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Strain-dependent lactic acid bacteria fermentation modulates nutritional quality and bioactive properties of phycocyanin-rich extract from A. platensis /
No full textPhycocyanin-rich extracts from Arthrospira platensis (spirulina) are hailed for their bioactive properties, amino acid composition, and micronutrient content. However, their further valorisation through microbial fermentation remains largely underexplored. In this study, we applied strain-dependent lactic acid bacteria (LAB) fermentation as a process-engineering strategy to modulate the biochemical profile and functional potential of phycocyanin-rich extracts under controlled anaerobic conditions. Lactiplantibacillus plantarum, Lactobacillus acidophilus , and Levilactobacillus brevis all achieved high cell densities within 24 h, accompanied by strong acidification linked to glucose utilisation and lactic acid formation. Fermentation induced characteristic strain-specific changes, including reductions in phycobiliproteins, shifts in free amino acid profiles, biosynthesis of water-soluble vitamins (notably thiamine and pyridoxine), and accumulation of biogenic amines at levels well below safety thresholds. Fermented extracts also exhibited antimicrobial activity, particularly in L. plantarum -fermented samples. These findings suggest that LAB fermentation can significantly alter the nutritional quality and biofunctional potential of phycocyanin-rich extracts, providing mechanistic insights and knowledge for the development of a next-generation fermented algal-based functional ingredients for future food systems
Kinetic parameters of transesterification of rapeseed oil with butanol using dolomite as heterogeneous catalyst /
No full textWe analyzed the kinetic parameters of rapeseed oil transesterification with butanol using dolomite as a heterogeneous catalyst under the optimal conditions that we previously determined. At 5.24 wt% dolomite, 110 °C, and a 13.71:1 M ratio of butanol to oil, we found that the reaction followed an irreversible pseudo-first-order model, which we successfully implemented in an Aspen Plus simulation software to design the model of kinetic reactor. In addition, we proposed purification steps for the reactive mixture within the same model to produce biodiesel following requirements of standard EN 14214
In vitro antioxidant, photoprotective, and volatile compound profile of supercritical CO2 extracts from dandelion (Taraxacum officinale L.) flowers /
No full textThis study aimed to develop a sustainable approach for isolating bioactive lipophilic components from Taraxacum officinale flowers using supercritical carbon dioxide extraction (SFE-CO2) and to assess the effect of adding 5% ethanol (EtOH) as a co-solvent on extraction yield, in vitro antioxidant capacity in CUPRAC and ABTS assays (TEACCUPRAC and TEACABTS), total phenolic (TPC) and flavonoid (TFC) content, β-carotene concentration, and photoprotective potential, expressed as the sun protection factor (SPF). SFE-CO2 at 35 MPa and 40 °C resulted in 50% of the total yield within 15 min, with equilibrium reached after 120 min (final yield of 4.6 g/100 g flowers). Co-solvent addition increased yield by ~50% and shortened extraction time. The EtOH-modified extract exhibited markedly higher antioxidant activity, with a 2-fold increase in TEACCUPRAC (167 mg TE/g E), an 11-fold increase in TEACABTS (194 mg TE/g E), and a 3-fold increase in TPC (91 mg GAE/g E), along with improved recovery of flavonoids and β-carotene. Volatile profiling revealed monoterpenoids, aldehydes, and esters as dominant groups, with carvone (14.0–16.5%) and dill ether (4.2–5.8%) as major contributors to aroma. The SFE-CO2 + 5% EtOH extract achieved the highest SPF value (49.5 at 1 mg/mL; SPF > 6 at >0.1 mg/mL), indicating strong photoprotective potential and potential suitability for natural antioxidant and cosmetic applications
Factors of artificial intelligence integration into project management.
