This workshop translates an AI- and XR-enhanced Double-Diamond learning framework into a hands-on experience for educators, instructional designers, and program leaders. Building on a five-week studio project with first-year students from architecture, design, digital art, communication, and music production, the workshop compresses the core phases of the intervention into an intensive, collaborative session.

Participants will experience a mini journey through Discover–Define–Develop–Deliver using a concrete design challenge: creating a small “sensory habitat” rooted in local cultural narratives or sustainability themes. In the Discover phase, participants analyse a short case, sketch initial spatial ideas, and surface opportunities for AI and XR support. In Define, they work in small groups to craft “prompt stacks” that connect design intent with generative AI tools for image or 3D concept generation while explicitly discussing authorship and ethical considerations. During Develop, they explore how extended realities (e.g., AR/VR visualisations, immersive walkthroughs via example media) can be used to validate and refine proposals. In Deliver, groups distil their process into a short narrative and map potential learning outcomes and assessment strategies for their own courses.

The workshop will foreground human creativity, critical reflection, and responsible use of AI, rather than tool demonstration alone. Participants will leave with: (1) an adaptable Double-Diamond activity template for AI/XR literacy, (2) example prompts and reflection questions used with students, and (3) concrete ideas to integrate similar exploratory projects into their own curricula with realistic time and resource constraints.

This workshop introduces a challenge-based learning (CBL) approach for integrating research, design, and development in engineering education through extended reality (XR). Developed at the University of Twente’s BMS Lab, the model addresses the increasing need for multidisciplinary collaboration by teaching students to speak a shared language across research, design, and technical development.

Participants will actively use a one-page project canvas to translate research ideas into structured user stories, requirements, and project management tasks. The session demonstrates how XR tools can serve as a human-centered medium for prototyping, testing, and validating ideas without requiring coding skills.

By working through a live exercise, participants will experience the dilemmas of aligning user stories, requirements, and project management under resource constraints. This practical, interactive format ensures relevance to STE’s focus on human-centered engineering education and highlights how XR can be embedded as a innovation in curricula.

Keeping the body balance is one of the most important objectives of a person in relation to the environment. For this objective are working together the locomotion system, the nervous system and the sensing organs. Because of its importance there are a lot of published papers on the body balance subject.

Most of the published papers are using statistical instruments on some test results. These mentioned results are obtained applying gait equilibrium tests, using test platforms, on groups of human subjects. We preferred to make a model of balance function, using affordable hardware and software instruments and verify it on theoretical cases to be applied in future practice for real cases.

First, we taken the general feedforward mathematical model of three excitable cells, available in other published papers and then applied it to the interconnecting architecture of neurons inside cerebellum. We simulated the mathematical model obtained for the cerebellum, for some theoretical parameters, in MATLAB, resulting in a set of curves for a set of optimized parameters. We realized a simulation schematic in LTSpice with user defined blocks for neurons and synapses, already published in recent papers, and parametrized them using the optimized parameters obtained after the simulations in MATLAB. We simulated disturbances using the simulation schematic in LTSpice and verified that the results were according with the reality, adjusting the parameters.

Our paper gives us the opportunity to confirm some of the known facts about the nervous system but also to meet into discussion some characteristics with which we were not aware of. Improving the simulation elements and adding some calculus power only the imagination could be the limit of the number of simulated situations.

Field Effect Transistors with Graphene (G-FETs) seem to be a serious candidate, as alternative to the traditional MOSFET transistors, benefiting from small dimensions, sometimes devices created on a single atomic layer, but accompanied by higher currents than in silicon, as the literature reported.

Therefore, the G-FETs simulation represents a strong tool to develop this device area. The most reputable device simulation software is Silvaco (Athena, Atlas). How-ever, in the libraries of this software does not exist graphene, as a semiconductor material. However, graphene is a 2-dimensional sheet of carbon atoms, arranged in hexagonal shape, a configuration similar to the diamond. Therefore, the first G-FET transistor simulated in this work will use diamond with all its intrinsic properties.

This work presents the development of a simulation tool, designed to study the impact of physical activity and lifestyle on the health of young adults. The implemented solution produces synthetic physiological signals, such as ECG, EDA, and skin temperature, based on personalised profiles provided by the user. The system integrates a specific regression model for each bi-osignal, geographical route simulation, and interactive visualisations that en-able contextualised analysis of physiological and emotional patterns. The tool was designed to support teaching, prototyping, and research activities in biomedicine. The results were represented in time graphs and interactive maps, allowing observation of the evolution of the signals along the simulat-ed paths. The validity of the signals produced was confirmed through model performance tests, with metrics such as the coefficient of determination (R2) and the root mean square error (RMSE). The results obtained reinforce the physiological plausibility of the simulated signals and the applicability of the system in real digital health contexts.

Obtaining diagnostic material from small pulmonary lesions remains a persistent barrier to early-stage lung cancer diagnosis. Contemporary diagnostic pathways increasingly rely on bronchoscopy techniques and computed tomography (CT)-guided transthoracic needle sampling; however, limited cellularity, obscuring blood, variable smear thickness and other artifacts can compromise both rapid on-site evaluation (ROSE) and downstream classification. This manuscript describes a rapid, ethanol-based liquid preparation that is intended to standardize slide cellular distribution, reduce obscuring background, and generate two complementary cytology slides from minimal sample volume. Immediately after sample acquisition, one to three drops of aspirate or collected material are introduced into a 10 mL ethanol-based preservation vial. Using an F50 filtration device, half of the preserved suspension is processed onto a first slide; the slide is removed approximately 20 seconds after initiation and immediately stained with Diff-Quik or a rapid Papanicolaou (PAP) variant to support ROSE. The remaining suspension is processed in the same manner onto a second slide, which is placed directly into ethanol for subsequent PAP staining and further evaluation. Representative images demonstrate preservation of diagnostic cytomorphology across benign, reactive, and malignant lung cytology categories, including lymphocyte-rich inflammatory backgrounds, macrophage-predominant specimens, atypical epithelial populations, squamous cell carcinoma, and metastatic carcinoma. The workflow is designed to reduce preparation-to-preparation variability and to provide consistent, interpretable slides in settings where tissue acquisition is challenging and time is constrained. Future prospective studies should quantify adequacy, diagnostic concordance, and suitability for ancillary testing within standardized lung cytopathology reporting frameworks.

