Engineering education

During the last three decades, higher education institutions (HEIs) have integrated sustainability into their curricula. Recent research has focused on sustainability competences and pedagogical approaches, and how the latter foster the development of the former. Previous studies have examined the connections between pedagogical approaches and sustainability competences from an educators\' perspective. However, there has been limited research addressing a students\' perspective, particularly in relation to gender. This paper aims to provide insights into developing students\' sustainability competences through pedagogical approaches from a gender perspective. A survey was applied to students at 17 universities from 13 countries. The responses were analyzed using: (1) Descriptive statistics and Friedman tests; (2) t-tests comparing perceptions of male and female students; and (3) correlations between competences and pedagogical approaches comparing both perspectives. The results showed differences in how male and female students\' competences ranked, with female students having a more concentrated range of competences. The pedagogical approaches results showed little to no differences and concurred with the published literature. The results also indicated differences between genders regarding how the pedagogical approaches develop the competences, where female students tend to develop more sustainability competences than males. HEIs must recognise gender-based perceptual differences and develop strategies that build upon the strength of both perspectives and, thus, better implement sustainability competence education.

Keywords: education for sustainable development , gender , pedagogical approaches , students' perception , sustainability competences

Objective. Objective evaluation of operational training remains challenging, particularly when comparing physical training with virtual reality (VR) training. Behavioral and subjective measures capture performance and perceived workload but provide limited insight into the underlying neurocognitive adaptation. This study proposes and validates a synthetic EEG-derived cognitive training index (CTI) that integrates multiple training-sensitive neurometrics into a single composite metric, with two aims: to validate CTI as an objective marker of training progression in a physical training group, and to test whether VR-based training leads to neurocognitive adaptation comparable to physical training when participants subsequently execute the task in the real environment. Approach. Participants completed three repetitions of a realistic confined-space industrial maintenance procedure structured into Entrance, Rolling shutter, and Pipe replacement phases. Behavioral outcomes (execution time, procedural errors) and subjective workload NASA Task Load Index (NASA-TLX) were collected after each trial. Wearable EEG was recorded continuously; six neurometrics were computed from cleaned EEG using band-specific Global Field Power over predefined electrode sets. CTI was obtained via principal component analysis applied at the trial level to the multivariate neurometric time series, followed by segment-wise analysis. Temporal specificity of CTI was assessed through phase-randomized surrogate-data analyses. Main results. Execution time decreased across trials (F = 14.82, p <.001, η2p = 0.35), procedural errors decreased similarly (F = 10.31, p <.001, η2p = 0.27), and NASA-TLX declined (F = 11.94, p <.001, η2p = 0.30). CTI increased significantly with training (F = 18.21, p <.001, η2p = 0.39) and was selectively higher in learning-relevant phases (Entrance and Pipe replacement), while remaining near baseline in the low-demand Rolling shutter phase. Surrogate analyses showed that real CTI exceeded null expectations, supporting temporal specificity. Repeated-measures correlations indicated significant negative associations between CTI and procedural errors (R = − 0.42, p <.01) and between CTI and NASA-TLX (R = − 0.38, p <.01), while CTI was not significantly related to execution time (R = 0.18, p =.12). Significance. CTI provides a psychologically grounded, multivariate EEG marker that tracks training progression and aligns with operationally meaningful outcomes, namely procedural accuracy and perceived workload, rather than task speed alone. This approach supports objective comparison of cognitive adaptation across training modalities and provides a practical basis for neurophysiologically informed training evaluation and future adaptive training systems.

