Tallinn University of Technology
  • Tallinn, Harjumaa, Estonia
Recent publications
The modern electric power transmission system is a geographically extensive network which can span hundreds of kilometres, crossing harsh terrain and making manual inspection of its components costly. This paper proposes a novel condition assessment methodology for transmission overhead lines that is significantly more cost effective compared to traditional foot-patrol visual inspections. The proposed methodology utilizes a multi-stage ensemble deep learning network to automatically classify tower conditions based on high resolution aerial images. While aerial inspections allow relatively quick inspection of transmission routes, they are not usually used for condition assessment due to the high capturing altitude of images which would require time consuming manual processing to identify defects on hundreds to thousands of images. The proposed methodology automatically isolates transmission poles, disaggregates components, detects defects and determines the health index of concrete structures and insulators. The method involves pushing images through layers of automatic detection, region of interest (RoI) extraction and patching. A number of recent object detectors were tested on real-world data to evaluate their performance and an ensemble model is composed to improve the reliability of the detection. Results indicate that the multi-layer approach with output-based ensemble modelling, can effectively detect critical defects, although incipient fault conditions remain uncertain.
The protein wolframin is localized in the membrane of the endoplasmic reticulum (ER), influencing Ca2+ metabolism and ER interaction with mitochondria, but the exact role of the protein remains unclear. Mutations in Wfs1 gene cause autosomal recessive disorder Wolfram syndrome (WS). The first symptom of the WS is diabetes mellitus, so accurate diagnosis of the disease as WS is often delayed. In this study we aimed to characterize the role of the Wfs1 deficiency on bioenergetics of muscles. Alterations in the bioenergetic profiles of Wfs1-exon-5-knock-out (Wfs1KO) male rats in comparison with their wild-type male littermates were investigated using high-resolution respirometry, and enzyme activity measurements. The changes were followed in oxidative (cardiac and soleus) and glycolytic (rectus femoris and gastrocnemius) muscles. There were substrate-dependent alterations in the oxygen consumption rate in Wfs1KO rat muscles. In soleus muscle, decrease in respiration rate was significant in all the followed pathways. The relatively small alterations in muscle during development of WS, such as increased mitochondrial content and/or increase in the OxPhos-related enzymatic activity could be an adaptive response to changes in the metabolic environment. The significant decrease in the OxPhos capacity is substrate dependent indicating metabolic inflexibility when multiple substrates are available.
Working within the theoretical framework set by the Technology Acceptance Model (TAM) literature, this paper clarifies how psychological factors (emotions, attitudes, beliefs, and information-seeking) affect skill development in the context of smart farming technologies. Interviews with multiple stakeholders from the agriculture sectors of three European countries (Belgium, Italy, and the United Kingdom) were used to develop a new conceptual model that attempts to generalize the complex interplay existing between skills and psychological factors in the context of smart technology adoption. This conceptualization provides a systematic view of the correlation between skills and psychological factors, complements the TAM by introducing the new concept of attitude to learning, and clarifies how the interplay between cognitive and emotional components influences the decisions to adopt and use smart technologies. In addition to these theoretical contributions, the paper emphasizes the importance of designing policy initiatives that tackle both cognitive and emotional barriers to the adoption of smart technologies, urging decision makers to move away from the simplistic assumption that increasing the digital skills of potential users automatically leads to growth in the adoption and implementation of smart technologies.
Augmented reality (AR) and virtual reality (VR), collectively referred to as “extended reality” (XR), have begun to diffuse in industry. However, the current levels of awareness, perceived limitations, and use of AR and VR, as well as the potential differences on these aspects between these technologies are still not well known. Moreover, it is unknown whether small and medium-sized enterprises (SMEs) differ from large companies on these issues. This research employed a mixed methods research design to address this gap by carrying out a cross-sectional survey (n = 208) to gauge European industrial companies’ level of AR and VR awareness and adoption, and by interviewing 45 companies in nine European countries in order to identify critical enabling factors in the adoption of XR for SMEs. Results show no statistical difference between the respondents’ perceptions toward AR and VR or in their use levels. Thus, examining AR and VR under the umbrella term XR seems justified, especially in the context of their organizational use. However, larger companies were found to be using XR more than SMEs. Analysis of interviews based on the technology–organization–environment framework also yielded several enabling factors affecting XR adoption and specified whether they are particularly highlighted in the SME context. Overall, this paper contributes to XR research by providing a holistic multi-country overview that highlights key issues for managers aiming to invest in these technologies, as well as critical organizational perspectives to be considered by scholars.