Background: Ultrasound-guided fine-needle aspiration (FNA) is the cornerstone for triaging thyroid nodules and diagnosing thyroid malignancy. Rapid on-site evaluation (ROSE) can reduce non-diagnostic sampling and repeat procedures while it is resource-intensive, with increased labor demands at a time when many health systems face constraints in pathology staffing and distribution.

Objective: To describe and preliminarily evaluate a label-free computational microscopy workflow for thyroid FNA ROSE that couples standardized cytology smear preparation with quantitative phase imaging (QPI), enabling immediate digital review without chemical stains.

Methods: Unstained cytology smears were imaged with a proprietary computational microscope that reconstructs quantitative phase maps and renders diagnostically interpretable cytology-like images. Matched slides were stained with Diff-Quik for qualitative comparison.

Results: Label-free QPI images preserved key cytomorphologic information (cellularity, architectural patterns, nuclear-to-cytoplasmic relationships and background characteristics) relevant to adequacy assessment and preliminary categorization.

Conclusions: A stain-free QPI workflow on conventional cytology smear preparations for thyroid FNA may support rapid adequacy assessment and tele-enabled ROSE while preserving material for ancillary studies. Further prospective validation with larger cohorts and quantitative performance metrics are warranted.

The article presents a comparative study of two practical implementations of elec-tronic circuits for transferring photovoltaic energy from photovoltaic panels into electrical energy. The initial energy source is sunlight which is converted by pho-tovoltaic panels into electrical energy. First, the efficiency of solar panels repre-sents the amount of sunlight that a panel can convert into electricity and is de-pendent on the type of photovoltaic panels, namely on silicon or polysilicon sub-strate, on the manufacturing technology and is typical for general purpose solar panels in the range from 20% to 25%. The efficiency of the conversion system is defined by the amount of energy harvested from the solar panels relative to the amount of electrical energy delivered to the consumer (load), which can be im-proved by techniques such as the Maximum Power Point Tracking (MPPT) algo-rithm.

The power management unit is an essential assembly in precision applications where reference voltages with increased accuracy are required. The design of these circuits is a challenge, especially when it comes to the automotive field, where supply voltages can vary significantly (between 3.3 and 40 V).

The power management unit consists of a voltage reference, a voltage stabilizer, a bias circuit, a monitoring block and a pre-stabilizer with buffer.

The article presents the design, optimization and implementation of the bandgap voltage reference circuit that must provide a stable and accurate reference voltage, independent of process variations with temperature and supply voltage.

To reduce process variations, bipolar transistors were used because they have low variations with the manufacturing process. The base-emitter voltage of these transistors is approximately 0.6V at room temperature (27°C) independent of transistor dimensions or doping. In contrast, the threshold voltage of a MOS transistor is influenced by the thickness of the oxide layer, its quality, as well as the concentration of impurities present in both the transistor channel and the pol-ysilicon gate. For the implementation of the circuit at the schematic and layout level, the Virtuoso Schematic Editor and Virtuoso Layout Suite programs provid-ed by Cadence were used.

In a context characterized by declining enrollment in technical degree programs and a shortage of interest in engineering vocations, the Guitar STREAM initiative (School of Engineering of Bilbao, University of the Basque Country, UPV/EHU) emerges as an innovative educational response. This project integrates the STEAM disciplines (Science, Technology, Engineering, Arts, and Mathematics) with a strong emphasis on sustainability, engaging students in engineering through the construction of electric guitars and the design of related infrastructures. In doing so, students experience a hands-on, motivating learning process that fosters collaborative work. The initiative not only aims at the acquisition of technical competences but also at the development of professionals who are aware of global challenges and the social role of engineering. Moreover, it has been consolidated as a vocational guidance model that connects both university and pre-university students with the appeal of engineering applied to real-world and creative contexts

The rapid evolution of drone technology has created new opportunities for education, particularly in fostering interdisciplinary, hands-on learning within STEM and STEAM fields. This paper presents DRONAISSANCE: Skies to Classrooms (DS2C), an Erasmus+ Cooperation Partnership in School Education that aims to empower teachers and students to integrate drones as pedagogical and experiential tools in the classroom. The project responds to European priorities on promoting STEM excellence, advancing digital readiness, and supporting teachers’ professional development. It addresses the lack of pedagogical frameworks and training on drone integration by developing two main outputs: the European Drone Educator’s Toolkit and the European Drone Education Platform. These resources equip educators with the technical, legal, ethical, and methodological knowledge required to implement drone-based learning activities across disciplines. The project also seeks to engage underrepresented groups, particularly girls, in STEM through inclusive practices and collaborative learning environments. Through transnational training, local implementation, and the organization of Drone Awareness Weeks and Drone Festivals, DS2C promotes innovation, equity, and sustainability in European school education. The paper discusses the project’s design, methodology, expected impact, and contribution to the European Drone Strategy 2.0 and the European Green Deal by fostering a digitally skilled and environmentally conscious generation.

The Engineering graduates often enter the workforce unprepared for the cross-disciplinary, agile, and AI-augmented demands of Industry nowadays. This study investigates the misalignment between academic engineering curricula and industry expectations through a qualitative analysis of three expert interviews and content mapping across ten course syllabi. Results reveal key gaps in business reasoning, interdisciplinary teamwork, and hands-on system integration. Based on these findings, we recommend targeted pedagogical interventions, including embedding AI-enabled tools, simulating industry-relevant design processes, and requiring multidisciplinary collaboration in early academic years. These suggestions support the ongoing development of Education 4.0 frameworks that prepare engineers for digital-era work environments.

The incorporation of virtual reality (VR) technologies into the biomedical field marks a turning point in the way medical devices are conceived, developed, and tested. A VR tool designed for the creation and validation of personalized biomedical devices brings together immersive environments, advanced 3D modeling, and additive manufacturing. This innovative approach allows engineers and healthcare professionals to explore design alternatives, refine details, and predict potential challenges before physical prototypes are produced. The result is a more efficient, cost-effective, and patient-centered process that aligns with current demands for precision medicine.

