Riga Technical University

The publication highlights the versatile applications and synthesis strategies of 7H-benzo[de]anthracen-7-one (benzanthrone) derivatives explored in the past 27 years, obtained through direct synthesis such as palladium-catalyzed cyclizations or func-tionalization through nucleophilic aromatic substitution, Kabachnik-Fields, Sonogashira, Vilsmeier-Haack type and other reactions. These approaches have enabled the development of compounds with tailored luminescent properties. Among the diverse applications, significant focus is placed on the role of benzanthrone derivatives in biomedical imaging, liquid crystal systems and fluorescence-based sensing technologies, with emerging uses in optoelectronics and materials science. This comprehensive review underscores the pivotal role of benzanthrone derivatives in advancing fluorescent materials science while highlighting ongoing innovations and their impact on technological and scientific advancements.

The study investigates the application of white rot fungi for reactor–scale microalgae harvesting and explores the mechanisms underlying the algal–fungal interactions and their impact on biomass composition. Enzymatic analysis and microscopy revealed that the formation of algal-fungal complexes and successful harvesting are coupled with fungal cellulose-degrading enzyme production and hydrolytic processes of microalgae cells. Fluorescence intensity decreased by over 80 % in cells stained with Calcofluor-white after interaction with white rot fungi, indicating the reduction in cellulose content in microalgal cells caused by fungal enzymatic activity. These enzymes also caused significant cell damage and more than 50 % decrease in microalgae cell size. The presence of cellulolytic enzymes broadens the potential application of the resulting biomass in various biotechnological applications. Moreover, reactor-scale bioflocculation resulted in over 95 % T. obliquus and almost 85 % C. vulgaris harvesting efficiency from secondary wastewater within less than 24 h, demonstrating the method's scalability and industrial applicability.

An identification of material properties in each inverse technique based on vibration tests is performed by minimizing the error functional between the experimental and numerical parameters of structural responses. Unfortunately, the dynamic parameters of test samples are very sensitive to experimental errors and numerical models applied in the identification procedure due to measurement inaccuracies or an approximated nature of the numerical analysis. In the present study an influence of numerical model errors associated with the hypotheses used for a material description on identified mechanical properties of laminated composites is demonstrated. For this purpose, two beam-like samples with different reinforcement schemes have been prepared by using simple manual layout technology. Lamina properties have been identified from vibration tests by using unidirectional and woven composite assumptions.

This study explores an advanced approach to enhancing the antimicrobial efficacy and hydrophilicity of poly(lactic acid) (PLA) scaffolds through the strategic incorporation of cellulose nanocrystals (CNC). The compatibility between these biodegradable polymers was investigated to optimize antimicrobial agent release while preserving structural integrity. PLA nanocomposites incorporating the antimicrobial agents curcumin (Cur) or polyhexamethylene biguanide (PHMB) were fabricated using three distinct electrospinning-based methodologies. The antibacterial properties were assessed via a disc diffusion test against five bacterial strains: Escherichia coli, Escherichia coli B+, Lactobacillus salivarius, Streptococcus sanguinis, and Streptococcus mutans. In addition, drug release experiments were conducted to determine the diffusion kinetics in a simulated blood serum medium, demonstrating sustained drug release for up to 98 hours. PHMB demonstrated potent antibacterial activity, while curcumin primarily exhibited bacteriostatic effects. The thermal stability of the nanocomposites exhibited an increase of up to 41 °C in the maximum degradation temperature. The mechanical properties were assessed to further examine the interactions between CNC and PLA and the possibility to reshape the materials for different delivery approaches. The findings underscore the crucial role of CNC in modulating the interaction between PLA and antimicrobial agents, making it a promising candidate for biomedical applications requiring controlled drug release. This study provides valuable insights into the structural, thermal, and antibacterial performance of CNC–PLA nanocomposites, establishing a strong foundation for the development of advanced biodegradable materials for drug delivery and antimicrobial applications.

