The rapid advancement of high technologies in healthcare requires medical personnel to continuously adapt to new tools and processes. This study explores the impact of remuneration as organisational culture on the well-being of medical professionals working with high technologies and self-perceived health status of inhabitants. The study identifies key factors that influence performance of medical professionals to decrease patients' fear of medical personnel and institutions and increase awareness of qualified medical personnel related to more adequate remuneration. The objectives of this study are to analyse scientific findings on organ-isational culture and its relation to performance of medical personnel, as well as analyse data from the database IVP070 and DSV030 of the Central Statistical Bureau of the Republic of Latvia. For data analysis, descriptive statistics methods were used. Findings indicate that a strong organisational culture characterised by collaboration, leadership support, and continuous learning plays a crucial role in enhancing professional efficiency and patient outcomes. Furthermore, the study highlights how improved well-being and efficiency among medical personnel contribute to the economic sustainability of the healthcare sector, general population and, consequently, the overall economic stability of the country. By improving efficiency through a supportive work environment, healthcare organisations can contribute to better service delivery and a more resilient economy, ultimately leading to increased national productivity and growth. This research highlights the importance of cultivating a positive organisational culture not only to improve individual worker performance and patient outcomes but also to promote the long-term economic sustainability and growth of a country.
The study assesses Latvia's solar energy potential using high-frequency solar irradiation datasets from Jelgava (inland) and Ventspils (coastal). The analysed period covers September 2023 to August 2025, including 69,823 measurements in Jelgava and 69,827 in Ventspils, taken at 10-15-minute intervals. The mean irradiation intensity was 97.2 W/m & sup2; in Jelgava and 106 W/m & sup2; in Ventspils, while maximum values reached 1095 W/m & sup2; and 1097 W/m & sup2;, respectively. Integrated results indicate annual solar availability of approximately 900-1100 kWh/m & sup2;/year in both regions. Seasonal analysis shows very low winter irradiation in Jelgava, with 8-10 W/m & sup2; in December-January, while summer peaks occur in June (199 W/m & sup2;) and remain high in July-August (170-175 W/m & sup2;). The findings confirm that PV generation is technically feasible in Latvia despite its northern latitude. Jelgava demonstrates a higher inland peak potential, whereas Ventspils provides more stable coastal irradiation patterns. The results support regionally optimised PV planning and highlight the need for balancing solutions during the low-irradiance winter months.
The article investigates the dynamic behaviour of a box-shaped building structure subjected to seismic excitation modelled as a sinusoidal base displacement. The structure is represented as a spatial system of interconnected plate and beam elements, accounting for bending, shear, and torsional effects. The problem is solved numerically using the finite difference method. Calculations are performed for three dimensionless excitation frequencies beta(1) = 16, beta(2) = 17, and beta(3) = 17.5, with a base displacement amplitude of A(0) = 0.02 m. Material properties are taken as Eb = 20000 MPa and Ec = 7500 MPa, with geometric parameters a = 3.5 m, b = 5 m, and hc = 0.2 m. Results show that as the excitation frequency approaches the natural frequency (beta approximate to 17.5), resonance leads to a significant increase in displacements. Maximum dimensionless deflections (W/A(0)) occur in the upper regions of bending panels and are notably higher near openings compared to solid sections. The presence of openings increases displacement values, particularly along their edges, while beam and plate elements exhibit consistent deformation patterns.
The article explores the strategic importance of geospatial infrastructure in assessing and planning the use of renewable energy sources (RES) across Ukraine. Despite the growing need for sustainable energy development, the role of spatial data and GIS technologies in determining regional renewable energy potential remains understudied. This study addresses this gap by building an integrated GIS-based model that incorporates a variety of spatial indicators, including climate conditions, topography, solar radiation, wind speed, biomass availability, and water resources. Using spatial analysis, geospatial modelling, and multi-criteria assessment through an index approach, the study assesses how the availability, accuracy, and timeliness of geospatial infrastructure affect energy planning decisions. The results show that high- quality GIS tools significantly optimise the identification of suitable locations for renewable energy facilities. A new approach to integrating environmental and information spatial factors is proposed. The results of the study have practical value for the development of regional and national energy strategies, the creation of digital platforms for assessing the potential of renewable energy sources and guiding political decisions. The authors advocate the creation of a national open-access geoinformation platform that integrates critical spatial datasets for use by government, academic and business structures.