No full textIn recent years, artificial intelligence has become an increasingly significant factor in organizational activities, accelerating digital transformation and creating competitive advantage across various sectors. Scientific research indicates that artificial intelligence solutions enable the optimization of business processes, the generation of real-time insights, more efficient resource management, and evidence-based decision-making. The growing potential of artificial intelligence is particularly evident in the field of project management, as increasing project complexity, high requirements for time and cost control, and the need for effective risk management encourage the adoption of more advanced solutions. This master’s thesis aims to evaluate the integration of artificial intelligence in project management. Integration is examined through the target areas defined in the PMBOK 8th edition, which are essential for implementing project management principles. These target areas reveal the essence of project management regardless of the applied methodological approach. The thesis examines the interconnections between project management and organizational change and identifies the value created by artificial intelligence across various target areas. The literature reveals a lack of practical guidelines for artificial intelligence integration in project management, both in Lithuania and globally; therefore, the objective of this study is to identify the factors that determine successful artificial intelligence integration in project management processes. Research object – the integration of artificial intelligence in project management areas. Research results. The analysis of scientific literature and the conducted empirical study revealed that artificial intelligence integration in project management is a promising practice; however, its level of implementation maturity within organizations remains limited. The findings show that although the adoption of artificial intelligence is increasing globally and organizations plan to actively integrate AI-based solutions, in practice, it is most often applied as a supporting rather than a strategic tool. Five target areas of project management were identified: initiation, planning, execution, monitoring and control, and closure. Artificial intelligence integration and its generated value are evident across all target areas; however, the study revealed that the greatest value created by artificial intelligence is observed in the planning area. Nevertheless, empirical data demonstrated the broader applicability of artificial intelligence in other areas than previously emphasized in the literature. It was determined that artificial intelligence contributes to time savings, increased efficiency, and the automation of routine tasks, with these benefits achieved using various AI tools, such as Microsoft Project, ChatGPT, and others. The study also revealed that organizational factors have the greatest influence on successful artificial intelligence integration, particularly management support and financial resources, while technological, human, ethical, and external factors also remain significant. The key success factors identified include employee training, consistent communication, and the assessment of organizational readiness
The impact of international trade barriers on Lithuania‘s exports.
No full textAs a small open economy, Lithuania is highly dependent on international trade, therefore trade barriers, tariff and non-tariff shocks have a direct impact on the country's export volumes and economic stability. The aim of this master's final degree project is to assess the impact of international trade barriers on Lithuanian exports. The project discusses trends in international trade barriers, the theoretical basis of trade barriers and methods for assessing their impact on the economy, and assesses the impact of international trade barriers on Lithuanian exports in 2015-2024, with a focus on trade with the US and Russia due to the sanctions imposed on Russia and the US - China trade war. Based on an analysis of scientific literature, it was found that international trade barriers have a negative impact on the economy, which reduces trade volumes, restricts the movement of capital, disrupts global supply chains, and increases production costs. In order to determine whether trade barriers, sanctions, and customs tariffs affect changes in Lithuanian exports, a correlation-regression analysis was performed. It was found that there is a link between sanctions and exports of goods produced in Lithuania, and that sanctions have a statistically significant negative impact on exports of goods produced in Lithuania, while US import tariffs do not have a statistically significant impact on changes in Lithuanian exports
Is digitalization leading to CO2 emission cutting? /
No full textPurpose – Our article aims to contribute to the main research question of whether digitalization can be used to mitigate carbon emissions. One of the main challenges in capturing the effects of digitalization on carbon emissions lies within the measurement. Design/methodology/approach – We create six proxiesto measure digitalization thatrepresent the dynamics of the ICT sector, relative size, relative business expenditures of R&D in the ICT sector, the relative imports and exports of ICT goods and relative digital capital. We perform OLS regression on a sample covering 26 European Union countries during the time period 2003–2019. To add statistical robustness, we perform the quantile panel regression. Findings – Ourresultsshow that the relative size ofthe ICTsector and digital capital have a neutral impact on the country’s carbon emissions. An increase in ICT imports of goods and ICT exports of goods as a ratio of the overall country’s imports and exports, on the other hand, could lead to an increase in carbon emissions. On the other hand, the net trading balance of ICT goods (ICT exports minus ICT imports) in our data set for EU countries lowers carbon emissions. Our results provide no conclusive evidence for a relationship between business expenditures on R&D in the ICT sector and carbon emissions. Originality/value – We contribute to existing literature by creating new measurements to capture digitalization and identifying which digitalization aspects either enhance or diminish carbon emissions, and we apply this approach to the European Union based on 26 countries for the period of 2003–2019
Do pension funds beat inflation? Assessing trend-dependent risk and dominance techniques /
No full textIn this paper, we address whether pension fund investments are losing value over time. We propose a new methodology for pension fund risk and performance evaluation based on the trend-risk measurement concept. We analyze the two-sided and downside deviations from a given trend. In the long term, inflation and consumer price changes significantly affect an investor’s wealth. For this reason, we consider these macroeconomic indicators to represent a time-dependent trend, which pension funds should outperform. Furthermore, we propose the concept of Time-Cumulative Dominance. This methodology serves as a valuable tool for both portfolio managers and regulators. In the empirical part, we study this new methodology across various pension funds in Lithuania while reflecting on various market conditions and regimes detected by Hidden Markov Models. The results highlight the impact of portfolio composition on the ability to outperform inflation and consumer price changes in the long-term period. We also observe a negative effect during market anomalies
Improving technical department efficiency through application of artificial intelligence in fault management process.
