Tallinn University

This article investigates the propagation of a deadly human disease, namely, leprosy. To this end, an integer-order system of differential equations is considered. At the outset, the mathematical model is transformed into a fractional-order model by introducing the Caputo differential operator of arbitrary order. A result is established which ensures the positivity of the fractional-order epidemic model. Equilibrium states for the model are derived in order to investigate the stability of the continuous model at these particular points. The basic reproduction number, R0 , is obtained for the leprosy model using the next-generation matrix approach. The classical Jacobian theory and the Routh–Hurwitz criteria are applied to ensure that the fractional leprosy system is locally asymptotically stable at both steady states when R0 < 1. On the other hand, a comparison theorem and the Lypunov function technique are used to prove that the fractional-order system is globally asymptotically stable when R0 > 1. To find the approximate solutions for the continuous epidemic model, a non-standard numerical scheme is constructed. The main features of the non-standard scheme (such as positivity and boundedness of the numerical method) are also confirmed by applying some benchmark results. Simulations and a feasible test example are presented to discern the properties of the numerical method proposed. Our computational results confirm both the analytical and numerical properties of the finite-difference scheme.

The ageing global population puts heavy pressure on healthcare systems everywhere. Addressing ageing-related chronic conditions requires employment of novel innovative solutions. Telehealth technologies, including telepresence robots (TPRs), are being rapidly developed to provide healthcare services efficiently wherever needed. This article explores the role of TPRs in addressing the challenges of providing healthcare to an ageing population, emphasizing their potential advantages and drawbacks. Employing an exploratory research approach with qualitative data collection techniques, we tested three TPR usage scenarios in simulated healthcare settings: anamnesis, measurements, and falls and frailty. The study employed a non-random purposive sample comprising 25 participants, and was conducted at a medical facility in June 2023. The findings suggest that TPRs offer promising solutions for healthcare professionals and patients, especially in scenarios when physical presence is impossible or physical isolation is required to prevent contagion. However, the technology is not yet ready to substitute fully human medical workers, potentially causing patient reluctance and emphasizing the need for patient-centered approaches to technology adoption. In addition, more studies are needed to address ethical, privacy, and scalability concerns.

Several academic and budgetary applications in robotics use low-cost encoders which usually present errors inherent to the fabrication of their components or the surrounding environment. However, the data gathered from these sensors could be used successfully if the estimations based on the data are be compensated. This note presents an efficient method to compensate the error of estimation for the distance traveled by robotic four-wheeled vehicles with a speed control based on pulse width modulation. The current approach uses a methodology based on artificial neural networks which compensate the estimation error and enables a way to model the error for a further method of position estimation. Precisely, the model proposed in this work is based on a 1 Springer Nature 2021 L A T E X template 2 ANN-based positioning error neural network with one hidden layer and five nodes. Our approach uses the information from the pulse width modulation value to control the speed of the vehicle as well as the values for the tick counting of the encoders in the four wheels. The back-propagation algorithm was used to calculate the weights of the nodes in the neural network. This method showed an improvement in the results with an error comparable to the intrinsic error due to the precision of the sensor, and gives an error model based on a zero-mean normal distribution.

In this work, a master-slave system composed by a pair of damped Duffing oscillators with variable coefficients and nonlinear coupling is investigated. An integral of motion for the system is obtained {using a symmetry transformation and Noether's theorem.} Some numerical examples are presented for different cases of damping and oscillation frequency, for a varying coupling constant. The system dynamics is studied by means of space-time surfaces, time series and phase portraits. For a constant oscillation frequency, the slave presents envelopes that tend to become chaotic as the coupling constant increases. Meanwhile, as the frequency increases with time, the slave has higher amplitudes and speeds than the master oscillator.

There is a lack of research on the motivations of fosterers in countries that have traditionally used residential childcare institutions and are now shifting their policies towards family-based foster care. Understanding fosterers’ motives is important for developing an effective and long-term foster care system. The aim of this article is to understand the motivations of fosterers in a society that is undergoing deinstitutionalization process. Semi-structured interviews were conducted with 16 active fosterers in Estonia (11 fosterers were from the Estonian majority and 5 from the Russian-speaking minority). The results show that new fosterers enter the foster care system with the desire to create a family and achieve a permanent arrangement. More experienced fosterers, who have fostered children for years, have done so largely because of the poor conditions in residential childcare institutions in the past. Although the foster care system has undergone significant positive changes, the improvements have not been enough to remove or lessen potential fosterers’ fears about the permanence and predictability of their fostering experience. These fears may lead to a foster care system that is inclined towards an arrangement similar to adoption where people prefer long-term care with fewer and younger children, leaving children who do not correspond to these criteria to remain in residential childcare institutions.