The paper presents a comparative analysis of the electricity consumption profiles of four consumer clusters identified by k-means segmentation of hourly smart-meter data from small and medium enterprises (SMEs) in Latvia. The four clusters were developed using data acquired from 3,016 metering points representing structurally distinct archetypes of temporal consumption behaviour: Cluster 0 (n = 382, evening entertainment), Cluster 1 (n = 1,712, continuously operating infrastructure), Cluster 2 (n = 323, daytime-active retail), and Cluster 3 (n = 599, nocturnal operations). The analysis demonstrates key dimensions of difference across clusters: peak hour of day, day-of-week seasonality, weekend-to-weekday load ratio, diurnal amplitude, and night-time baseload behaviour. These four distinct SME types account for the full range of SME load profile variability observed in the dataset. Implications for demand- side management, time-of-use tariff design, distribution network planning, and short-term load forecasting are discussed for each cluster and comparatively across the full typology.
This study examines why renewable energy projects in Latvia face local resistance despite generally high public support for decarbonisation. A mixed-methods approach was applied, combining regulatory and planning system analysis, comparative review of selected EU countries, a national survey (n > 1000), and qualitative stakeholder and focus group insights. The results show clear differences in technology acceptance: solar energy demonstrates high approval across all cases, while energy storage and cogeneration show medium-to-high acceptance. In contrast, wind energy exhibits the lowest and most polarized acceptance levels. Survey and qualitative findings indicate that resistance is not driven by rejection of climate goals, but by three main factors: low trust in institutions, weak procedural justice in planning processes, and widespread misinformation. Empirical evidence reveals that public consultations are often ineffective, characterised by one-directional communication, a lack of facilitation, and a lack of feedback loops. Pilot interventions reveal that structured engagement formats can reduce conflict intensity (from high to medium), improve information clarity (from low to medium-high), and slightly increase trust levels. The study concludes that renewable energy conflicts in Latvia are primarily governance- related rather than technological. Improving procedural fairness, transparency, and communication through "myths versus facts" tools and local benefit assessment can significantly enhance public acceptance and support a more socially sustainable energy transition.
Mechanoluminescence can be used as a contact-free visualisation of deformation, but particle integration in complex geometries is challenging. The paper demonstrates a proof-of-concept approach, in which plasma electrolytic oxidation (PEO) is used to create a porous alumina layer capable of hosting SrAl2O4: Eu, Dy mechanoluminescent particles on aluminium substrates. A 15-minute PEO treatment produced an optimal microstructure for particle retention and mechanical stability. While embedded particles showed limited mechanoluminescence due to outward pore expansion during bending, applying a thin epoxy glaze enabled strong and repeatable luminescent responses during both loading and unloading. The mechanoluminescence contrast ratio remained stable across long delay intervals, indicating robustness against afterglow decay. CMOS imaging confirmed the feasibility of spatial strain mapping, though surface inhomogeneity currently limits resolution. These results establish PEO-based particle integration as a promising route toward wire-free, full-field stress visualisation in metal components and highlight key microstructural factors for future optimisation.