Keywords: EEG , principal component analysis , training

This paper addresses the crucial aspect of position error modeling and compensation in industrial servomechanisms with the aim to achieve accurate control and high-performance operation in industrial robots and automated production systems. The inherent complexity and nonlinear behavior of these modules, usually consisting of a servomotor and a speed reducer, often challenge traditional analytical modeling approaches. In response, the study extensively explores the design and implementation of Machine Learning (ML) algorithms to obtain a comprehensive model of the Transmission Error (TE) in rotating vector reducers, which is a main source of robot motion accuracy errors. The ML models are trained with experimental data obtained from a special purpose test rig, where the reducer is tested under different combinations of input speed, applied load and oil temperature. In the second part of the work, the resulting predictive model, tailored to capture the intricate dynamics of the analyzed reducer, is imported into a programmable logic controller to enable online compensation strategies during the execution of custom motion profiles. Experimental tests are conducted using two distinct motion profiles: one generated with a cycloidal law, typical of industrial machinery, and the other extrapolated from the joints of an industrial robot during a pick-and-place task. The results demonstrate the effectiveness of the proposed approach, enabling accurate prediction and substantial reductions (over 90%) in the overall reducer TE through the implemented predictive model.

Keywords: Compensation approach , Machine learning , Predictive modeling , Servomechanism , Test rig , Transmission error

Safety in the industrial sector is paramount, with national legislations establishing specific regulations to reduce accidents. These regulations emphasize risk removal and proper operator training as preventive measures. However, traditional training can be time-consuming and costly, leading companies to meet only the minimum requirements. In particular, confined spaces pose significant dangers, often resulting in fatal, cascading accidents due to a lack of proper safety procedures. Therefore, effective training is crucial to mitigate these risks. Advanced technologies like Immersive Virtual Reality (IVR) present new opportunities for cost-effective and extensive training campaigns by eliminating the risks associated with real environment training, offering a safe yet realistic experience. Despite the widespread adoption of IVR training applications in industrial research, there is a notable lack of systematic approaches to evaluate their effectiveness. Conducting such evaluations is crucial to ascertain their impact on essential training outcomes, including participant engagement, knowledge transfer, and enhanced safety behavior during work activities. To address this gap, we developed an IVR-based training platform for confined space safety procedures and established a systematic validation procedure using the Kirkpatrick model to assess its effectiveness compared to conventional training methods. Results demonstrate that IVR training provides an excellent user experience, better knowledge transfer than the traditional approach, and improved performance in simulated procedures, reducing execution errors and completion times. This dual focus on creating and validating the IVR system underscores its potential as an effective training tool in hazardous environments such as confined spaces, where proper training can prevent tragic fatalities.

Keywords: Confined space , Safety training , User study , Validation procedure , Virtual reality

Higher education institutions (HEIs) have been incorporating sustainability into their curricula, which is crucial for providing students with the skills and knowledge to help societies become more sustainable. During the last 15 years, there has been increasing research on sustainability competences and the pedagogical approaches to develop them. There have been proposals on lists of competences, pedagogical approaches, their groups and their links. However, most research has focused on the perspective of educators, with limited research from the students' perspective. This paper aims to provide the students' perspective of the groups of sustainability competences, groups of pedagogical approaches, and how these connect. A survey was developed for this purpose, which was applied to participants from 17 HEIs worldwide. The responses obtained were analysed in a five-step process: Descriptive statistics, Spearman correlations, Principal Component analysis (PCA) to detect the competence and pedagogical approaches groups, correlations between the groups and regressions between the groups. The PCA analysis resulted in three competences' groups and confirmed the three pedagogical approach groups. The analyses showed differences in the competences' groups between the students' and educators' perspectives. The regression analysis groups shows how the pedagogical approach groups develop the competences' groups from the students' perspective. Developing sustainability competences requires a change in the higher educational system, where the educators' and students' perspectives need to converge so that the sustainability competence–based education model is more efficient and effective in educating the future leaders, decision-makers, educators, management and professionals.