The construction industry plays an important part in economic growth but notably contributes to environmental deterioration owing to excessive resource use and waste generation. The implementation of circular economy (CE) principles provides a revolutionary strategy for reducing these unfavourable consequences by encouraging resource efficiency, material reuse, and waste reduction. The article assesses the economic and environmental benefits of principles of CE in civil construction, with a specific emphasis on their use in Sri Lanka. The research contains an analytical examination of current trends, an assessment of economic implications, and an evaluation of environmental sustainability improvements provided by CE adoption. It incorporates global and local case studies to find best practices and obstacles in circular economy implementation. The study technique comprises statistical analysis, including Pearson correlation analysis, to explore links between CE methods such as material recycling, waste management, and energy efficiency. The results demonstrate that CE principles greatly cut operating costs, boost energy efficiency, and minimize carbon footprints. These observations emphasize the significance of concerted efforts among policymakers, industry stakeholders, and regulatory agencies to promote the adoption of sustainable construction practices. The article continues by underlining the significance of organized legislative frameworks and technical advancements to assist the circular transition within the construction industry.

This paper presents a developed model for assessing the competitiveness of water transport enterprises, taking into account their specific characteristics. The model includes eight components: economic, social, material and technical, geographical, innovation and information support, organizational and management, environmental, and infrastructural fields. It enables the diagnosis and comparison of the competitiveness level of water transport enterprises and provides recommendations for improvement. The research was conducted on maritime industry enterprises in the Odessa region as a case study. The results highlighted the need for optimization of port areas, infrastructure development, and attracting investments to enhance the competitiveness of the enterprises. The developed model can be valuable for analyzing and improving the competitiveness of maritime transportation enterprises and promoting regional development.

This paper presents a dynamic model for the transition of logistics service providers from traditional to green transportation services. Driven by regulatory requirements and increasing market demand for sustainable practices, the need for such a model is critical for logistics providers to manage the complexities and cost implications of adopting green technologies. Utilizing real-world data from a leading Baltic logistics provider, the model integrates both micro and macro-level factors, offering a decision-making tool that simulates various scenarios and fore-casts outcomes over a ten-year period. Key findings reveal that while green transportation costs are projected to decrease below traditional costs, demand for traditional services will persist, highlighting the need for a balanced approach in planning. This research contributes a novel framework that aids logistics providers in strategic planning and forecasting the evolution of green logistics, providing valuable insights into future trends in the industry

Background/Objectives: The timely diagnostics of bladder cancer is still a challenge in clinical settings. The reliability of conventional testing methods does not reach desirable accuracy and sensitivity, and it has an invasive nature. The present study examines the application of machine learning to improve bladder cancer diagnostics by integrating miRNA expression levels, demographic routine laboratory test results, and clinical data. We proposed that merging these datasets would enhance diagnostic accuracy. Methods: This study combined molecular biology methods for liquid biopsy, routine clinical data, and application of machine learning approach for the acquired data analysis. We evaluated urinary exosome miRNA expression data in combination with patient test results, as well as clinical and demographic data using three machine learning models: Random Forest, SVM, and XGBoost classifiers. Results: Based solely on miRNA data, the SVM model achieved an ROC curve area of 0.75. Patient analysis’ clinical and demographic data obtained ROC curve area of 0.80. Combining both data types enhanced performance, resulting in an F1 score of 0.79 and an ROC of 0.85. The feature importance analysis identified key predictors, including erythrocytes in urine, age, and several miRNAs. Conclusions: Our findings indicate the potential of a multi-modal approach to improve the accuracy of bladder cancer diagnosis in a non-invasive manner.

This study investigates the effect of phase change materials (PCM) on the properties of modified potato starch binders and hemp shive-based bio-composites, emphasizing their potential for sustainable construction applications. PCM-modified binders have shown reduced viscosity during gelatinization, enhancing their workability and uniformity during processing. A microstructural analysis reveals that PCM addition results in a denser and more cohesive binder network, leading to improved adhesion and reduced porosity. A thermal analysis demonstrates a shift to higher decomposition temperatures and a linear increase in specific heat capacity within the PCM phase-change range (20–30 °C), significantly enhancing the thermal storage capacity of the bio-composites. PCM addition improves compressive strength by up to twice, with optimal performance achieved at 8% PCM additive content. The prolonged cooling time, up to three times longer in bio-composites with PCM additive, highlights their effectiveness in thermal regulation. Additionally, bio-composites with a PCM additive exhibits increased bulk density and reduced water swelling, improving dimensional stability. These findings underline the dual benefits of enhanced thermal and mechanical performance in bio-composites with a PCM additive, making them a viable alternative to conventional building materials.