The use of traditional well logs to predict the permeability of heterogeneous carbonate reservoirs remains a challenge because of complicated connectivity of pore networks and nonlinear petrophysical processes. Although many machine learning processes are suggested, many studies use small validation processes and one-model evaluations, which are bound to give encouraging results. The article reports a statistically transparent and benchmarked machine learning process of making predictions of permeability on a single dataset of 130 matched core-log measurements of the Mishrif carbonate reservoir in southern Iraq. To represent the variability of the permeability on a multi-order-of-magnitude scale and stabilise the regression behaviour, the permeability was scaled on log scale (log10(k)). As opposed to the preceding studies, a systematic comparison of four machine learning models, i.e., Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Regressor (GBR) was conducted under identical conditions of 5-fold cross-validation to warrant that there was no bias in the analysis of generalisation. The findings indicate how powerful ensemble tree-based methods are whereby the optimum predictive performance (R2 = 0.92) was implemented using the Random Forest, then Gradient Boosting (R2 = 0.90), ANN (R2 = 0.88), and SVR (R2 = 0.85). In addition to predictive accuracy, the paper incorporates feature importance analysis, nonlinear sensitivity analysis, and two-dimensional interaction surface to obtain physical understanding of how porosity and shale volume interact with each other to affect the permeability behaviour. The results show that the increase in permeability as a result of the increase in porosity can be neutralized to some extent by shale-associated flow limiting factors and this suggests the importance of modelling nonlinear processes in carbonate systems. The suggested framework offers a sensible and reproducible direction of log-based permeability estimation and a goal benchmarking criterion of machine learning applications in heterogeneous reservoirs.
The study presents a comparative analysis of solar radiation data from ground-based measurements conducted at the Botanical Garden in Riga. The measurements, recorded at one-minute intervals, were compared with corresponding datasets from the Copernicus Atmosphere Monitoring Service (CAMS) Radiation Service, the DWD-ICON numerical weather prediction model, and the ERA5 reanalysis. The high temporal resolution of the ground-based data enables the detection of short-term fluctuations caused by transient cloud events, which are often smoothed out in model or reanalysis datasets with coarser (15-minute to hourly) resolution. Results show that CAMS generally reproduces the seasonal and diurnal patterns of global horizontal irradiance (GHI) with good accuracy but tends to overestimate peak radiation values, while ERA5 underestimates GHI under clear-sky summer conditions. To improve comparability, a LIDAR-based shading correction was applied using local obstruction data (vegetation and buildings), which increased the overall accuracy index up to 84 %. The findings highlight the importance of integrating high-resolution ground-based measurements with reanalysis and model outputs for solar energy applications in urban environments, where local shading and fine-scale atmospheric dynamics play a critical role.
The integration of artificial intelligence (AI) in education is transforming traditional teaching paradigms, opening up new possibilities for personalised, effective, and accessible learning; meanwhile, the discussion about ethical usage and bias is still growing. This study uses a knowledge mapping approach to explore the research landscape of AI in education by systematically analysing key research topics, trends, and knowledge gaps. It also discusses and explores the emerging debates on the use of AI as a "humanizer" and its impact on academic integrity. A bibliometric analysis was conducted on Scopus-indexed publications from 1976 to 2026. Network visualisation methods implemented in VOSviewer were used to examine publication growth, patterns of international collaboration, most influential studies, keyword co-occurrence patterns, and bibliographic relationships at the journal level. The results demonstrate the evolutionary nature of artificial intelligence applications all over the world from the perspectives of most cited works, collaborations among countries etc., the interdisciplinary collaborations that drive innovations, and the ethical issues associated with its implementation. This study provides a comprehensive overview of the current state of research highlighting the main trends, like countries with the most contribution in AI research in education are the USA and China, the biggest rise of AI research was seen from 2020 and 2024. However, despite the fact that AI is strongly influencing education processes, the study also discusses ethical aspects, like what should be done (and what can be at all), if a text is humanised?