Keywords: higher education institutions , pedagogical approaches , students , sustainability competences , sustainable development

Clinical simulation is fundamental for the healthcare staff to learn and enhance their procedural skills without causing harm to the patients. Despite its importance, in literature appears a deficiency of pediatric pathological mannequins, especially those simulating difficult airway management due to the obstruction of the passage of tubes, fiberscopes, or catheters. Given the importance of simulating complex scenarios in the medical staff's training, the authors decided to realize a modular high-fidelity pathological mannequin with nasal access using reverse engineering and additive manufacturing techniques within T3Ddy, a joint laboratory between Meyer Children's Hospital of Florence and the Department of Industrial Engineering of the University of Florence. The mannequin is developed from diagnostic images of a significant 30-month-old polymalformative patient also affected by Pierre-Robin syndrome modifying the tracheobronchial tree to reproduce an abnormal status. Rigid parts and silicone cast molds are manufactured using 3D-printed ABS/ASA while platinum-cure-silicones are used for the soft ones. Meyer's anesthesiologists collaborated to the realization providing feedback during design and production. The device is evaluated with a 5-point Likert scale questionnaire and results in a useful tool for the training of procedural skills related to difficult intubation as its realism, anatomical geometry, and tactic feedback are positively evaluated.

Keywords: 3D printing , mannequin , pathological airway , simulation

The evolving landscape of educational technologies has ushered Virtual Reality (VR) in the forefront of higher education. As the COVID-19 pandemic propelled a rapid shift toward e-learning, the demand for high-quality distance education has surged, prompting an exploration of VR as a viable solution. While existing research indicates that VR supports student engagement and learning experiences compared with traditional teaching methods, the lack of shared pedagogical frameworks and systematic analyses of its applications leaves a deeper investigation of VR's potentials and limitations in enhancing learning outcomes still unexplored. This paper presents a systematic literature review aimed at filling this gap by considering studies that evaluate VR-based teaching methods in comparison with traditional ones in higher education contexts in order to assess the strengths and weaknesses of this technology in improving students' learning outcomes and achieve inclusive education. The analysis focuses on a set of dimensions including the adopted research design, participants' characteristics, disciplinary field of application, VR technological features (i.e., immersivity, interactivity, operability, commercial availability, and presence of VR training), adopted teaching methodologies, assessed VR impact on learning outcomes and presence of studies involving students with disabilities or Specific Learning Disorders (SpLDs). Based on inclusion/exclusion criteria, a total of 71 studies of VR in higher education were analysed. Most of analysed studies employed quantitative methods (67%), while no qualitative studies were found. More than half of the studies were conducted with undergraduate students (61%). Most of the studies involved VR in STEM disciplines, with almost half of them concerning Health Sciences (45%). VR solutions were most frequently immersive (63%), predominantly using Oculus Rift and HTC Vive HMDs, interactive (59%), single-user (92%) and non-commercial (57%). Only a small portion of studies included a VR training in the research protocol (8%). Most of the studies compared lecture-based methodologies as control condition with active methodologies in the VR condition. Learning outcomes were positively influenced by immersivity, interactivity and active methodologies, although at different degrees. No study involved students with disabilities or SpLDs in the experimentation. By offering a multidimensional perspective on the application of VR in higher education contexts, the paper provides a valuable resource for educators, researchers, and policymakers navigating the dynamic intersection of VR and higher education.

Keywords: Augmented and virtual reality , Human-computer interface , Media in education , Post-secondary education , Teaching/learning strategies

The concept of Operator 4.0 has been recently defined to evolve the modern industrial scenarios by defining a knowledge sharing process from/to operators and industrial systems, creating personalized skills, and introducing digital tools towards socially sustainable factories. In this context, dynamic and adaptive user interfaces can make humans part of the intelligent factory system, supporting human work contextually and providing specific contents when needed, preserving the human wellbeing. This paper defines a human-centric methodology for the symbiotic co-evolution of operators' skills, assistive digital tools and user interfaces, developed within the Horizon Europe project titled “DaCapo - Digital assets and tools for Circular value chains and manufacturing products”. The project focuses on defining a new set of human-centric digital tools and services for the manufacturing industry capable of boosting the application of circular economy (CE) throughout the manufacturing value chains. The proposed methodology can link the specific needs of an industrial case to the definition of the most proper assistive digital tools and functionalities to drive the design of adaptive, proactive user interfaces for the Operator 4.0. The method has been applied and validated on one of the project use cases, involving a manufacturing company operating in warehousing and logistics.