One of the most notable contributions of VR is its capacity to simulate real anatomical conditions. Designers can virtually place a biomedical device within the human body and analyze how it interacts with different tissues, movements, and physiological parameters. This ability to visualize interactions reduces risks, minimizes errors, and improves overall device performance. Traditional prototyping often requires numerous iterations and significant resources, but VR enables testing at a fraction of the cost and time. By integrating VR with 3D printing, validated virtual models can then be physically produced, bridging the gap between digital design and tangible solutions.

In addition to its impact on design workflows, VR provides powerful benefits for education and professional training. Surgeons and healthcare practitioners can use immersive environments to rehearse procedures, test innovative devices, and become familiar with complex interventions before working with actual patients. This enhances confidence, reduces risks, and leads to better outcomes. Medical students can also gain a deeper understanding of device functionality by interacting with prototypes in a virtual environment, fostering experiential learning and skill development.

However, the implementation of VR in biomedical contexts is not without challenges. The regulatory environment governing medical devices is strict, and while essential for ensuring safety, it can slow the adoption of new technologies. Healthcare professionals may also be cautious about modifying established practices, especially when learning curves are steep. Technical requirements such as high-performance hardware, accurate calibration, and reliable software add further complexity. These barriers highlight the importance of offering comprehensive training and technical support to ensure successful integration of VR systems in clinical practice.

Despite these obstacles, the potential applications of VR in healthcare are expanding rapidly. Beyond design and validation, VR can support surgical training, rehabilitation programs, and patient engagement. For instance, patients can visualize their treatment plans or devices in a virtual space, improving understanding and trust. Rehabilitation can also be tailored to individual needs, with VR environments providing motivating exercises that accelerate recovery. The accessibility and affordability of VR tools are improving, paving the way for wider adoption across medical institutions.

Ultimately, the future of VR in biomedical device design depends on interdisciplinary collaboration. Engineers, clinicians, and researchers must work together to align technological capabilities with clinical needs. Such collaboration ensures that devices are not only technically feasible but also practical, safe, and beneficial in real contexts. By fostering cooperation and innovation, VR-based tools for biomedical design and validation hold the potential to transform healthcare, making it more efficient, personalized, and responsive to patient needs.

The rapid growth of the e-sports industry has transformed it from a niche entertainment activity into a global economic and cultural phenomenon, generating new professional opportunities for young people. However, access to these opportunities remains uneven, with socio-economic and educational disparities limiting participation and employability. The Erasmus+ project E4PROSPERITY – Professional Roadmap and Opportunities for Sustainable Participation in E-sports for the Youth addresses these challenges by creating inclusive, structured, and sustainable pathways for young people to engage in e-sports as a viable career field.

The project brings together universities, local authorities, and e-sports associations from Türkiye, Italy, Greece, and Montenegro to co-develop an innovative E-sports Training Curriculum and Screening Test, complemented by digital learning tools and multilingual e-books. The project’s outcomes include enhanced youth employability, new educational standards for e-sports training, and the creation of a sustainable European network linking education, industry, and public institutions. By integrating sport, digital innovation, and social inclusion, E4PROSPERITY contributes to the EU’s objectives of fostering youth empowerment, reducing inequalities, and supporting innovation and quality in youth work across Europe.

The accelerating green and digital transitions are transforming labour markets at a global scale, creating new opportunities while intensifying existing skills shortages. According to the Future of Jobs Report 2025, employers identify skill gaps as the primary barrier to business transformation, particularly in areas such as artificial intelligence, data analytics, cybersecurity, and energy-transition technologies. Although these competencies are increasingly essential for emerging job roles, many education and training systems face challenges updating curricula, accessing suitable technologies, and aligning programmes with rapidly evolving industry needs. As a result, the demand for workforce-ready talent continues to outpace the supply of graduates equipped with practical and future-oriented skills.

This round table focuses on practical models for effective education–industry collaboration that enable scalable, implementable, and sustainable approaches to green and digital skills development. Moving beyond theoretical frameworks, the session brings together educators, industry representatives, policymakers, and training experts (hybrid format) to share real-world experiences of designing and delivering impactful workforce development initiatives. These include co-created curricula, dual and apprenticeship systems, modular and micro-credential pathways, technology-enabled learning environments (remote, virtual, or smart labs, university makerspace…), employer-led academies, and rapid upskilling programmes targeting both youth and mid-career workers.

The discussion will explore how institutions and companies can jointly forecast labour-market needs, embed industry tools and smart technologies in teaching, and design assessment models that reflect real work environments. Attention will also be given to the challenges of implementation, such as funding, capacity-building, institutional coordination, and ensuring inclusiveness for diverse learner populations.

By the end of the session, participants will contribute to a shared set of actionable recommendations for strengthening the education–industry interface and preparing learners for emerging green and digital occupations. The round table aims to support the development of a resilient, future-ready workforce capable of driving innovation and sustainability in smart industry ecosystems.

This study examines how applicant profiles, prior experiences, and motiva-tion relate to admission decisions and early study persistence in informatics programs at Tallinn University of Technology (TalTech), Estonia (2020–2025). Drawing on Tinto’s integration model, Person–Environment Fit theo-ry, and Social Cognitive Career Theory, it analyzes how technical prepared-ness, institutional engagement, and self-efficacy shape enrollment and drop-out. The study combines qualitative content analysis of 168 applicant portfo-lios (2023–2025) with institutional enrollment and dropout data from 2020–2025, coding portfolios across five domains, including IT preparedness, en-gagement with TalTech, and extracurricular activities. Results show that prior engagement with the university, systematic IT experience, and bal-anced profiles combining technical and interpersonal competencies are the strongest predictors of both acceptance and persistence. The introduction of portfolio-based admissions was associated with reduced dropout by improv-ing alignment between applicants’ capabilities, motivations, and program expectations. At the same time, limited technical preparation and weak self-regulation increased the risk of withdrawal. Overall, the findings indicate that content-focused admissions can serve as an anti-dropout mechanism, with implications for equitable, data-informed admissions design in compu-ting education.