The paper presents an extended comparative thermal analysis of slotless and slotted permanent magnet synchronous motors (PMSMs). To position the investigated motor topologies within modern drive system development, a brief classification-based background on slotless motors is included. Analytical lumped-parameter thermal models are derived to describe copper loss generation, temperature-dependent resistance, volumetric heat source implementation, and stator-to-ambient heat transfer. Three-dimensional thermal simulations performed in Autodesk Fusion are used to compare temperature fields and thermal gradients for both motor constructions under identical operating conditions. The results show that slotless PMSMs reach lower peak winding temperatures and exhibit a more uniform temperature distribution, primarily due to the absence of stator teeth and reduced internal thermal resistance. These findings support the application of slotless PMSMs in high-efficiency and high-reliability drive systems.
The study investigates the optimisation of Balance of Plant (BOP) subsystems in Proton Exchange Membrane (PEM) electrolyser systems for green hydrogen production. The research highlights that BOP is not a secondary support unit, but a core contributor to overall efficiency, reliability, safety, and cost performance. Literature-based evidence indicates that 40-60 % of the total installed PEM electrolyser CAPEX is linked to BOP components rather than the stack itself. BOP inefficiencies can reduce net system efficiency by up to 10 percentage points, suggesting that high stack performance may still lead to weak plant-level results. Key BOP subsystems assessed include water purification, thermal management, power electronics, gas handling and purification, compression and storage, and control/safety systems. PEM electrolysis requires ultrapure water with conductivity below 0.1 mu S/cm and stable operating temperatures typically between 50 and 80 degrees C. A quantitative case study of a 10 MW PEM plant is used to illustrate subsystem-level impacts on cost and energy consumption. In this case study, BOP energy consumption is approximately 6-10 kWh/kg H-2, with major losses from power electronics (3-4 kWh/kg) and compression/storage (2-4 kWh/kg). Total electricity use is assumed at 55 kWh/kg H-2, including BOP losses. With 6,000 operating hours/year, the plant produces about 1.09 million kg H-2/year (approximate to 1,091 t/year). Under a base electricity price of 50 EUR/MWh, annual electricity cost reaches similar to 3.0 million EUR/year. Total CAPEX is assumed at 900 EUR/kW, resulting in 9.0 million EUR investment for the 10 MW plant. Using a 20-year lifetime and 8 % discount rate, the calculated LCOH is approximately 3.8 EUR/kg H-2. The results confirm that BOP optimisation is essential for reducing parasitic losses, improving net efficiency, and lowering the levelized cost of hydrogen.
This study examines the impact of economic growth, CO2 emissions, trade openness, and financial development on renewable energy consumption from 1998 to 2020 in 30 Asian countries, divided into two groups of low institutional quality and high institutional quality. The research methods include two-step Generalized Method of Moments, Panel-Corrected Standard Errors, and Feasible Generalized Least Square estimation methods for linear empirical investigation and the dynamic panel threshold model for non-linear investigation. The results show that there is a positive relationship between economic growth and institutional quality with renewable energy consumption. Specifically, countries with high economic growth, financial development, and institutional quality will have higher renewable energy consumption. In contrast, countries with low institutional quality that increase CO2 emissions tend to hinder renewable energy consumption. Meanwhile, trade openness has no significant impact on renewable energy consumption regardless of the institutional quality of the country.
The study presents a physics-aware simulation framework developed in Python to emulate Internet of Things (IoT)-based monitoring systems for electronic laboratory environments. The framework generates synthetic sensor data for temperature, humidity, and light intensity, representing environmental parameters that often influence the performance, stability, and safety of laboratory electronics and optoelectronic components. By implementing programmable threshold logic and real-time alert dispatch through RESTful APIs, the system enables automated detection of parameter exceedances and notification delivery without the need for physical hardware. The simulation supports both batch and real-time modes, allowing flexible emulation of thermal fluctuations, ambient light shifts, and humidity drift in controlled settings. Application scenarios include overheating prevention in laser-driven devices, monitoring of ambient light in optical benches, and detecting environmental drift in sensitive experiments such as photoemission or cryogenic setups. The results demonstrate reliable threshold crossing detection with response times under 2 seconds and support multivariate data visualisation to identify compound stress conditions. This framework is particularly suited for early-stage prototyping, instructional use, and safety validation in physics-oriented laboratories. By combining domain-specific sensor modelling with IoT-based control logic, the proposed method offers a low-cost and extensible backend for responsive laboratory monitoring and smart alert systems.
