing specifications of the used methodological approach, for its reproduction. We discuss the feasibility of this approach and the transmission of scientific contents on chaos theory, usually restricted to university students, in a high school Science, Technology, Engineering, Art, and Mathematics course, for the realization of advanced educational processes, implemented both in computational and real environments. We think that the idea of transforming science into art forms can drive students in acquiring scientific knowledge and skills, allowing them to discover the inner beauty of chaos.

Mathematics offers a virtually infinite range of incomparably beautiful shapes and patterns that can be used in the production of design objects. In particular, the generation of a large number of patterns can be achieved by varying input parameters of reaction-diffusion models, which simulate the distribution of chemicals. These patterns can be used in the customization of objects whose prototypes are made through additive manufacturing (3D printers). This technique allows very complex shapes and unique pieces to be physicalized in a short period of time. This study highlighted the possibility of creating, through a visual script, a potentially infinite catalog of objects based on mathematical patterns, identified using a Grey Scott reaction-diffusion model, those pattern are mapped on a customizable target surface.

An educational robotics lab has been planned for undergraduate students in an Electronic Engineering degree, using the Project Based Learning (PBL) approach and the NAO robot. Students worked in a research context, with the aim of making the functions of the NAO robot as social and autonomous as possible, adopting in the design process the Wolfram Language (WL), from the Mathematica software. Interfacing the programming environment of the NAO with Mathematica, they solved in part the problem of autonomy of the NAO, thus realizing enhanced functions of autonomous movement, recognition of human faces and speech for improving the system social interaction. An external repository was created to streamline processes and stow data that the robot can easily access. Self-assessment processes demonstrated that the course provided students with useful skills to cope with real life problems. Cognitive aspects of programming by WL have also been collected in the students’ feedback.

New consumer needs have led industries to the possibility of creating virtual platforms where users can customize products by creating infinite combinations of different results. This made it possible to expand sales by guaranteeing a wide choice that would satisfy all requests. The dynamic and flexible evolution of factories is guaranteed by the introduction of new technologies such as robotization and 3D printers, recognized as two of the pillars of Industry 4.0. The main aim of this paper is to achieve a workflow for the creation and implementation of personalised jewellery based on faces with different emotional expressions. To date, there are few works in the literature investigating the intersection between smart manufacturing and emotion recognition, and these are mainly related to improving human–machine interaction. The authors’ aim is to research for innovation in the intersection of three different fields of study such as parametric modelling, smart manufacturing and emotion recognition in order to create personalized and innovative manufacturable models. To this purpose, an application has been generated that exploits both visual scripting, typical of parametric modelling, and scripting, in the Python programming language. The generated algorithm implements a machine learning for emotion recognition that identifies the label of each user-generated face, validating the effectiveness of the method.