In the fast-evolving world of technology, is hard for education's system to keep the pace. The curricula for a 3-year study program is established at the enrolment of the students. After 3 years ,the students present their bachelor's thesis usually as an application with the topic related to what they studied and the expertise of the supervisor.

In order to observe the student’s approach to the topic of the bachelor thesis, we let the student to choose their own topic.

Nearly a quarter of the students from the promotion of 2025 volunteer to do their bachelor thesis in this way. The supervising was done in a laissez-faire manner. In the first semester, the communication with all 60 students was done via email exclusively. In the second semester, the supervisors organized one online meeting, two times a week.

Regarding the topics chosen by the students, we saw that there were topics that were not studied at the university, like applications using AI large language models or quantum computing technologies.

OpenAI launched ChatGPT in November 2022. In October 2022, the students began the university and finished in 2025. The curriculum of the study program did not contain any large language model topics. In this study we discovered that nearly 20% of the student implemented their bachelor thesis application that use a large language model (either hosted or using API).

The average grade for these students under our supervision was on the same level as the rest of the students.

By their choices, we see that students are more likely to tackle topics that go with the technology trends than remaining only by the supervisor’s proposals. Also, the students are willing to learn beyond university’s curricula in order to fulfill the market’s needs.

There are many myths surrounding women in IT, as well as the reasons for the lower representation of women compared to men. Several theories exist as to why women do not pursue studies in IT. For example, a common belief is that women are not interested in IT, that they are too afraid to study it, that people perceive it as a field for men, and that women are not considered intelligent enough to study it. Even though companies are expecting more women in IT, schools are reluctant to pursue female student candidates, as they fear that diversity will lower student quality and that an increase in women will result in lower timely graduation rates. The goal of this paper is to address some of these issues through the studies conducted among IT students at Tallinn University of Technology. Despite the growing emphasis on workplace diversity, higher education has been slower to respond to these biases. We aim to determine whether it is possible to identify gender-based differences in study motivation, entry pathways, and perceptions of study quality, and to translate these findings into institutional recommendations that can enhance women’s participation and success in IT education.

This paper is based on two institutional studies conducted at the Institute of Information Technology, Tallinn University of Technology (TalTech): a bachelor’s student survey carried out in autumn 2024 and a master’s student survey conducted in spring 2025. The surveys were designed to explore diversity and gender-related experiences among IT students, as well as to understand differences in study motivation, learning pathways, and perceptions of inclusion.

The surveys reveal clear gender- and study-level differences in students’ experiences and motivations in IT programs at Tallinn University of Technology. While both male and female students share a strong intrinsic motivation—namely, an interest in technology and problem-solving—their pathways into IT and perceptions of study quality differ notably.

The outcomes of the studies provide the basis for policy recommendations for TalTech as well as other IT institutions in Estonia and possibly elsewhere. The recommendations also take into account the broader Estonian educational context and existing research. The paper also seeks to align its insights with national and European gender equality policies in STEM education.

With software engineering courses taught in higher education gaining considerable ground in the past decade, teaching related ethical aspects has become an essential part of educating future software engineers. This short paper will be based on the experience of teaching a computer ethics course to a multicultural group of graduate computer science students. The paper will attempt to answer the following question: to what extent does culture influence ethical views of students in relation to different software development aspects?

Observations and conclusions will be drawn based on class reflection activities, questionnaires and empirical observations gathered during classes. The lessons learned during teaching this course can contribute to clarifying the relationship between cultural background and approaches to particular ethical aspects in software engineering. They can provide valuable guidance for future computer ethics classes, contributing to the essential education that future software engineers need.

The complexity inherent in the End Degree Projects (EDP) within engineering programs frequently results in substantial challenges for students regarding project preparation and execution. This also contributes to a heavy workload for the faculty involved in tutoring these works. The presented educational innovation project seeks to address these issues based on developing audiovisual resources intended to support students in EDP preparation and reduce the tutoring burden on teaching staff. This approach aims toward more efficient and sustainable learning outcomes.The primary motivation behind the study described here was to conduct a thorough needs assessment from the perspective of the teaching staff. The goal was to precisely identify the aspects of the EDP process that pose the most recurrent difficulties for students. The information gathered serves the critical purpose of determining what specific types of support or additional resources would be most beneficial, enabling the team to adequately identify and prioritize learning needs to focus the content of the audiovisual resources to be developed. The comprehensive needs assessment derived from the faculty survey establishes the necessary empirical foundation for developing targeted and effective educational support. The identified priorities will directly guide the content, format, and pedagogical design of the upcoming audiovisual resources. By aligning resource development with the empirically verified needs of the teaching staff, the project maximizes the potential for these videos to efficiently support student learning in the EDP process and successfully reduce the faculty’s tutoring load, thereby enhancing the overall quality and sustainability of the educational practice.

This paper proposes a methodology for implementing Artificial Intelligence (AI) in a dairy barn system at CAETEC (Experimental Agricultural Field from Tecnologico de Monterrey) to optimize dairy production, enhance milk nutritional value, and promote environmental sustainability. The project focuses on integrating data science, predictive models, and AI to select the best dairy cows. The core challenge lies in synthesizing vast amounts of data—including cow genetics, feeding behavior, rumination, milking results from a robotic system, and animal welfare—to inform decision-making. The methodology outlines four steps for implementing AI, including sensor identification, system interconnection, continuous training, and data analysis.

Various monitoring systems and data sources are utilized to feed the databases for the AI algorithms, including the Sense Hub system for rumination and health, the DeLaval VMS CLASSIC robotic milking system, the DeLaval activity meter system for behavior and heat detection, and the GREENFEED system for measuring methane emissions. The crucial, non-digital knowledge of the expert (veterinarian) is also digitized and integrated.

The selection of breeding sires, a crucial step in genetic improvement, is presented as a complex decision-making problem that is ripe for AI intervention. The analysis highlights the combinatorial complexity in selecting a stallion, even when considering only four specific genetic types (A2A2, High HHP$, Mastitis resistant PRO, and Polled Genetics) with multiple variables and sire options. The total number of possible ways to select sires, considering badges and characteristics, exceeds 172,000, which necessitates the use of AI to identify the optimal matches.

