Iouliia Skliarova

The digital transformation of engineering education has created new opportunities to support laboratory learning through continuous formative assessment. However, digital technologies are frequently implemented as isolated tools rather than as components of a coherent pedagogical framework. This paper presents LAB@SMART, an integrated digital framework that supports the complete FPGA-based laboratory learning cycle by combining pre-laboratory preparation, in-class formative assessment, laboratory practice, post-laboratory consolidation, and continuous feedback within a unified workflow. The framework integrates complementary digital technologies with conventional laboratory teaching without requiring modifications to the curriculum or laboratory infrastructure. A pilot deployment was conducted during the second semester of the 2025/2026 academic year in a Digital Systems Laboratory course involving two laboratory groups (35 students). The evaluation combined academic performance, informal implementation observations, and an anonymous student questionnaire. Among students who attended the examinations, the pilot groups showed a slightly higher pass rate than the remaining course cohort while also obtaining a slightly higher average final grade. Students also reported positive perceptions of the integrated workflow, particularly regarding laboratory preparation, continuous monitoring of their learning progress, and formative feedback. Although the pilot was limited in duration and scale, the results provide preliminary evidence of the practical feasibility of the proposed framework and support its further evaluation in other engineering laboratory contexts.

In this study, a digital-twin-oriented integration architecture was developed for district heating networks in a laboratory Software-in-the-Loop (SIL) environment and evaluated. The implemented testbed combined a virtual SIMATIC S7-1500 controller executed in S7-PLCSIM Advanced V8.0, WinCC Runtime Advanced, OPC UA communication, a Python-based integration layer, and a Fluidit Heat thermohydraulic model. The physical district heating network, field sensors, pumps, actuators, and an operational field PLC were not connected to the experimental loop. Field-derived heat load and pressure values for five monitored consumers were stored in PLC data blocks. Python validated and mapped the tags, initiated predefined Fluidit Heat scenarios, processed the model outputs, and returned the results to the PLC for SCADA visualization. Six operating scenarios were evaluated. In the combined baseline and 120% demand scenarios, the delivered heat increased from 2369.84 to 2843.79 kW, plant thermal output from 2377.14 to 2850.80 kW, and plant flow from 12.288 to 14.737 L/s. The maximum supply velocity increased from 1.192 to 1.430 m/s, and the maximum unit pressure loss increased from 1.338 to 1.912 bar/km (+42.92%). The minimum consumer differential pressure remained at approximately 4.000 bar, above the adopted 3.5 bar criterion, and no heat deficit was calculated. The experiment demonstrated functional software interoperability, event-driven scenario execution, and bidirectional simulation data exchange. Because the baseline values contributed to model parameterization and the physical network was not connected, the comparison was a consistency assessment rather than independent field validation. Automatic optimization, autonomous preferred-mode selection, and actuator-level control were not implemented.

This study examined significant progress in intelligent manufacturing (IM) technologies in collaboration with a prominent producer of electric power transformers. The study particularly focused on the role of intelligent supply chain management (SCM) technologies in optimizing the manufacturing process. The intelligent SCM modules incorporated in the intelligent machine demonstration utilize an ontology to establish linguistic linkages across key aspects such as intelligent supplier selection, component ordering, and intelligent product quality prediction. These modules are essential in synchronizing orders with analytic hierarchy process analysis and multi-objective integer optimization, thereby improving both the efficiency and quality of the manufacturing process. One of the key challenges faced by decision-makers is identifying multiple feasible solutions while adhering to stringent operational constraints. To provide further insights, this study also includes a comprehensive literature review of the transformer manufacturing process, covering advanced technologies, intelligent SCM, optimization techniques in transformer design, and various IM methods. This review critically examines the advantages and limitations of existing solutions, identifying areas where further advancements are needed. By integrating intelligent supply chain technologies with manufacturing processes, this study highlights potential improvements that can enhance operational performance and decision-making in transformer production. Received: 5 January 2025 | Revised: 26 March 2025 | Accepted: 14 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Adilbek Tazhibayev: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing. Irbulat Utepbergenov: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Iouliia Skliarova: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – review & editing.