This study investigated the development of study engagement from the end of upper secondary school through the first and second years of higher education. The participants experienced the challenges related to the first wave of the COVID-19 pandemic while they were either university students or preparing for university entrance exams. The study employed a person-oriented approach to determine what kind of developmental trajectories emerge in study engagement when following the student participants from the end of upper secondary school through the first and second years of higher education. Furthermore, the study investigated whether socio-emotional skills obtained by the end of secondary school and before the pandemic play a role in more adaptive development through demanding restrictions related to the worldwide pandemic and general changes in the learning environment. The 852 participants answered questionnaires on study engagement and socio-emotional skills in spring 2019, and of them, 426 individuals who continued their education answered regarding study engagement again in spring 2020 and winter 2020/2021. The grades in math and Finnish language were also included. The results indicated that most students tended to experience a drop in study engagement during the first wave of COVID-19 compared to the pre-pandemic level; however, they managed to boost their engagement back to previous levels approximately 6 months later. Students who managed to recover their engagement also tended to have higher socio-emotional skills than students who were struggling with study engagement before the pandemic or who started to struggle during the pandemic.

Background The career decisions of medical students are pivotal in shaping the future healthcare workforce. In many countries, the number of medical students who choose general practice (GP) as their career is insufficient to meet the needs of the healthcare system. Aim The aim of this study was to describe the factors influencing medical students’ career intentions and their preference for a career in GP. Design & setting A cross-sectional study involving medical students from Flanders (Belgium), Estonia and Hungary. Method An online questionnaire was used to gather data. Multivariable logistic regression was conducted. Results Altogether 1601 medical students participated in this study. 18.5% of the participants were interested in GP. Factors related to medical students and the curriculum which predicted the interest in GP were being a woman, being a medical student from Flanders, being a 6th year medical student, coming from a rural area and having GP role models. Students preferring GP named the following factors as important: short and low intensity training program, having long-term and close relationship with patients, continuity of care, regular and flexible working hours and opportunities to achieve work-life balance. Conclusion This study adds further evidence which characteristics and factors can predict medical students’ interest in GP, having GP role models being the most important predictor. Further research into which qualities medical students value in their role models could give us better understanding on how we can support GPs to be better advocates for their specialty and thereby help increase interest in GP.

Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D Modeling" e-course on the Moodle platform at TTK University of Applied Sciences, Tallinn, Estonia. By predicting trends, identifying patterns, and optimizing resource allocation, machine learning can improve the efficiency of e-learning and provide students with tailored recommendations for acquiring relevant knowledge and skills. The results of the study show that machine learning algorithms can be used to process the available e-course log data, using the clickstream of e-course resources and for their automated processing. The results suggest potential applications in personalized course recommendations, prediction and dropout prevention strategies, resulting in a more effective and personalized educational experience. Future research will focus on improving models of available registration data, exploring and using advanced machine learning techniques to improve the accuracy and usefulness of predictions, and providing faster recommendations to help students navigate their studies more effectively.

Energy can be seen as an important mediator of relations between humans and natural environments. This chapter focuses on the spatialities of shifting oil shale-dependent energy regimes that bring together territorialisation dynamics and anticipated landscapes in rescaling processes. This study analyses the encounters and narratives within the two main spheres of energy transition in Estonia—negotiating fossil fuel dependencies and territorialising offshore wind resources. By examining these carbon-lowering spheres together, we connect rescaling processes simultaneously to legacies and to emergent characters of energy landscapes. The shift towards more space-dependent energy production is discussed through three sets of relational processes: politicisation of the existing regime; ways of engagements and knowledge exchange; hybridisation along co-constituting of human and non-human agencies.

Climatic oscillations are considered primary factors influencing the distribution of various life forms on Earth. Large species adapted to cold climates are particularly vulnerable to extinction due to climate changes. In our study, we investigated whether temperature increase since the Late Pleistocene and the contraction of environmental niche during the Holocene were the main factors contributing to the decreasing range of moose (Alces alces) in Europe. We also examined whether there were significant differences in environmental conditions between areas inhabited by moose in Europe and Asia, that could support the division of moose into western and eastern forms, as suggested by genetic and morphological data. We analysed environmental conditions in the locations of 655 subfossil and modern moose occurrences over the past 50,000 years in Eurasia. We found that the most limiting climatic factor for the moose distribution since the Late Pleistocene was July temperature. More than 90 % of moose records were found in areas where mean summer temperature was below 19 °C, with July temperatures showing over 3 times narrower interquartile range compared to January temperatures. We identified significant differences in environmental conditions between areas inhabited by the European and Asiatic moose. In Europe, the species occurred in regions with milder climates, higher primary productivity, and more frequently within forest biomes compared to Asiatic individuals. The moose range shifted more in the west-east than in the south-north direction during the Holocene climate warming in Europe. We conclude that although the area of suitable moose habitat has increased since 12–8 ka years BP, as demonstrated by environmental niche modeling, the retreat of A. alces in large areas of Europe was likely caused by anthropogenic landscape change (e.g., deforestation) and overhunting by humans during the late Holocene rather than by climate warming during the Pleistocene to Holocene transition.