The paper proposes that a 'cow-match' tool can be developed using data science and algorithms to identify the optimal cow-sire match. This tool is designed to support decision-making in an academic context, offering a practical application for students. The significant number of variables and possibilities demonstrates the immediate need for an AI-powered prompt and decision theory to assist students from different backgrounds, like agronomy (with prior knowledge) and industrial engineering (without previous knowledge), in selecting the best sires based on multiple, often conflicting, objectives (e.g., milk production, cost, reproductive issues, and pollution). The goal is to minimize errors and measure the probability of success in the selection process.

This paper presents a learner-centered pedagogical framework for teaching the Information System Factory (ISF) concept using a flipped classroom and project-based learning approach. Originating in the 1980s, the ISF vision anticipated industrialized, component-oriented information system development, but could not be fully realized due to technological limitations at the time. Advances in cloud infrastructure, AI-assisted development, and low-cost computing now make this vision practically accessible within educational contexts.

The proposed framework integrates flipped classroom pedagogy with hands-on system development activities across the full System Development Life Cycle (SDLC), with particular emphasis on the construction and integration phase. Conceptual content is introduced through pre-class materials, while synchronous sessions focus on coached, collaborative system assembly using open-source tools and single-board computers such as Raspberry Pi. AI-assisted coding sup- ports component selection and exploratory learning, while small-group coaching enables individualized feedback and reflection.

The framework employs Mediathread as a collaborative platform for multimedia annotation and reflection, enabling students to connect theoretical concepts with practical implementation. The ISF concept has been applied in multidisciplinary contexts including Business Administration, Cultural Heritage, and the In-ternet of Things, demonstrating flexibility and adaptability across domains.

Rather than presenting a controlled evaluation study, the paper contributes a design-oriented instructional model informed by teaching practice and formative observations. By integrating flipped classroom principles, project-based learning, and an ISF-inspired development workflow, the framework offers a transferable approach for educators seeking to bridge conceptual understanding and authentic information system development in contemporary higher education.

The digitalization of vocational education requires scientifically grounded approaches to assess learning outcomes resulting from the integration of artificial intelligence, simulation tools, and Learning Management Systems (LMS). In response to the rapid evolution of digital technologies and the need for efficient resource utilization, this study proposes a mathematical model for evaluating the effectiveness of digital tools in training future professionals. The model is based on a system of weighted performance indicators: knowledge quality (Kq), learning time (Kt), student engagement (Ka), and resource expenditure (C). These indicators are synthesized into a composite efficacy index (E), enabling comparative analysis between digital and traditional instructional approaches. The research methodology involved analyzing data from LMS platforms such as Moodle and Google Classroom, as well as AI-based interactive environments. Student engagement was measured through analytical surveys, while instructional scenarios and cognitive load were modeled to assess learning dynamics. Generative AI tools were used to analyze student responses, identify semantic patterns, and refine indicator structures. Data normalization, statistical filtering, and expert-based weighting ensured the model’s validity. The model integrates quantitative metrics (e.g., scores, time, cost) with qualitative indicators (e.g., engagement, cognitive interaction), offering a comprehensive framework for evaluating educational outcomes. Validation was conducted through a comparative study involving two cohorts: one using digital technologies and another following traditional methods. Results demonstrated improved knowledge acquisition, a 22% increase in student activity, reduced learning time, and lower resource consumption in the digital cohort. The composite efficacy index (E) showed a 19% increase compared to the traditional model. This model provides a robust foundation for strategic decision-making in the digitalization of vocational education. Its application supports enhanced training quality, efficient resource allocation, and the development of adaptive, data-driven educational systems.

Digital Twin (DT) technology has been used as a transformative approach for data monitoring and industrial process optimization, especially in older machinery contexts where retrofitting sensor networks is more difficult. This systematic review analyzed 803 publications from 2015 to 2025. The main focus was on sensor placement methodologies for DT applications in industrial machinery. The comprehensive database searches were done using Scopus and Google Scholar databases. We identified and categorized sensor placement approaches into six primary methodologies, including optimization-based methods, machine learning approaches, model-based techniques, information theory methods, rule-based/heuristic approaches, and hybrid methods. Our analysis shows a significant trend in the direction of data-driven and AI-enhanced approaches. For old machinery applications, we found that hybrid approaches combining physics-based models with machine learning show superior performance in scenarios with limited historical data. This work provides practitioners with a comprehensive taxonomy of sensor placement strategies. It highlights a trade-offs between implementation complexity, data requirements, and performance for old industrial systems.

In modern network environments, the exponential growth in data traffic and architectural complexity has made the centralized management of Software-Defined Networks (SDNs) increasingly challenging. Traditional SDN controllers, while programmable, still depend on static rules and human intervention for policy definition and threat mitigation, limiting their responsiveness in dynamic and large-scale deployments. This paper introduces a Large Language Model (LLM)-based agentic framework designed to enhance the cognitive and autonomous capabilities of SDN control planes. By integrating advanced reasoning and contextual understanding from LLMs, the proposed system enables intelligent policy generation, adaptive network reconfiguration, and explainable decision-making in real time.The framework leverages Mininet for virtual SDN simulation, Suricata as a telemetry and event-generation layer, and a multi-agent LLM pipeline for interpreting network events, classifying contextual significance, and dynamically generating flow rules. These rules are executed through an OpenDaylight SDN controller, allowing the network to respond automatically to anomalies, performance degradations, or security events without manual intervention. This closed-loop automation demonstrates how LLM-driven reasoning can transform SDN architectures from reactive, rule-based systems into proactive, self-optimizing infrastructures. The proposed approach supports continuous adaptation, scalability across cloud and IoT environments, and transparency through human-readable reports, marking a step toward fully autonomous and explainable network orchestration.

Collaborative robots promise agility and resilience in manufacturing, yet many deployments fail to deliver tangible relief for frontline employees. Addressing this gap, we present a transparent, LLM-supported procedure for “shift handover to the robot,” which systematically delegates repetitive, routine tasks to an unmanned robot shift while preserving human oversight and value creation during staffed hours. The framework operationalises ambidextrous automation: it protects the exploitation of proven processes while intentionally freeing human time and attention for exploration activities such as improvement, onboarding, observation, and learning. The central thesis is that transparent, data-grounded delegation can convert the potential of collaborative robotics into realised employee relief and stable productivity. This paper summarises context, approach, outcomes, and brief preliminary validation results of the procedure, including “exploration surplus” and practical SME adoption guidance.