The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.
For determining precise sea surface heights, six marine GNSS (global navigation satellite system) survey campaigns were performed in the eastern Baltic Sea in 2021. Four GNSS antennas were installed on the vessel, the coordinates of which were computed relative to GNSS–CORS (continuously operating reference stations). The GNSS–CORS results are compared to the PPP (precise point positioning)-based results. Better accuracy is associated with the GNSS–CORS postprocessed points; however, the PPP approach provided more accurate results for longer than 40 km baselines. For instance, the a priori vertical accuracy of the PPP solution is, on average, 0.050 ± 0.006 m and more stable along the entire vessel’s survey route. Conversely, the accuracy of CORS-based solutions decreases significantly when the distances from the GNSS–CORS exceed 40 km, whereas the standard deviation between the CORS and PPP-based solutions is up to 0.075 m in these sections. Note that in the harbor (about 4 km from the nearest GNSS–CORS), the standard deviation of vertical differences between the two solutions remains between 0.013 and 0.024 m. In addition, the GNSS antennas situated in different positions on the vessel indicated different measurement accuracies. It is suggested for further studies that at least one GNSS antenna should be mounted above the mass center of the vessel to reduce the effects of the dominating pitch motion during the surveys.
This paper proposes a deep Reinforcement Learning (RL) based co-design approach for joint-optimization of wireless networked control systems (WNCS) where the co-design approach can help achieve optimal control performance under network uncertainties e.g. delay and variable throughput. Compared to traditional and modern control methods where the dynamics of the system are important for predicting a system's future response, a model-free approach can adapt to many applications of stochastic behaviour. Our work provides a comparison of how the control performance is affected by network uncertainties such as delays and bandwidth consumption under an unknown number of devices. The control data is transmitted under different network conditions where several applications transmit background traffic data using the same network. The problem contains several sub-optimization problems because the optimal number of devices is non-deterministic under network delay and channel capacity constraints. The proposed approach seeks to minimize control error in wireless network control systems in order to improve Quality of Service and Quality of Control. This proposed approach is used and compared using three model-free RL Q-learning algorithms for high-throughput flow control in a double emulsion droplets formation application. The results show that the allowable number of devices for reliable network communication under bounded network constraints is 10 when using binary search. The control performance of the system without considering network effect in the reward function (Scenario 1) was good with the C51 algorithm; when including OMNet++ based network effect in the reward function (Scenario 2), the best performance was achieved with all three algorithms (C51, DQN, DDQN) with an exponential reward function, and only with C51 in the case of a linear reward function. Finally, under random network conditions (Scenario 3), C51 and DDQN performed well, but DQN did not converge. Comparisons with other machine learning and non-machine learning algorithms also highlight the superior performance of the utilized algorithms.
This chapter provides a concise overview of the entire monograph by assembling summaries of 10 individual chapters starting with a global review of large-scale, persistent nutrient fronts of the World Ocean followed by regional chapters on the Arctic Ocean, North Atlantic, Baltic Sea, Kuroshio Current, and the Yellow Sea, a global review of CDOM dynamics at fronts, a chapter on persistent organic pollutants and marine organisms in the Kuroshio-Oyashio frontal zone, and two chapters on marine litter and its dynamics in frontal zones.
The propagation of an action potential in nerves is accompanied by mechanical and thermal effects. Several mathematical models explain the deformation of the unmyelinated axon wall (a mechanical wave). In this paper, the deformation of the myelinated axon wall is studied. The mathematical model is inspired by the mechanics of microstructured materials with multiple scales. The model involves a Boussinesq-type equation together with a modification that describes the process in the myelin sheath. The dispersion analysis of such a model explains the behaviour of group and phase velocities. In addition, it is shown how dissipative effects may influence the process. Numerical calculations demonstrate the changes in velocities and wave profiles in the myelinated axon wall.