Keywords: Adaptive interfaces , Circular economy , Human-Machine Interfaces , Operator 4.0 , User experience design

The advent of GPT has caused a real revolution in many application contexts. Even the TRIZ community has had to face up to this new technology, questioning the possible integrations with traditional paths and tools. Many problem-solving experts have for some time been proposing specific prompts based on the methodology's tools such as functional analysis, reconstruction of cause-effect relationships, identification of Resources, 40 inventive principles, etc., in order to support the problem solver, or even replace him altogether, during the inventive process. The free generation of LLM content has been applied for very different purposes such as, for example, to contextualize general purpose heuristics in specific domains, or as a search engine to answer technical questions, to suggest creative ideas or improve the formulation and redefinition of a problem, or finally to find connections between different application contexts. This article proposes a critical analysis of the real effectiveness of these prompts according to the different needs of users. The analysis was carried out using a software application that was developed in-house and for which a testing phase was conducted on a variegated sample covering both the academic and industrial fields, with more experienced users and users who have been approaching TRIZ for less time.

Keywords: AI - Artificial Intelligence , CAI - Computer Aided Innovation , GPT , LLM - Large Language Model , Problem-Solving , TRIZ

Considering the importance of the recent renewed interest in geometric specification management in industry and academia, along with the evolving landscape of educational models post-COVID-19, the authors aimed to investigate, through a literature review, the status of educational practices on these topics. In particular, the presence of educational initiatives dedicated to geometric and functional product specifications in mechanical or industrial engineering programs was investigated, with a specific focus on standards such as ISO GPS and ASME GD&T outside the Italian context. An analysis was then conducted to identify common teaching strategies and the development of specific tools to improve the understanding of these complex topics. The review of education and training experiences revealed significant trends and insights for future training efforts. In particular, the prevalence of active learning practices emerged, and detailed course descriptions demonstrate the effectiveness of hands-on approaches, often supplemented by specific tools, in facilitating student understanding.

Keywords: Active Learning , Engineering Education , GD&T , ISO GPS , Technical Drawing

The paper presents an overview of current training endeavours for technology-related/professional skill acquisition in immersive environments together with educational/training needs that emerge from the literature. The aim is to establish a general framework for the design and development of educational initiatives for lifelong learners, both HEI students and professional. The paper details how immersive technologies enable a wide variety of skills (e.g. psychomotor, spatial, procedural, decisional skills) to be acquired and trained while reducing the costly and risky conditions of on the field training. On the other hand, the framework will also display how the immersive technologies might trigger high engagement and motivation in active/student-centred learning contexts, making them suitable to provide meaningful learning experiences for the largest possible variety of learning styles, which are vital to ensure life-long learning and embrace the principles of pedagogy and andragogy.

Keywords: Education Framework , Immersive Environment , Professional Skill Training

Digital transformation of industrial training processes is still an ongoing process. The transformation requires a specific training model to be applied to an already structured context, to experienced workers, sometimes with their needs and specific skills, and the solution is designed considering a series of boundary conditions. To design a new training model that the industry will accept, it is important to consider the design space offered by technology, but at the same time, to consider human complexity. This paper proposes a methodology to analyze current training strategies in the industrial sector to design new tools according to a digital transformation logic that considers the underlying principle of the human centricity of Industry 5.0.