No full textThe master’s thesis examines the possibilities of applying smart technologies to equipment maintenance in order to reduce downtime caused by failures and increase production stability. The relevance of the topic is driven by the growing complexity of automated production lines and the need to restore their operation quickly under conditions of skilled labour shortage. The novelty of the research lies in the practical testing of an artificial intelligence-based conversational system in the maintenance department of a manufacturing company. This system integrates equipment documentation, failure history and technicians’ accumulated knowledge and provides real-time support for repair decisions. The object of the research is the work of the case company’s maintenance department when using artificial intelligence tools for equipment failure diagnostics and troubleshooting. The aim of the project is to assess the potential of artificial intelligence tools in maintenance activities in order to increase efficiency in failure diagnosis and repair processes. The theoretical part reviews maintenance department performance indicators, methods for assessing equipment reliability, and the challenges of knowledge transfer and onboarding of new technicians, which can be addressed through digital knowledge bases and intelligent support systems. The empirical research is based on a single-case study. Data from the computerised maintenance management system were analysed, including production volumes, failure frequency, mean time to repair and mean time between failures, as well as technicians’ work with the artificial intelligence tool in daily practice. The results show that, as production volumes increased, the artificial intelligence solution helped to keep failure levels stable, shorten the diagnostic stage and reduce the average repair time, which in turn lowered equipment downtime costs. The economic analysis indicates that, even under conservative assumptions about the contribution of artificial intelligence, the costs of developing and maintaining the system can be recovered within a relatively short period. The findings are limited by the focus on a single shop floor and a short observation period, but they indicate directions for further research by extending the solution to different equipment groups and assessing its long-term impact on equipment reliability. The thesis concludes that artificial intelligence can become an effective support tool for maintenance technicians if it is integrated into a clearly defined failure management process, with high-quality data, well-maintained documentation and continuous development of employee competences
From pixels to plates: exploring AI stimuli and digital engagement in reducing food waste behavior in Lithuania among generation Z and Y /
No full textThe global issue of food waste is a significant concern due to its extensive social, economic, and environmental repercussions. To attain our sustainable future objectives, we must confront the food waste challenge directly. This study, grounded on the Stimulus–Organism–Response (S-O-R) theoretical framework, examines the impact of AI-based stimuli—passion, usability, perceived personalization, and perceived interactivity—on users’ intentions of minimizing food waste. Social presence and psychological engagement signify internal organism (O) states, while self-efficacy acts as the moderating factor between these organism states and intention (R). Data were gathered via Computer-Assisted Web Interviewing (CAWI) in a stratified quota sample of 315 participants in Lithuania, concentrating on Generation Y and Millennial Generation Z consumers of the Samsung Food app, aimed at promoting food waste reduction. Participants were pre-screened and recruited via several means to guarantee an adequate sample. The results indicate that passion, usability, and perceived interactivity substantially influence social presence and psychological engagement. Nonetheless, these organism-level variables did not have an immediate impact on behavioral intention, and all indirect (mediated) effects from stimulus response were significantly rejected. Conversely, self-efficacy considerably influenced the association between social presence and psychological engagement with intention, indicating that enhanced user confidence enhances the possibility of turning engagement into behavioral responses. This study features generational differences between Y and Z and only found significant interaction between perceived personalization and social presence in Generation Y, as compared to Generation Z. This work extends the literature on AI-driven behavior modification by asserting that mere involvement is inadequate. Enabling consumers by enhancing self-efficacy is crucial for developing viable AI-based applications that encourage sustainable customer behavior
Unlocking thermal flexibility through demand-side response: baseline methodology assessment and heating electrification in the Baltic region /
No full textDemand-side response (DSR) flexibility is gaining increasing attention across power systems undergoing the energy transition, where renewable generation now dominates supply patterns. However, its reliable integration remains constrained by baseline methods that fail to accurately capture the operational characteristics of distributed demand resources, particularly thermally driven loads. This research provides a practical, decision-oriented framework for baseline selection, combining a two-stage process of technical feasibility assessment and multi-criteria performance evaluation. Eight baseline-method families are systematically evaluated, with empirical validation using 39 Latvian consumption sites and a Lithuanian hybrid heat-pump and photovoltaic system demonstration. Results show that static historical baselines are insufficient to capture thermal inertia, cyclic heat-pump operation, and cyclic compressor behaviour, while adaptive, weather- and PV-sensitive methods substantially improve accuracy (MAE reduction from 6.65 to 3.62 kWh), ensuring robust and transparent flexibility quantification. Market welfare simulations using 85 days of Baltic 2024 summer day-ahead market data indicate that even modest volumes of price-responsive DSR (5–50 MW) can reduce scarcity-hour market-clearing prices by up to 33 €/MWh and increase substantial social welfare gains (0.59–4.3 million euros) highlighting the tangible economic benefits of improved baseline accuracy. Overall, the study establishes that accurate, integrity-preserving baselines coupled with digital metering infrastructure unlock significant short-term and intraday flexibility, bridging technical precision with system-level market and welfare outcomes