This paper presents a novel structure of the transformer-less grid-connected inverters. The proposed inverter is combined with six power switches and two power diodes which can generate six voltage levels at the output. Furthermore, the proposed inverter can overcome the leakage current issue in the photovoltaic (PV) system, which is the major problem in grid-tied PV applications. Additional significant features include-reduced filter size, lower total harmonic distortion (THD) of the injected current to the grid, and voltage boosting ability. Moreover, the proposed topology provides full reactive power support to the grid. A control strategy is designed and implemented to provide a voltage boost ability without using any additional dc-dc boost converter. Finally, the performance of the proposed inverter is validated by the 770 W laboratory prototype. INDEX TERMS Transformer-less inverter, voltage boosting ability, leakage current limitation, and photovoltaic application.
The Gulf of Riga is a relatively shallow bay connected to the deeper central Baltic Sea (Baltic Proper) via straits with sills. The decrease in the near-bottom oxygen levels from spring to autumn is a common feature in the gulf, but in 2018, extensive hypoxia was observed. We analyzed temperature, salinity, oxygen, and nutrient data collected in 2018, along with historical data available from environmental databases. Meteorological and hydrological data from the study year were compared with their long-term means and variability. We suggest that pronounced oxygen depletion occurred in 2018 due to a distinct development of vertical stratification. Seasonal stratification developed early and was stronger in spring–summer 2018 than on average due to high heat flux and weak winds. Dominating northeasterly winds in early spring and summer supported the inflow of saltier waters from the Baltic Proper that created an additional deep pycnocline restricting vertical transport between the near-bottom layer (NBL) and the water column above. The estimated oxygen consumption rate in the NBL in spring–summer 2018 was about 1.7 mmolO2m-2h-1, which exceeded the oxygen input to the NBL due to advection and vertical mixing. Such a consumption rate leads to near-bottom hypoxia in all years when vertical mixing in autumn reaches the seabed later than on average according to the long-term (1979–2018) meteorological conditions. The observed increase in phosphate concentrations in the NBL in summer 2018 suggests a significant sediment phosphorus release in hypoxic conditions counteracting the mitigation measures to combat eutrophication. Since climate change projections predict that meteorological conditions comparable to those in 2018 will occur more frequently, extensive hypoxia would be more common in the Gulf of Riga and other coastal basins with similar morphology and human-induced elevated input of nutrients.
MOOC landscape is evolving, also boosted by distance-learning necessity of recent health crises. Logistics is an interdisciplinary area across business processes and functions, engineering, global views and sustainability. As lifelong learning appears a new norm and it is difficult for HEIs to provide a programme with both sufficient focus on foundational skills as well as topical expertise, students and practitioners can turn to MOOCs for complementary instruction. This study presents a data collected from 198 logistics-themed MOOCs across four major platforms (edX, Coursera, FutureLearn and Udemy) to evaluate the topical availability across main areas of direct logistics expertise. Regardless of relative abundance, the study suggests both thematic gaps and criticism of MOOC development priorities. The study allows to argue against feasibility of compiling a full online programme of MOOCs, lack of linkages and of coherent design. Within current paradigm, MOOCs shall remain complementary not a substitute to college programme experience. Keywords: logistics knowledge areas, supply chain competences, MOOCs, future of higher education, lifelong learning.
Dropping out of school is traditionally frowned upon by judging the individual and pointing out supply-side waste – resources have been spent without the intended output of a capable graduate. This paper analyses views of dropouts from a local business administration undergraduate programme in Estonia. The survey and interviews focused on ex-students 2-15 years post-leave to chart a spectrum of dropout causes, resulting impacts and personal reflections. The data suggests the majority of students perceive significant value in their cut-short college experience, while a minority expressed various hard feelings. The paper discusses the extent to which student retention can be increased in the focal case (retention ceiling around 75%) and anticipated improvement actions. The data shows that learning without diploma is still perceived as valuable learning, which fits modern business education paradigm. Therefore the paper argues against viewing graduation rate as the main KPIs in business studies at publicly funded school. Keywords: undergraduate business education, student attrition, college dropout causes, programme development, value of learning.