A large proportion of greenhouse gas emissions come from heating and hot water supply and developed district heating systems will play an important role in meeting climate targets. The research presents a methodology for the study of the influence of combined factors on future thermal energy demand. System dynamics modeling has been applied to residential buildings in terms of renovation, new building construction, subsidy fund and assessment of reducing future thermal energy and greenhouse gas emissions. Changes in the consumption of thermal energy of district and decentralized thermal supply, under the influence of energy efficiency and financial factors were studied. Renovation of old buildings has a great impact on achieving the goals set on the way to climate neutrality by reducing heat energy consumption. As shown by the simulations carried out with the existing funding and legislation, by 2050 the expected reduction for users of district heat supply in Riga is 3 % and for users of alternative heating 2 %, from the existing 2023 consumption.

The recent AI boom requires more focus on energy-efficient and scalable optical interconnects. Silicon Photonics is enabling technology to satisfy growing demand. However, the lack of lasers and high-performance modulators hinders wide-scale adoption. Therefore, we present a heterogeneously integrated Indium Phosphide electro-absorption modulator with Silicon waveguides. We demonstrate up to 256 Gb/s on-off keying, 340 Gb/s 4-level pulse amplitude modulation, 375 Gb/s 6-level pulse amplitude modulation, and 360 Gb/s 8-level pulse amplitude modulation transmission over 500 m and 6 km of single-mode fiber with performance satisfying requirements of 6.25% overhead hard-decision forward error correction threshold of 4.5×10-3. Additionally, we investigate the modulator at 200 Gb/s per lane scenarios, demonstrating excellent performance with a simple seven-tap feed-forward equalizer.

Segmentation neural networks are widely used in medical imaging to identify anomalies that may impact patient health. Despite their effectiveness, these networks face significant challenges, including the need for extensive annotated patient data, time-consuming manual segmentation processes and restricted data access due to privacy concerns. In contrast, classification neural networks, similar to segmentation neural networks, capture essential parameters for identifying objects during training. This paper leverages this characteristic, combined with explainable artificial intelligence (XAI) techniques, to address the challenges of segmentation. By adapting classification neural networks for segmentation tasks, the proposed approach reduces dependency on manual segmentation. To demonstrate this concept, the Medical Segmentation Decathlon ‘Brain Tumours’ dataset was utilised. A ResNet classification neural network was trained, and XAI tools were applied to generate segmentation-like outputs. Our findings reveal that GuidedBackprop is among the most efficient and effective methods, producing heatmaps that closely resemble segmentation masks by accurately highlighting the entirety of the target object.

The radio-over-fiber (RoF) system is promising to support broadband transmission and increased flexibility. To boost channel capacity in multi-carrier RoF systems with variable-rate forward error correction, probabilistic shaping and water-filling-based entropy loading outperforms bit-power loading in terms of achievable information rate. However, its reliance on specific channel conditions limits practical use in channel-dynamic RoF systems, highlighting the need for adaptive entropy loading that requires minimal channel state information. This paper presents a deep neural network-based transfer learning model for adaptive entropy prediction in discrete multi-tone signals, addressing frequency-selective responses in RoF systems. Numerical and experimental results confirm capacity-approaching generalized mutual information (GMI) and smoother normalized GMI (NGMI) performances, consistently achieving the 0.83 NGMI threshold across subcarriers. Unlike traditional methods requiring pre-measured signal-to-noise ratios (SNR), this approach simplifies implementation by using only demodulated data and the received SNR, providing a more channel-independent entropy loading option in dynamic RoF systems.

In this study, ten language models are explored and compared in an English-Latvian semantic information retrieval setting, where the indexed collection of documents is written in English while the query documents are written in Latvian. Currently, no similar research has been done regarding the Latvian language. A dataset of 77736 pairs of articles from Latvian and English Wikipedia was created, transformed into embedding vectors, and used for retrieval experiments with brute force search, Hierarchical Navigable Small World method, and Inverted File Indexing method. The LaBSE language model achieved the best performance for short texts and a version of Sentence-BERT and E5-large for long texts.