At the core is an interactive, checklist-guided decision process that combines fourteen practical criteria with context data from production systems. Evidence includes cycle times, takt adherence, setup and changeover characteristics, error history, scheduling buffers, and environmental, health, and safety constraints. A retrieval-augmented LLM interprets this evidence and proposes candidate tasks for delegation, supporting both cooperative day-shift work and an autonomous, unmanned robot shift. Each recommendation carries a transparent decision trail – prompt-response pairs, data inputs, and scored criteria – that forms standardised handover documentation and enables auditability.

The procedure includes a lightweight task library, exception-handling rules (human fallback, safe stops, and alerts), and scheduling integration so that the robot shift is treated as a governed planning resource rather than ad-hoc automation. We anchor the design in ambidexterity theory by positioning exploration and exploitation as mutually reinforcing over time. Delegating monotony to the robot creates an “exploration surplus”: time and cognitive bandwidth that humans reinvest in improvement and innovation, which, in turn, yields more exploitable routines for subsequent robot shifts. This virtuous cycle is illustrated conceptually through an idealised productivity curve and can be tracked empirically using operational metrics.

We outline measurable targets for repeatable adoption in small and medium-sized enterprises: quantitative indicators include OEE deltas, interruption counts, idle time in unmanned shifts, error rates, and rework; qualitative indicators include perceived workload, trust, and collaboration quality. Expected outcomes are fewer operator interruptions, more stable after-hours execution, and higher acceptance due to clear, auditable decisions. Because the criteria and data interfaces are explicit, the approach is pragmatic to implement and portable across heterogeneous equipment bases. Limitations include data availability and quality, the need for robust safeguards in unmanned operation, and disciplined management of prompts and knowledge bases; we propose mitigations such as minimum data thresholds, watchdog mechanisms, and continuous review of decision logs.

Taken together, the procedure reframes robot handover from a narrow technical integration to a socio-technical management practice. It clarifies which tasks are suitable, why they are suitable, and how their delegation will be evaluated, making automation choices legible to engineers, planners, and operators alike. By turning the robot shift into an accountable planning instrument, the framework helps factories convert potential into realised benefits with traceability, safety, and human-centred relief at scale.

Throughout the entire life cycle of stationary gas turbine units (GTUs), such as industrial gas turbine power stations and gas pumping units, numerous types of tests are carried out. The results of these tests must be documented in reporting materials, including current test records with unit operating parameters over the course of testing, final consolidated protocols for each stage, summary data tables for operational documentation (e.g., passports, technical formulars, etc.), and other related records. The content and format of such documentation are defined by the manufacturer’s requirements and applicable standards, essentially representing standardized forms with a clear structure.In the work authors presenting the easy prompt-base solution for the automated test documentation generation. Implementation of the LLM for the test report generation is allowing to reduce the time for the developing working documentation and reduce the human error.

The automatic generation of Programmable Logic Controllers (PLCs) code serves as a tool for enhancing the intelligence and efficiency of manufacturing systems. PLCs can control a wide range of operations and processes, making them indis-pensable in today's industrial settings. One of the most significant innovations brought in this field of Artificial Intelligence (AI) is automated code generation.

The rapid advancement of AI has significantly impacted software develop-ment, particularly in automated code generation. AI-powered tools as machine learning and deep learning models aim to generate, optimize, and refine code, re-ducing development time and enhancing software quality. AI is becoming an in-telligent assistant, capable of accelerating software development phases. We first explore a comprehensive IEC 61131-3 international standard for multiple PLC programming languages as Structured Text (ST) and Ladder Diagram (LD). ST language is a high-level, text-based programming language used in PLC pro-gramming systems. Due to its structure and syntax, high-level programming lan-guages - like Delphi programming language - offer special readability and main-tainability, making it the preferred choice for developing complex control logic.

One main contribution of this paper is to design a structure for ST high-level programming language with its Delphi-like syntax. This is reflected in the case study where an application is presented that was first virtualized. And the pro-gram for the programmable logic controller that controls the operation of the mo-tors of a conveyor was created, in ST language, by using an AI generation tool that uses the natural programming language method.

This work aims to investigate the available platforms for digital archiving, including open-source solutions, and the implementation of such a solution at Transilvania University of Brasov (UniTBv). The initiative is aligned both with national plans for the development of the digital infrastructure for open science - in particular through the university's participation in the National Open Science Cloud Initiative (RO-NOSCI) - and with European guidelines for promoting transparency in research in South East Europe, reflected in the NI4OS-Europe project. At the national level, there is a lack of a coherent policy on the promotion and adoption of open access practices to research results, which contributes to their marginal presence in the public space. Facilitating open access to scientific information could have a significant impact on the academic community and civil society, stimulating inter-institutional cooperation and enhancing the dissemination of knowledge to citizens.

The metal-oxide-semiconductor field-effect-transistor (MOSFET) is the most used transistor nowadays in both digital and analog applications. With the need for low-power circuits and some analog computation functions, the subthreshold (low current) regime of the MOSFET is also used. Therefore, in modelling one must account for two current domains, over threshold voltage and subthreshold, with two different drain current versus gate voltage formulas. Also, the point of transition from one formula to the other must be determined, therefore a longer calculation time is needed. It is useful to determine a unified model and corresponding formula for MOSFET drain current, so the calculation does not depend on the above or subthreshold value of the gate voltage. This is determined in this work together with the extraction of the MOSFET parameters in a practical case.

Digital systems are becoming increasingly prevalent making a qualified cybersecurity workforce and a foundational "cybersecurity culture" essential in all professional fields. Traditional higher education finds it challenging to adapt curricula rapidly to the practical demands of the cybersecurity field, especially for non-specialist students and not only. This program addresses the gap by providing foundational, hands-on cybersecurity education to students from diverse fields of study, building upon existing knowledge about the necessity for cross-disciplinary cybersecurity competence.