: Equations of state are powerful tools for modeling thermophysical properties; however, so far, these have not been developed for shale oil due to a lack of experimental data. Recently, new experimental data were published on the properties of Kukersite shale oil, and here we present a method for modeling the properties of the gasoline fraction of shale oil using the PC-SAFT equation of state. First, using measured property data, correlations were developed to estimate the composition of narrow-boiling-range Kukersite shale gasoline samples based on the boiling point and density. These correlations, along with several PC-SAFT equations of the states of various classes of compounds, were used to predict the PC-SAFT parameters of aromatic compounds present in unconventional oil-containing oxygen compounds with average boiling points up to 180 °C. Developed PC-SAFT equations of state were applied to calculate the temperature-dependent properties (vapor pressure and density) of shale gasoline. The root mean square percentage error of the residuals was 13.2%. The average absolute relative deviation percentages for all vapor pressure and density data were 16.9 and 1.6%, respectively. The utility of this model was shown by predicting the vapor pressure of various portions of the shale gasoline. The validity of this model could be assessed for oil fractions from different deposits. However, the procedure used here to model shale oil gasoline could also be used as an example to derive and develop similar models for oil samples with different origins.
Electrospun polymer nanofiber materials have been studied as basic materials for various applications. Depending on the intended use of the fibers, their morphology can be adjusted by changing the technological parameters, the properties of the spinning solutions, and the combinations of composition. The aim of the research was to evaluate the effect of electrode type, spinning parameters, polymer molecular weight, and solution concentration on membranes morphology. The main priority was to obtain the smallest possible fiber diameters and homogeneous electrospun membranes. As a result, five electrode types were selected, the lowest PVA solution concentration for stable spinning process was detected, spinning parameters for homogenous fibers were obtained, and the morphology of electrospun fiber membranes was analyzed. Viscosity, conductivity, pH, and density were evaluated for PVA polymers with five different molecular weights (30–145 kDa) and three concentration solutions (6, 8, and 10 wt.%). The membrane defects and fiber diameters were compared as a function of molecular weight and electrode type. The minimum concentration of PVA in the solution allowed more additives to be added to the solution, resulting in thinner diameters and a higher concentration of the additive in the membranes. The molecular weight, concentration, and electrode significantly affected the fiber diameters and the overall quality of the membrane.
With the exceptional COVID-19 circumstances in early 2020, public service co-production went through a push towards digitalisation. Using normalisation process theory as the basis for analysis, the article looks at the immediate effects of digitalisation on restorative practices, which is a co-produced approach to delivering justice. A comparative case study conducted in Estonia, Finland, Ireland and Portugal showed that digitalisation meant a more directive role for the mediators and more responsibility for the citizens in organising the service context. The process became more business-like, which put some integral aspects of restorative justice at risk, such as trust building and feeling connected. The launch of digital restorative services depended more on service providers' readiness to try digital solutions and less on service experience before digitalisation.
Although Northern Europe has been the target area in many regionwide geoid determination studies, the research has been land-focused, neglecting bathymetry information. With new projects, such as the Baltic Sea Chart Datum 2000, the attention is shifting toward the marine geoid. Hence, consideration for bathymetry has become relevant, the influence of which is studied. In the relatively shallow Baltic Sea, accounting for bathymetry-based residual terrain model reduction during gravity data processing induces marine geoid modeling differences (relative to neglecting bathymetry) mainly within 2 cm. However, the models can deviate up to 3–4 cm in some regions. Rugged Norwegian coastal areas, on the other hand, had modeling improvements around a decimeter. Considering bathymetry may thus help improve geoid modeling outcomes in future Northern Europe geoid determination projects. Besides using the conventional precise GNSS-leveling control points, the paper also demonstrates the usefulness of shipborne GNSS and airborne laser scanning-derived geoidal heights in validating geoid modeling results. A total of 70 gravimetric geoid solutions are presented, for instance, by varying the used reference global geopotential models. According to the comparisons, GOCO05c-based solutions generally perform the best, where modeling agreement with GNSS-leveling control points reached 2.9 cm (standard deviation) from a one-dimensional fit.
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4,181 members
Eve-Ly Ojangu
  • Department of Chemistry and Biotechnology, Division of Gene Technology
Juri Vain
  • Department of Software Science
Prashanth K G
  • Department of Mechanical and Industrial Engineering
Veiko Karu
  • Department of Geology
Sreekanth Mandati
  • Department of Materials and Environmental Technology
Ehitajate tee 5, 19086, Tallinn, Harjumaa, Estonia
Head of institution
Tiit Land