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Developing a lifelong learning service model to enhance SME competitiveness in the green transition
No full textThe realization of the green transition requires sustainable, carbon-neutral industry, with a skilled workforce,
which needs a new service model of lifelong learning to facilitate this systemic change. In Finland, three Universities
of Applied Sciences are collaboratively developing a lifelong learning service model (LLLSM) to enhance the competitiveness
of SMEs in three sectors of the manufacturing industry. By promoting industrial sustainability and carbon
neutrality, the pilot addresses critical needs for skills development and knowledge sharing. The methodology focused
on co-creation within a collaborative network, utilizing surveys, interviews, and pilot implementations to develop the
model. Academic, industrial, and sectoral stakeholders participated in the design of this practical and scalable solution.
The piloted LLLSM model provides SMEs with tools to adopt green practices and supports their role in a sustainable
and competitive economy.Taip / YesEuropean Union from the European Social Fund Plus (ESF+)Continuous learning service model for the Needs of the Sustainable and carbon-neutral manufacturing IndustryR-0093
The interaction of physical and mental risk factors on office workers in the banking sector in Latvia
No full textOver the last years, physical and especially psychosocial risks have emerged as one of the main ones for intellectual
work performers, including workers in the banking sector. This is due to the context of new technologies, digitalization
and automatization of work processes. The interaction of physical and mental stress in the working environment
and the risk factors have a significant impact on the employee’s wellbeing and performance. Aim of the research
was to investigate the interaction of physical and mental strain on office workers in the banking sector in Latvia based
on questionnaire and NASA-TLX method application results. Questionnaire results proved that main physical risks
at work are prolonged sitting and discomfort in various body parts, but mental strain involves excessive work-related
tasks, high-speed work, physical exhaustion. Using NASA-TLX method it was proved that banking sector employees
experienced a moderate level of mental workload. Female employees reported higher scores across all NASA-TLX dimensions,
suggesting greater cognitive and emotional strain. Interaction between physical and mental risk factors at
the banking sector work environment can cause significant health and work efficiency issues.Impact of ergonomics and psychosocial risks on work performance for office workers in banking sectorTaip / YesImpact of ergonomics and psychosocial risks on work performance for office workers in banking sectorLU-BA-ZG-2024/1-0018ESS2024/465-ZG-
Artificial intelligence for building management systems: a review of data and security challenges
No full textSiekiant didinti pastatų energinį efektyvumą vis daugiau dėmesys kreipiamas į pastatų išmanumą ir energiją
vartojančių sistemų valdymo efektyvumo gerinimą. Dirbtinio intelekto integravimas į pastato mikroklimato sistemų
valdymą yra inovatyvus būdas, leidžiantis sutaupyti energijos. Visgi čia svarbu ne tik dirbtinio intelekto modelio pasirinkimas,
bet kritiškai svarbus elementas yra duomenys – jų kiekis, kokybė ir patikimumas, prieinamumas bei kiti aspektai,
tarp jų ir asmeninių duomenų panaudojimo etiniai aspektai. Šiame straipsnyje yra apžvelgiami su duomenimis
susiję iššūkiai, su kuriais susiduriama siekiant panaudoti dirbtinį intelektą šildymo, vėdinimo ir oro kondicionavimo
(ŠVOK) sistemų valdymui tobulinti, bei pateikiamos įžvalgos ir rekomendacijos.In the context of enhancing the energy efficiency
of buildings, there is a growing emphasis on the development
of smart buildings and the optimisation of energy management
systems. The integration of artificial intelligence into the
control of building indoor climate systems represents a novel
approach for energy conservation. However, it is imperative to
recognise that the selection of the AI model is not the sole determining
factor in the efficacy of this approach. The quality,
quantity and reliability of the data, its availability, and other
pertinent considerations, such as the ethical utilisation of personal
data, are also crucial factors. This paper discusses the
data-related challenges of using artificial intelligence to improve
the control of HVAC systems and provides insights and
recommendations.Taip / Ye
Biblioteka informuoja, 2025 Nr. 25 (722)
No full textNaujai į Web of Science ir Scopus įtrauktų Vilnius Gedimino technikos darbuotojų publikacijų sąrašai ir kitos bibliotekos aktualijos.25 (722)202
Improving Engineering Education for a Sustainable Future
No full textIntegrating sustainability into engineering education is essential for equipping future professionals with the skills to build a resource-efficient world. This paper explores an interdisciplinary approach at TTK University of Applied Sciences, Tallinn, Estonia, to embed sustainability into core engineering courses. The study applies AI algorithms to evaluate pedagogical strategies that foster responsibility, ethical decision-making, and innovation, which is a novel method in contrast to traditional ones. By integrating AI-based analysis with sustainability-focused engineering curricula, the research presents a data-driven, interdisciplinary model that enhances ethical decision-making and ecological responsibility. Results emphasize the value of a learner-centred approach—optimization, personalization, interactivity, and adaptability—to prepare professionals with the ecological mindset needed for modern engineering challenges.Taip / Ye
Design and Analysis of a Wide Input Voltage Range Low-Dropout Regulator in TSMC 180nm BCD Technology