The forecasting of high-fidelity, transient fluid flows remains a formidable computational challenge due to the high-dimensional and non-linear nature of the data produced by numerical simulations. The study presents a novel hybrid deep-learning framework to address this challenge by combining a Variational Autoencoder (VAE) with a Long Short-Term Memory (LSTM) network for the efficient spatiotemporal prediction of flow fields around a NACA 0012 airfoil. The VAE first learns a compressed, low-dimensional latent representation of the high-dimensional flow data, which includes pressure and velocity components, achieving effective dimensionality reduction while preserving essential spatial features. An LSTM network then learns the temporal dynamics within this latent space to forecast future states. The model was trained and validated on a dataset of 300 time-step snapshots, successfully generating a 15-step-ahead forecast. Results demonstrate the model's high accuracy, with a key metric - the maximum velocity magnitude - predicted with only a 3.8 % error compared to the ground truth simulation data. This study validates the VAE-LSTM architecture as a powerful and computationally efficient tool for forecasting complex fluid dynamics, offering significant potential for applications in real-time control and design optimisation where rapid prediction is critical.
Research in electric discharge and industrial applications led to the requirement of advanced engineering materials that can withstand various temperatures without significantly affecting their properties. This paper aims to analyse the behaviour of electric discharge in wire-to-plane electrostatic precipitators as a function of temperature. In wire-to-plane electrostatic precipitators at varying temperatures, the physical parameters, including the current-voltage characteristic, onset voltage, corona conductance, electric field, and current density distributions of the discharge DC corona, were experimentally examined, with the corona discharge serving as the ionization source. This project involved the design and construction of an experimental cell to regulate its internal temperature. The current density and electric field were ascertained using the Tassicker model and a statistical analysis with the regression model validated the current-voltage characteristics. The increase in temperature correlated with a rise in the corona current at the collector plate, while the onset voltage diminished. The applied voltage and temperature significantly influenced the properties of corona current density and the electric field. The temperature for a given atmospheric condition strongly affected the discharge current, according to the experimental results. Changing the values of temperature not only changed the property of the gas but also affected the corona onset conditions and ion mobility.
The fracture and pull-out behaviour of vertically aligned basalt composite fibres embedded in an oil shale ash (OSA)-based cementitious matrix was investigated using the double cantilever beam (DCB) test. OSA replaced cement at 0 %, 10 %, 15 %, and 35 % to reduce carbon emissions and improve the mechanical properties of fibre-reinforced concrete. The basalt fibres were oriented vertically, perpendicular to the fracture plane, and aligned with the loading direction to facilitate accurate assessment of the pull-out mechanisms during crack initiation and propagation. The DCB test involved two notched concrete beams joined by a thin fibre-reinforced layer, which enabled controlled crack opening. Specimens with varying OSA content were evaluated for peak load, fracture energy, interfacial bond strength, and fibre pull-out. The results indicated that vertical fibre alignment enhanced load transfer and inter-facial resistance, resulting in higher pull-out forces and improved crack-bridging compared to random fibre placement. Incorporating a moderate amount of OSA improved fracture performance by strengthening the matrix-fibre interface and promoting more ductile failure. Specifically, 10-15 % OSA produced notable improvements in fracture resistance and fibre-matrix bonding, shifting the failure mode from brittle, matrix-dominated to a more ductile, pull-out-controlled process. On the contrary, 35 % of OSA reduced the strength of the interfacial bond due to matrix dilution. Force-displacement curves demonstrated that optimally modified mixtures dissipated more energy and delayed crack propagation. Post-test examination of fibres and force-displacement data confirmed a transition from brittle fracture to gradual pull-out, primarily attributed to enhanced fibre-matrix adhesion. In general, OSA-modified matrices with vertically aligned basalt fibres demonstrated significant potential for developing durable, high-strength, and crack-resistant cementitious composites.