Governance in the blockchain context revolves around mechanisms that allow decentralized systems to evolve in time. The paper proposes a conceptual goal model for a token economy governance structure (TEGS). This was accomplished by completing a narrative review of existing literature from various academic databases. The findings of the review result in a seven-step TEGS goal model that includes: defining the governance areas and stakeholders, defining the level of decentralization, defining on-chain or off-chain mechanisms and desired voting mechanism properties, defining core voting mechanisms and support mechanisms for voting. Future research will incorporate technical specifications about best code-writing and quantitative parameters for voting mechanisms.

Modification of mRNA by methylation is involved in post-transcriptional regulation of gene expression by affecting the splicing, transport, stability and translation of mRNA. Methylation of adenosine at N6 (m6A) is one of the most common and important cellular modification occurring in the mRNA of eukaryotes. Evidence that m6A mRNA methylation is involved in regulation of stress response and that its dysregulation may contribute to the pathogenesis of neuropsychiatric disorders is accumulating. We have examined the acute and subchronic (up to 18 days once per day intraperitoneally) effect of the first METTL3/METTL14 activator compound CHMA1004 (methyl-piperazine-2-carboxylate) at two doses (1 and 5 mg/kg) in male and female rats. CHMA1004 had a locomotor activating and anxiolytic-like profile in open field and elevated zero-maze tests. In female rats sucrose consumption and swimming in Porsolt’s test were increased. Nevertheless, CHMA1004 did not exhibit strong psychostimulant-like properties: CHMA1004 had no effect on 50-kHz ultrasonic vocalizations except that it reduced the baseline difference between male and female animals, and acute drug treatment had no effect on extracellular dopamine levels in striatum. Subchronic CHMA1004 altered ex vivo catecholamine levels in several brain regions. RNA sequencing of female rat striata after subchronic CHMA1004 treatment revealed changes in the expression of a number of genes linked to dopamine neuron viability, neurodegeneration, depression, anxiety and stress response. Conclusively, the first-in-class METTL3/METTL14 activator compound CHMA1004 increased locomotor activity and elicited anxiolytic-like effects after systemic administration, demonstrating that pharmacological activation of RNA m6A methylation has potential for neuropsychiatric drug development.

This study aims to reconstruct the changes in storminess during the past 7600 years in the northeastern Baltic Sea region. For storminess reconstructions, aeolian sand influx (ASI) in coastal peat bog deposits was applied as an indicator of the past storminess. We analyzed cores from four peat bogs in the western and northern coastal areas of Estonia: the cores from Hiiumaa N, Hiiumaa SW, Saaremaa and Juminda study sites covered the past 3700, 3750, 2400, and 8400 years, respectively. The sediment chronologies were established using 36 14C dates. Image-analysis method (ImageJ) was used to count and measure the grain size of all sand particles at every centimeter to gain high-resolution ASI records. Although minor site-to-site variations exist, all four ASI records were in general consistent, suggesting that stormier periods occurred around 7300, 6600, 5900, 4600, 3600, 2900, 2400, 2100, and 1600 cal yr BP and over the last 1200 years. The results and comparisons with other storminess and climate studies indicate a shift in climatic conditions around 2500 cal yr BP when stormy periods became more frequent. The ASI values were also high during the last millennium, suggesting either higher storminess or more suitable transport mechanisms for sand into the coastal bogs: the niveo-aeolian transport mode could have been favored in winters, especially during the Little Ice Age, and human impact on landscapes has probably increased over the past centuries.

Blockchain technologies enable transparent and trusting inter-organizational business collaborations in many domains, including also real-world assets (RWA). Consequently, decentralized application (DApp) platforms emerge that record collaboration-relevant data in an immutably traceable way. Such DApps comprise smart contracts that automate the enforcement of defined business protocols and conditions in an environment without reliance on trusted centralized third parties. Accurate RWA identification must include the correlated parties together with their respective roles in public network inter-organizational collaborations (IoC) involving blockchain technologies. Still, enterprises tend to prefer permissioned networks for DApps due to privacy concerns and challenges related to user management. RWA related organizations and involved roles are exposed to contextual changes that demand secure, adaptable, and versatile authentication means before involving them in blockchain-enabled IoC processes. Given these factors, the paper explores the combination of the Logistics BDT DApp for RWA tracking together with the multifactor challenge set self-sovereign identity authentication (MFSSIA) for effective counterfeit circulation prevention. This combination comprises non-fungible token (NFT)-based RWA identity authentication as a running case. This running case involves a set of configurable challenges and integrates them into the existing DApp combination to manage trustable IoC processes.