No full textThis paper presents the design and simulation of a low-dropout (LDO) linear voltage regulator intended for integration in high-voltage DC-DC converter systems. Implemented in a 180 nm BCD CMOS process, the proposed LDO supports an input voltage range of 8 V to 60 V and provides a regulated 5 V output with up to 20 mA load current. A custom three-stage error amplifier is introduced, combining low-voltage signal processing with high-voltage interfacing, while a self-biased startup circuit ensures reliable operation across process, voltage, and temperature (PVT) variations. The LDO achieves a worst-case quiescent current of 95.5 µA at 60 V input and 125 °C, and demonstrates excellent line and load regulation of 0.075 mV/V and 0.215 mV/mA, respectively. Power supply rejection reaches –60 dB at 1 kHz under nominal conditions. Compared with recent state-of-the-art designs, this work achieves a favorable balance of wide input range, regulation precision, and power efficiency, making it well suited for analog and digital internal supply rails in automotive and industrial applications.Research Collaborative Seed Grant Program between NSYSU and Vilnius Gediminas Technical UniversityNSYSU-VGTU-2024-01Taip / YesNSYSU-VILNIUS TECH-2024-0
Evaluating CNN, RNN, and Vision Transformer for Emotion Recognition: Strengths and Weaknesses
No full textThis paper explores three prominent deep learning architectures — Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Vision Transformers (ViT) — for emotion recognition, examining their potential strengths and weaknesses under various conditions. It discusses how each approach may capture critical spatial, temporal, or global features in emotional data, highlighting differences in feature extraction, representational capacity, and scalability. Additionally, new solutions are proposed to enhance accuracy and adaptability, integrating design principles that address recognized challenges in real-world implementations. Novel insights are offered on aligning model selection with specific application demands, such as the nature of input signals, available computational resources, and desired real-time performance. While the comparative analysis remains broad to accommodate diverse use cases, it underscores the importance of carefully balancing accuracy and efficiency. Conclusions drawn from the investigation include recommendations on when each architecture may be most advantageous, providing a flexible framework for researchers and practitioners to navigate the trade-offs. These findings have implications for developing adaptive emotion recognition systems that leverage state-of-the-art deep learning techniques across multiple contexts.Taip / Ye
28 th International Conference Mathematical Modelling and Analysis, May 26–29, 2025, Druskininkai, Lithuania. Abstracts
No full textApplying technology acceptance models in the digital transformation of educational institutions: a systematic review
No full textThe implementation of digital transformations in the management of educational organizations is a complex
process that requires not only structured solutions but also consistent realization. Technology adoption models, such
as TPACK, TAM, and UTAUT, help structure and analyze the factors that drive the adoption of digital technologies in
educational institutions. This article aims to analyze the principles of operation of technology adoption models (TAM,
UTAUT, and TPACK) and their application in educational organizations undergoing digital transformation. The study
examines how these models help understand technology adoption in the education sector, highlighting the key challenges
and opportunities revealed in previous scientific research. The study applies a systematic analysis of scientific
sources using the PRISMA method. The research results showed the use of TAM and UTAUT models in digital transformations
of educational institutions does not cover the pedagogical aspect. Therefore, the TPACK model effectively
expands the findings and provides a broader framework for identifying the reasons behind the slow adoption of digital
transformations in educational institutions. The analysis presented in the article can be useful for educational institutions
seeking to implement digital transformations more efficiently and rapidly within their organizations.Taip / Ye
Experimental studies on the application of the adsorbtion method for chromium ions removal from contaminated water
No full textBesiplečianti pramonė ir auganti miestų urbanizacija didina nuotekų užterštumą sunkiaisiais metalais. Nepakankamai
efektyviai išvalytose nuotekose esantys chromo jonai gali tapti toksiški aplinkai, sukelti neigiamų padarinių
gyviesiems organizmams. Dėl chromo toksiškumo, būtina pašalinti chromo jonus iš užteršto vandens, siekiant
apsaugoti žmonių sveikatą ir aplinką, o vienas tinkamiausių chromo jonų šalinimo metodų iš užteršto vandens yra
adsorbcija. Šiame straipsnyje aprašomas adsorbcijos metodo taikymas chromo jonams šalinti iš užteršto vandens, naudojant
adsorbentą – keramzitą. Buvo tiriami keli svarbūs parametrai: adsorbcijos proceso priklausomybė nuo laiko,
chromo jonų koncentracijos ir pH, tirtas adsorbcijos procesas kolonėlėje. Tyrimo rezultatai parodė, kad keramzitas yra
tinkamas adsorbentas chromo jonams šalinti iš užteršto vandens.Expanding industry and growing urbanization of
cities increase the pollution of wastewater with heavy metals.
Chromium ions contained in insufficiently effectively treated
wastewater can become toxic to the environment and cause
negative consequences for living organisms. Due to the toxicity
of chromium, it is necessary to remove chromium ions
from contaminated water in order to protect human health
and the environment, and one of the most suitable methods
for removing chromium ions from contaminated water is adsorption.
This article describes the application of the adsorption
method for removing chromium ions from contaminated
water using expanded clay as a adsorbent. Several important
parameters were studied: the dependence of the adsorption
process on time, chromium ion concentration and pH, and
the adsorption process in the column was studied. The results
of the study showed that expanded clay is a suitable adsorbent
for removing chromium ions from contaminated water.Taip / Ye