There are many compelling justifications for the significance of MV/LV distribution transformers in facilitating customer-centric services in the next years. The integration of transformer-level intelligence has the potential to strengthen the characteristics of the low voltage (LV) grid management system, which is often lacking in automation. The transformer enables the use of advanced technologies such as local private networks and EDGE computing. The transformer plays a vital role in connecting the appliances of end consumers with the medium voltage grid. Typically, the location of the facility is in close proximity to the client inside a neighboring energy village. Transformer stations are thus ideal locations for the placement of batteries that facilitate the sustenance of the local energy community, bigger photovoltaic panel systems, and expedited charging apparatus. This article presents, use of Smart Digital Nodes (SDNs) as central nodes within the energy community, replacing the conventional MV/LV distribution transformers. This research is intended to provide a new digital service model at the distribution transformer level. A real-life scenarios are presented. Hence, the transformer station presents a very advantageous site for the integration of batteries, substantial photovoltaic (PV) panel sets, or rapid charging devices within the local energy community.

The paper suggests fast hardware accelerators for discovering clusters of zeros and/or ones in binary vectors. Any cluster is composed of successive bits with the same value (either 1 or 0). Search for such segments is required in many practical problems, for example, coding, data, and image processing. The proposed solutions enable, for a given vector, answering such questions as how many one/zero clusters can be found; what is the largest number of consecutive ones/zeros; what is the number of clusters having k consecutive ones/zeros; is the vector only composed of segments with exactly k consecutive ones/zeros; and some others. The relevant practical applications, for which acceleration is required, are also discussed. The paper suggests two core architectural solutions that are based on combinational and iterative networks of gates. Each network is modeled in software (C++ language) and then specified in a hardware-description language (VHDL), synthesized, and implemented in FPGA. Finally, the results of the circuits’ evaluations and comparisons are presented.

This paper is devoted to identifying online teaching strategies appropriate for blended and face-to-face higher STEM (Science, Technology, Engineering, and Mathematics) education. The study is inspired by the experience gained during the COVID-19 pandemic lockdown, which forced many higher education institutions worldwide to shift abruptly to distance education and try many new tools, teaching methods, and strategies. Some of these tools and strategies were abandoned as soon as the lockdown had been lifted and the institutions returned to their regular functioning, but some of them are bound to stay. Certainly, it would be beneficial to include the most valuable of the gained skills and competences in traditional on-campus and blended courses. The study is based on an online questionnaire, addressed to the STEM faculty of the University of Aveiro, Portugal (which is an example of an institution that used to provide face-to-face instruction), whose analysis permits to derive a number of important recommendations. The results are compared with our previous work, where the students’ perspectives were analyzed, and similarities and discrepancies in appreciation of the involved parties are highlighted. This work extends the body of knowledge about the impact of the COVID-19 pandemic on STEM education by examining the challenges and opportunities faced by teachers. The recommendations derived contribute to improving the learning outcomes of online STEM education in many similar institutions.

The paper selects reusable operations over binary and ternary vectors that are frequently used in many important practical applications (that are explicitly ind

In this paper, we aim to identify online teaching and learning practices that would be beneficial for blended and traditional on-campus education within STEM (Science, Technology, Engineering, and Mathematics) courses. Our university, as well as the majority of higher education institutions worldwide, has had few to no experience in delivering full online courses before 2020. The teaching process was, however, severely affected and modified by the COVID-19 pandemic, forcing an abrupt and unprepared shift towards online education. In this work, we look at the pandemic as causing a very favorable side effect that forced the university to study, test, apply, and evaluate the benefits and drawbacks of online education and assessment methods. The study is a result of joint efforts from different departments at the University of Aveiro, Portugal, connected to STEM undergraduate and graduate programs and is based on a questionnaire targeted towards students. In total, 167 valid STEM students’ answers have been collected and analyzed, both quantitatively and qualitatively. As the result, the best teaching and learning practices are identified and the main difficulties and obstacles experienced by students are detected. Some of the problems are common to many higher education institutions, such as the lack of teacher preparation in delivering quality online synchronous and asynchronous classes, technical limitations (network bandwidth/weak equipment), ineffective communication during synchronous classes, gaps in student skills, and low activity of some students and even teachers. We believe that the presented results would allow for improving future on-campus, distance, and blended learning courses, particularly through avoiding less effective teaching and assessment methods and favoring those techniques that students consider more efficient. This ultimately would lead to a more rewarding teaching/learning experience.