The main goal of the current work is to build the “bridge” between theoretical knowledge and practical skills required by the current cybersecurity practices based on a hybrid approach. The initiative aims to train mixed-discipline students in vital cybersecurity activities, developing a broad cybersecurity culture and preparing them for roles focused on delivering security solutions. The project also seeks to prove the effectiveness of an interactive, practical training platform for this mixed student body.

The anticipated outcome is the successful development of content and training of students in practical cybersecurity solution delivery, in alignment with Equity, Diversity and Inclusion (EDI) principles. It is expected for participants to achieve a measurable increase understanding, knowledge and application of concepts like SIEM and Ethical Hacking, stepping from theoretical background to practical deployment and auditing of security solutions. Additionally, the project anticipates the design of a scalable, validated model for cross-disciplinary cybersecurity education that can be replicated in other institutions and fields of study, fostering a campus-wide cybersecurity culture. By the end of the training program, students are invited to offer cybersecurity services to local community organizations (LCOs).

In the last decades Machine Learning models have become increasingly used in different areas of interest, leading to the increase of computational and energy resources. In this context, sustainability has become a global imperative, and Green AI is currently used to encapsulate the concept of development of high-performing and eco-friendly models.

We propose in this paper a function for the selection of the best prediction model from a model library, based on a criterion which combines accuracy, latency, and carbon footprint.

Recommendation systems and sentiment analysis are two of the most common tasks that require high resource consumption. The purpose of this study is to create a system that is based on these two applications with a user-friendly desktop interface. Unlike a traditional system that involves running a single model for each functionality, this one will have to choose the optimal model based on the real-time values of carbon emissions intensities from Romania region obtained from an Electricity Maps API and other criteria based on user input, also taking into consideration the type of device on which the model is running. This approach represents a step in the emerging direction of Green AI paradigm, which also represents the motivation of this study.

The proposed system is composed of two main components: (1) image-based recommendation system and sentiment analysis. The recommendation system uses the Fashion Product Images (Small) dataset from Kaggle platform. The candidate pre-trained CNN (Convolutional Neural Networks) models are: ResNet50, InceptionV3 and MobileNetV2. The score function for the model selection takes into consideration the following aspects: the accuracy and latency of K-Nearest Neighbors algorithm for image recommendation of the entire dataset, the documented latency of each CNN model, the resolution of uploaded image by user, the emission factor score and the device where the model runs. The image resolution criterion offers a “bonus” if the image is close to the model target or a “penalty” otherwise. The emission factor score value adjusts the model score based on the emission factor value from the API. Higher values disadvantage complex models, while simpler models gain preference. This logic applies to the sentiment analysis component, too, where the utilized models are BERT (Bidirectional Encoder Representations from Transformers) and VADER (Valence Aware Dictionary and sEntiment Reasoner), here a particular criterion being the text length.

Few results of this study are as following: In the recommendation system, it was observed that for large dimension images, if the emission factor from the API exceeds the value of 360 gCO2/kWh, the selected model is MobileNetV2 in support of sustainability and for the sentiment analysis component, if the length of text exceeds 50 characters and the emissions value are over 300 gCO2/kWh, VADER is the selected model.

According to the official documentation of the utilized models, the results align with expectations, reflecting each model’s complexity and dimension. These key characteristics along with CO2 emission data, guide the selection of the appropriate model based on the platform used to run the app and user input.

Teleoperated object handling in hazardous environments poses a risk to operators. The development of drones and robotic systems opens horizons for novel civic applications such as technical inspections, maintenance at high altitudes, special deliveries or interventions in hard-to-reach areas. Previous studies have shown that using a live 3D interface along with safe communication methods, and a modular design can make interventions not only safer, but also more efficient and secure. However, easily accessible and practical prototypes are still needed to demonstrate that these solutions can be implemented at low cost.

This work aims to develop and design a remotely controlled robotic arm. It can be installed on aerial work platforms, including drones. It is a scalable and modular solution that is mitigating the operator risks. Its applicability covers the military and the civilian fields, including operations related to maintenance, inspections, light object delivery, etc. So, our goal is to design, develop and control a modular and simple hardware-software system consisting only of Arduino, servomotors, serial communication/API and a 3D interface which provide accurate control, real-time feedback and operational reliability across such diverse missions.

This paper presents a technology-enhanced, project-based educational framework for teaching intelligent control concepts in engineering curricula. The framework integrates theoretical instruction with computational model-ing and real-time experimentation using a low-cost DC motor platform, ena-bling students to follow the complete control workflow from modeling and simulation to hardware implementation and performance evaluation under realistic non-ideal conditions.

The approach is supported by interactive modeling workflows, MATLAB/Simulink tools, and Live Scripts, and is applied to control prob-lems including proportional-integral-derivative, model predictive, and model reference adaptive control. Representative laboratory case studies illustrate how students implement and experimentally validate their designs on DC motor-based systems and related platforms.

Experimental outcomes and student feedback indicate that the proposed framework improves student engagement and supports the development of practical control skills and conceptual understanding. Overall, the framework provides an accessible and affordable model for intelligent control education, bridging theory and practice through hands-on learning.

Autonomous vehicles, whether mobile (Autonomous Mobile Robots, AMRs) or aerial (Unmanned Aerial Vehicles, UAVs) are extensively studied for various objectives such as inspection, search and rescue, transportation or any other tasks related to human support. Considering that UAVs [1], as well as AMRs become essential devices used for example for delivering packages or mails, and potentially in military applications, it is interesting to evaluate adversarial interactions between them. First, it becomes relevant to evaluate if a drone or a mobile vehicle transporting a package makes the delivery to the correct recipient, and not to another one belonging to a competing delivery company. Second, it is essential to investigate the adversarial interactions between autonomous vehicles applications. In this context, it is mandatory to understand adversarial interactions following game strategies like pursuer-evader dynamics or teams-based games inspired by example like soccer.