Keywords: Digital transformation , Industrial training , Industry 5.0

The widespread availability of Information and Communications Technology paradigms encourages people, especially older adults, to maintain independence in their daily activities, improving health and well-being. However, some elements of criticality can arise when trying to meet two worlds apart. The present work aims to discuss the main factors that affect older people's engagement with a user-centred software platform to increase people's awareness of the availability of services for social care and entertainment purposes. A mixed-method approach was defined to correlate the attitude towards technology, measured through the TechPH index, to the usability and user-experience outcomes. Despite the limited sample size, the findings of this preliminary study suggest that the use of technology is not associated with any increase in the stress level, since the initial perceived difficulty to learn and use the system decreased after some training. Moreover, education level, living conditions, age, gender and TechPH index do not affect the usability of the SW interface, rather it depends on the familiarity with the use of the smartphone, the perceived usefulness and the difficulty in using it.

Keywords: Human-Computer Interaction , Older Adults , Technology Use , Usability , User Experience

With approximately 431,000 cases, kidney cancer ranks 14th in terms of diagnostic frequency and accounts for 2.2% of all new cancer cases worldwide.12. For malignant clear cell renal cell carcinoma (ccRCC), which necessitates WHO/ISUP grading based on tissue microscopic analysis, early diagnosis is critical for targeted treatment. With the use of a convolutional neural network with three blocks, this study presents a machine learning-based system to grade kidney tumors using CT scans. The system divides tumours into low-grade (grades 1 and 2) and high-grade (grades 3 and 4), which corresponds to the tumour's aggressiveness and helps determine the prognosis. The challenge kits21 database's 237 preoperative CT scans were used to train the model, which yielded an accuracy of 93.67% and an f-score of 91.80%. For reliable performance validation, the approach consists of data preprocessing, augmentation, and a binary classifier with a majority voting ensemble model. The objective of this non-invasive method is to improve diagnostic accuracy in clinical settings by decreasing inter-observer variability. The dataset will be enlarged in future work to enable wider application. Although there is hope for the system's clinical support, more validation is necessary before generalization can occur.

Keywords: AI , ccRCC , Deep learning , grading , image processing , kidney cancer

More than 90% of atrial thrombi in patients with non-valvular atrial fibrillation originate in the Left Atrial Appendage (LAA), a diverticulum of the left atrium of the human heart, whose morphology predisposes the stasis of blood within it. The Left Atrial Appendage Occlusion procedure shows promise for reducing the risk of stroke due to blood clots in the body circulation after forming in the LAA; however, it must be performed by experienced operators due to the numerous associated risks. The objective is to develop a simulator for training novice operators in the procedure to plan the optimal occlusion path on representative models. Starting from a dataset of 20 diagnostic images provided by the Azienda Ospedaliero Universitaria of Careggi, a three-dimensional reconstruction of the anatomical areas involved in the surgical procedure and their spatial identification are performed. These results are used to design a device for the adjustment of a 3D printed model of the LAA with correct orientation and spatial coordinates. The simulator is a highly customizable and patient-specific prototype system; the ease of use and small size help its acceptability and reduce the time required for practice in use.

Keywords: 3D Printing , LAA , Simulator , Surgical training

Technological development made vehicle safer thanks to the advanced driving assistance systems (ADAS). There exists passive systems, like ABS that boosts vehicle performance during an emergency braking, and active systems, like active cruise control that actively drives car during normal cruise. However, ADAS need to further evolve to continue the risk reduction and to increase automation level. Physiological sensing and deep learning are promising options at these regards. This paper will present a methodology to estimate the driver drowsiness from photoplethysmography (PPG) waveform, acquired by no-invasive probe embedded in the vehicle steering wheel. PPG signal is treated using the innovative hyper-filtering process, then it is elaborated by a convolutional deep architecture. A driver sample, made by 70 people distributed by gender and age, was selected to validate the approach, monitoring PPG and confirming the actual level of drowsiness by electroencephalography. The proposed network makes a classification task to distinguish a wakeful drive from a drowsy one. The developed method has an accuracy of 99%, which were higher than other networks considered for the benchmark.