Cannabidiol (CBD) is recognized for its therapeutic properties in various conditions. However, CBD’s limited water solubility and sensitivity to environmental stresses hinder its efficacy and bioavailability. Encapsulation in drug delivery systems, particularly liposomes, offers a promising solution. This study aims to prepare CBD-containing liposomes using commercially used lipids distearoyl phosphatidylcholine (DSPC) and dipalmitoyl phosphatidylcholine (DPPC), and 1,2 distearoyl-sn-glycero-3 phosphoethanolamine-N-[carbonyl-amino(polyethylene glycol)-4300] (ammonium salt) (DSPE-PEG) and to perform in vitro studies – cell viability and CBD release. Liposomes were synthesized using thin-film hydration method, and characterized by Fourier-transform infrared (FT-IR) spectroscopy, dynamic light scattering (DLS), and scanning transmission electron microscopy (STEM). DLS analysis revealed that CBD incorporation reduced liposome size by 23–53%, depending on the liposomes. Encapsulation efficiency followed the order: DPPC CBD (63%) < DSPC CBD (74%) < DSPC DPPC CBD (81%) < DSPC DSPE-PEG CBD (87%). CBD release profiles indicated that DPPC CBD liposomes released the highest CBD amount initially, while DSPC DSPE-PEG CBD exhibited sustained release, achieving 79% release over 504 h. In vitro cell viability tests showed that blank liposomes were non-cytotoxic. However, CBD-loaded liposomes significantly reduced cell viability for defined type of CBD containing liposomes. The inclusion of DSPE-PEG improved encapsulation efficiency and liposome stability, making DSPC DSPE-PEG CBD liposomes more suitable for long-term CBD release. Compared to other studies, encapsulation of CBD in liposomes enhances its bioavailability, allowing lower concentrations of CBD to be directly delivered to cells, resulting in observable changes in cell viability.

The paper presents the most comprehensive and large-scale global study to date on how higher education students perceived the use of ChatGPT in early 2024. With a sample of 23,218 students from 109 countries and territories, the study reveals that students primarily used ChatGPT for brainstorming, summarizing texts, and finding research articles, with a few using it for professional and creative writing. They found it useful for simplifying complex information and summarizing content, but less reliable for providing information and supporting classroom learning, though some considered its information clearer than that from peers and teachers. Moreover, students agreed on the need for AI regulations at all levels due to concerns about ChatGPT promoting cheating, plagiarism, and social isolation. However, they believed ChatGPT could potentially enhance their access to knowledge and improve their learning experience, study efficiency, and chances of achieving good grades. While ChatGPT was perceived as effective in potentially improving AI literacy, digital communication, and content creation skills, it was less useful for interpersonal communication, decision-making, numeracy, native language proficiency, and the development of critical thinking skills. Students also felt that ChatGPT would boost demand for AI-related skills and facilitate remote work without significantly impacting unemployment. Emotionally, students mostly felt positive using ChatGPT, with curiosity and calmness being the most common emotions. Further examinations reveal variations in students’ perceptions across different socio-demographic and geographic factors, with key factors influencing students’ use of ChatGPT also being identified. Higher education institutions’ managers and teachers may benefit from these findings while formulating the curricula and instructions/regulations for ChatGPT use, as well as when designing the teaching methods and assessment tools. Moreover, policymakers may also consider the findings when formulating strategies for secondary and higher education system development, especially in light of changing labor market needs and related digital skills development.

This article examines demographic shifts and changing housing needs in Latvia, with a focus on their implications for social housing policy. Latvia faces significant demographic challenges, including population decline, rapid ageing, and changing household structures. The study analyses historical trends in population, age structure, household composition, and migration patterns based on data from the Latvian Central Statistical Bureau and Eurostat. It also assesses the current housing landscape, including characteristics of housing stocks, homeownership rates, rental market dynamics, and social housing provision. The research reveals a growing mismatch between the existing housing stock and evolving needs, particularly for smaller, accessible units suitable for an ageing population. Based on these findings, the article proposes policy recommendations to address Latvia’s evolving housing needs. These include short-term adjustments to current policies, long-term strategic planning for housing development, and innovative financing mechanisms for social housing. The study also explores potential challenges in implementing these recommendations, such as funding restrictions and political acceptance. By aligning housing policy with demographic realities, Latvia can better meet the needs of its changing population and enhance the overall quality of life of its citizens. This research contributes to a better understanding of the adaptation of housing policy adaptation in the context of significant demographic change.