The review provides a short summary of the production of turquoise hydrogen using catalytic methane pyrolysis, focusing on transition metal (Fe, Ni, Co) and carbon-based catalysts. Methane pyrolysis enables the production of CO2-free hydrogen, with solid carbon serving as a valuable by-product. Fe, Ni, and Co catalysts are highlighted due to their strong C-H activation capacity, high activity, and economic feasibility. In turn, carbon-based catalysts offer high thermal stability and resistance to coking, providing a longer catalyst lifetime. Other materials, such as noble metals or complex oxides, are less considered due to the cost, limited scalability, or lower selectivity in solid carbon formation. Focusing on combined cocatalysts and carbon systems provides a balance between catalytic performance, durability, and economic viability, which is the most practical direction for promoting sustainable turquoise hydrogen production.
The study presents an industrial-scale evaluation of two PEM electrolyser stacks incorporating Naco-engineered titanium coatings on porous transport layers (PTLs) and bipolar plates. Stack 0316/44 included both coated PTLs and coated bipolar plates, while Stack 0317/44 utilised only coated PTLs. Both stacks were conditioned for 40 hours at 15 bar and subsequently tested at current densities of 0.6 and 1.2 A cm-2. At 150 A, the total voltages measured were 89.8 V for 0316/44 and 88.9 V for 0317/44, whereas at 300 A, they reached 99.3 V and 102.8 V, respectively. Gas purity remained within safe limits, with residual oxygen (O2) in hydrogen (H2) ranging from 14 ppm to 31 ppm and H2 crossover remaining below 0.7 % across all tests. Integrated operation in the SIRIO 1000 system at 300 A produced the combined voltages of 106.0 V and 104.0 V for the two stacks. Average power consumption during joint operation was 5.75 kW/Nm(3), approximately 10-20 % higher than that of standard industrial stacks. The results confirm the stable operation and acceptable gas-separation performance of the coated components under high-pressure, high-current conditions, while also identifying efficiency gaps that require further optimisation. The study provides the first comparative industrial assessment of the coated PEM stack architectures and establishes a foundation for future durability and optimisation studies.
In line with the objective endorsed by the Council of the European Union, a climate-neutral European Union must be achieved by 2050. This means significantly reducing greenhouse gas emissions in the EU by 2050 and finding ways to offset residual and unavoidable emissions. Energy communities, in particular, are seen as citizen-led energy actions that will help pave the way for a clean energy transition. Along with the European Union's objective of achieving climate neutrality by 2050, the involvement of citizens in the energy transition and the reduction of greenhouse gas emissions is of paramount importance. One way to facilitate the transition is through the creation of energy communities: citizen-led initiatives that promote the use of renewable energy sources and local energy production. However, the success of these initiatives depends not only on regulatory and technological conditions, but also on citizens' willingness to cooperate and act collectively. The paper explores what would motivate Latvian residents to participate in energy communities and assesses their potential for engagement, with a particular focus on multi-apartment buildings and their potential role in energy communities. Drawing on a theoretical literature review and survey data, the study assesses the awareness, motivation, and barriers to the development of energy communities among the public and building managers. It provides an in-depth analysis of residents' attitudes and their potential for participation in Latvia - a country where the concept of energy communities is still relatively new and largely unexplored. The results of the study show that residents prefer economic benefits over the benefits of the local community or the environment, and also show the small experience of the population in collective action. These findings of the study contribute to a broader understanding of the social aspects of the European energy transition in the case of Latvia and are useful for both policy makers and local governments, which should take the lead in organising and coordinating the development of energy communities.