Keywords: ADAS , automotive physio-sensing , Deep Learning

In the evolving landscape of driving training, the integration of technology has played a pivotal role in shaping educational methodologies and enhancing learning outcomes. Virtual training methods, which often rely on the use of monitors, have been the standard for decades. However, with the advent of augmented reality (AR), a new dimension of interactive and immersive training is emerging, promising to revolutionize the way driving skills are taught and acquired. This paper aims to explore and compare the effectiveness of virtual monitor-based training with the innovative use of augmented reality in driving simulations. The focus is on understanding how each method affects learning efficiency, user engagement, and the practical applicability of the skills acquired through these training modalities. Virtual monitor-based training systems are valued for their accessibility and cost-effectiveness, making them a prevalent choice in many training programs. On the other hand, augmented reality introduces a layer of interactive elements that overlay the real world or simulated environments, providing real-time feedback and enhancing situational awareness. This paper seeks to delineate the strengths and limitations of the AR-integrated driving training simulations. The goal is to provide insights into how virtual-based training can be optimized to enhance the safety, efficiency, and effectiveness of driving education, ultimately contributing to better-prepare drivers in their driving performances.

Keywords: Augmented Reality , Driving Simulator , User Experience

Structural topology optimization approaches are widely used to create lightweight and efficient components through additive manufacturing. The race to lightweight components should also account for natural frequencies when designing components and structures subjected to dynamic loads, as in aerospace and automotive engineering. These optimization tools require extensive manual setup, mainly when tuning input parameters that govern algorithmic functions and convergence. In this context, machine learning approaches can circumvent the trial-and-error process associated with the manual setup of simulation factors. This study presents a method that utilizes machine learning to suggest optimal input parameters, such as evolutionary rate and mesh dimensions, for creating lightweight and optimized structures while maintaining a consistent natural frequency. The framework incorporates a neural network trained on a collection of previously solved, analogous problems. A dissimilarity metric derived from problem metadata is used to determine tuning parameters. This approach can be applied to analyze and optimize product configurations where only marginal conditions may change; a Bayesian optimizer based on data coming from the neural network is used to improve the structure, substituting the topology optimization algorithm and reducing the time-to-market of a specific product.

Keywords: Artificial Neural Network , Bidirectional Evolutionary Structural Optimization (BESO) , Design for Additive Manufacturing , Frequency Optimization , Topology Optimization

Manufacturing industries today are undergoing a formidable process of transition to completely new and sometimes unfamiliar modes of operation. This process is also known as the 5th Industrial Revolution and involves the implementation of a set of technologies including, among others, digital technologies such as XR-Extended Reality, AI, Big Data and Digital Twins. In particular, Extended Reality, which encompasses the full spectrum of advanced 3D models and techniques for visualizing and interacting environments, is already a standard de facto method at some stages of the product life cycle. In the design phase of complex systems, for example, it supports designers, other business functions and end users in the collaborative analysis of complex assemblies. While these technologies are supported as enablers for the aforementioned transition in industries, their use at the academic level remains focused on the goal of creating more interactive educational experiences and enhancing understanding of complex concepts. In contrast, this paper analyzes the use of XR in mechanical engineering curricula with the goal of identifying the main challenges for creating advanced digital skills for engineers and empowering them to integrate such knowledge into industrial process optimization.

Keywords: Engineering Education , Extended Reality , Focus Group , Mechanical Engineering

This paper aims to provide an updated overview about the Human Pose Estimation (HPE), which is a typical Computer Vision-related problem. The review firstly analyzes the importance of the research topic, both in terms of number of publications and application fields. Thanks to the introduction of efficient machine learning techniques, nowadays HPE is feasible without the necessity of specialized and expensive instrumentations. The proposed review considers only the works based on the use of Machine Learning techniques, and it is divided in two parts: an analysis of methodologies presented for the 2D HPE and a similar one for the 3D HPE. Being this last generally approached as an extension of the 2D HPE, the Authors propose a section discussing the effective validity of the most diffused and open source 2D HPE methodologies.

Keywords: Computer Vision , Human Pose Estimation , Human Posture , Movement analysis