The rapid evolution of Artificial Intelligence (AI) has transformed the operation of drone swarms from human-controlled formations into autonomous, decentralized systems capable of collective intelligence. Inspired by natural phenomena such as bird flocks or fish schools, drone swarms act as multi-agent systems where each unit follows simple local rules, producing complex global behavior without centralized control. This paradigm shift unlocks scalability, resilience, and efficiency previously unattainable by traditional single-drone systems. The goal of this research is to analyse how AI methodologies enable intelligent coordination, autonomous decision-making, and cooperative mission execution in drone swarms, demonstrating measurable advantages in efficiency, adaptability, and reliability across military, civil, and humanitarian domains and to prepare for development of architectural solution of a control system for swarms of drones.

Auto-graded unit tests are commonplace in programming education, yet their use as pre-learning diagnostics remains underexplored. This study evaluates a short, optional, pre-course Python diagnostic built around unit tests and immediate pass/fail signals. Although the surface task (imple-menting add(a: int, b: str) -> str) appears simple, the suite demands robust handling of messy numeric inputs (locale formats, fractions, scientific nota-tion, bases, complex numbers, currency symbols, verbal numerals). The in-vestigation addressed three questions: (i) how instant pass/fail affects mo-tivation, confidence, and self-esteem; (ii) how students judge usefulness, fairness, and transparency; and (iii) which design changes they prefer.

Engagement logs from students (N=18) and post-course interviews (N=15) were analysed. One third completed the full suite, while many non-completers still reached advanced cases, indicating exploration beyond su-perficial tinkering. Interviews showed that the test felt authentic and moti-vating when it resembled real development work; however, repeated opaque failures reduced confidence. Participants reported that usefulness and fair-ness would be enhanced by an immediate linkage of tests outcomes to con-crete study steps and by the provision of a minimal, autonomy-supportive hint after repeated failure. Changes in intrinsic motivation followed a con-sistent pattern: curiosity at first ‘fail’, a short confidence boost at first ‘pass’, and a dip during repeated failure that could be recovered with a brief informational cue.

Two complementary typologies are proposed to interpret these responses: a three-profile scheme aligned with self-determination theory and a simple two-axis matrix defined by competence-signal sensitivity and transparency requirement. Practical implications include stating optionality and scope up front, maintaining purely binary early feedback, introducing a single mi-cro-hint after repeated failure, mapping outcomes to syllabus items, and ac-knowledging partial structure with lightweight code-aware indicators.

Simulation-based learning plays a crucial role in robotics education by allowing students to practice complex tasks safely and cost-effectively. Digital twins—virtual replicas of physical robots—foster a deeper understanding of kinematics, control, and automation, yet many open-source simulators lack the reliability and precision necessary for consistent educational use. Commercial platforms like Festo CIROS offer advanced multi-body modeling and inverse-kinematics capa-bilities, but no digital twin exists for the Niryo Ned2 collaborative robot. This study addresses this gap by developing a fully functional digital twin of the Niryo Ned2 in CIROS, including peripheral devices such as a conveyor, object feeder, ultrasonic sensor, and sorting bins. A MELFA Basic program implements color-based pick-and-place operations using inverse-kinematics motion and by integrat-ing digital inputs and outputs. The resulting simulation provides a realistic, in-teractive environment that mirrors physical robot behavior, enabling hands-on experience in programming, sensor integration, and automation workflows with-out physical hardware. The work demonstrates the potential of CIROS as a robust educational platform for digital twins and lays the foundation for future en-hancements, including vision-based and AI-driven automation training.

Neuroanatomy education is challenging due to the spatial complexity of brain structures and the difficulty of three-dimensional visualization in traditional teaching methods. Immersive learning technologies, such as 3D Virtual Learning Objects (VLOs), have demonstrated potential for improving knowledge comprehension in advanced educational settings, aligning with the principles of Smart Education and Digital Twins. This research seeks to evaluate the impact of 3D VLOs as digital twins of neuroanatomical structures on improving the spatial understanding of neuroanatomy students, promoting immersive and active learning strategies.An interactive 3D VLO modeling key brain structures was implemented, integrated into a virtual learning platform. The methodology used was ADDIE for the development of the VLOs and the educational process. An experimental design was applied. Spatial understanding and retention were measured pre- and post-intervention, complemented by usability and learning perception surveys using the TAM model. The hypothesis was tested using the Wilcoxon test, and the alternative hypothesis that VLOs improve spatial understanding of neuroanatomy was accepted. The results identified that students who used 3D OVAs significantly improved their spatial understanding and retention of neuroanatomical content compared to traditional methods. They also reported greater motivation, satisfaction, and interactivity with learning. 3D OVAs as digital twins constitute an effective tool for teaching neuroanatomy and, in Education 4.0, enhancing spatial understanding. This strategy aligns with the objectives of Smart Education and the integration of advanced learning technologies, offering a replicable model for education in health sciences and other disciplines that require complex spatial visualization.

Palpation, a cornerstone of medical examination, relies on the practitioner's tactile acuity and anatomical expertise. This paper introduces an innovative augmented reality (AR) approach to enhance foot palpation training and practice. Traditionally, developing proficiency in palpation techniques requires extensive hands-on experience, which can be time-consuming and inconsistent. To address these challenges, we have developed an AR application for foot palpation training that provides real-time guidance by overlaying palpation zones on a scanned image of the patient's foot.

Our study compared the accuracy of locating the medial cuneiform bone using traditional methods versus our AR app. A paired-samples t-test (n=30) revealed a statistically significant improvement in accuracy when using the AR app (M = 4.55 cm, SD = 0.53) compared to traditional methods (M = 9.28 cm, SD = 3.24, p < 0.001). The mean improvement of 4.73 cm (95% CI: 3.52 to 5.94) underscores the potential of AR technology to enhance anatomical education and improve clinical skills.

The AR application integrates with a wearable device, such as the Head Mounted Tablet (HMT-1), enabling interactive learning experiences for medical students and residents5. This technology not only facilitates skill acquisition but also offers potential applications in remote patient care, such as training visiting nurses to triage homebound patients at risk of diabetic foot complications.

Our findings suggest that AR-assisted palpation training could significantly enhance the learning experience, potentially leading to improved diagnostic accuracy and procedural outcomes in clinical settings. This approach represents a promising advancement in medical education, bridging the gap between traditional training methods and the evolving landscape of healthcare technology.