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This paper examines several model based approaches for retrieving surface soil moisture from ALOS-2 PALSAR-2 quad-pol imagery, over a lime stone quarry in southeastern Finland. The study primarily targets physically interpretable semi-empirical modeling approaches, with generic ML modeling used as a benchmark. Along with common polarimetric observables, we propose a generalization of the SAR time series based TU Wien soil moisture index (SMI) retrievals examined across several representational spaces derived from polarimetric coherency matrix $[T3]$. This study was conducted over a closed tailing storage facility and a landfill, with a set of 9 repeat pass PALSAR-2 images. The best semi-empirical configuration combining temporal context SMI and current observation PolSAR parameters achieved $R^2=0.67$ and RMSE $=5.65$ volumetric \% units. The strongest $SMI_{[T3]}$ approach with sediment-specific calibration, achieved $R^2=0.66$ and RMSE $=5.67$ vol. \%, which was considerably better than using $SMI_{HH}$ or $SMI_{VV}$. The proposed approach was sensitive to representations: dB-based projection outperformed linear or trace-normalized $[T3]$ representation. Factoring in sediment inf

A life cycle model of consumption and labor supply describes employment decisions of a collection of individuals during their lifetime. We develop a life cycle model describing a heterogeneous population operating in Finland under a wide variety of employment states and life situations. A rich life cycle model requires a large state space representing the possible states of simulated agents. The results demonstrate that the model reproduces a number of statistics of the Finnish employment market such as the age structures of employment rate and unemployment rate, distributions of observed effective marginal tax rates and participating tax rates, and proportion of part time work. As an application of analysis of a reform, we analyze how the program of Orpo government influences employment and public finances in Finland.

Persistent rural-urban disparities in broadband connectivity remain a major policy challenge, even in digitally advanced countries. This paper examines how these inequalities manifest in northern Finland and Sweden, where sparse populations, long distances, and seasonal variations in demand create persistent gaps in service quality and reliability. Drawing on survey data (n = 148), in-depth interviews, and spatial analysis, the study explores the lived experience of connectivity in Arctic rural communities and introduces a novel Cellular Coverage Inequality (CCI) Index. The index combines measures of rurality and network performance to quantify spatial disparities that are masked by national coverage statistics. Results reveal that headline indicators overstate inclusiveness, while local users report chronic connectivity gaps affecting work, safety, and access to services. Building on these findings, the paper outlines policy reflections in six areas: shared infrastructure and roaming frameworks, spectrum flexibility for rural operators, performance-based Quality-of-Service monitoring, standardized and transparent reporting, temporal and seasonal capacity management, and digital-sk

Considerable efforts have been made at the high school level to encourage girls to pursue software engineering careers and raise awareness about diversity within the field. Similarly, software companies have become more active in diversity and inclusion (D&I) topics, aiming to create more inclusive work environments. However, the way diversity and inclusion are approached inside software engineering university education remains less clear. This study investigates the current state of D&I in software engineering education and faculties in Finland. An online survey (N=30) was conducted among Finnish software engineering university teachers to investigate which approaches and case examples of D&I are most commonly used by software engineering teachers in Finland. In addition, it was researched how software engineering teachers perceive the importance of D&I in their courses. As a result of the quantitative and thematic analysis, a framework to identify attitudes, approaches, challenges and pedagogical strategies when implementing D&I themes in software engineering education is presented. This framework also offers a process for integrating D&I themes for the cu

Real world spatio-temporal datasets, and phenomena related to them, are often challenging to visualise or gain a general overview of. In order to summarise information encompassed in such data, we combine two well known statistical modelling methods. To account for the spatial dimension, we use the intrinsic modification of the conditional autoregression, and incorporate it with the hidden Markov model, allowing the spatial patterns to vary over time. We apply our method to parish register data considering deaths caused by measles in Finland in 1750-1850, and gain novel insight of previously undiscovered infection dynamics. Five distinctive, reoccurring states, describing spatially and temporally differing infection burden and potential routes of spread, are identified. We also find that there is a change in the occurrences of the most typical spatial patterns circa 1812, possibly due to changes in communication networks after major administrative transformations in Finland.

The utilization of health data for secondary purposes, such as research, sta-tistics, and development, has become increasingly significant in advancing healthcare systems. To foster the above, Finland has established a framework for the secondary use of health and social data through legislative measures and the creation of specialized institutions, which are the first of their kind in the world. In this paper, we give an overview of our implementation for using secondary health and social data in a centralized fashion. As a technical contribution, we also address key implementation aspects related to implementing the framework.

Mispronunciation detection (MD) models are the cornerstones of many language learning applications. Unfortunately, most systems are built for English and other major languages, while low-resourced language varieties, such as Finland Swedish (FS), lack such tools. In this paper, we introduce our MD model for FS, trained on 89 hours of first language (L1) speakers' spontaneous speech and tested on 33 minutes of L2 transcribed read-aloud speech. We trained a multilingual wav2vec 2.0 model with entropy regularization, followed by temperature scaling and top-k normalization after the inference to better adapt it for MD. The main novelty of our method lies in its simplicity, requiring minimal L2 data. The process is also language-independent, making it suitable for other low-resource languages. Our proposed algorithm allows us to balance Recall (43.2%) and Precision (29.8%), compared with the baseline model's Recall (77.5%) and Precision (17.6%).

The current fossil fuel and climate crisis has led to an increased demand for renewable energy sources, such as wind power. In northern Europe, the efficient use of wind power is crucial for achieving carbon neutrality. To assess the potential of wind energy for private households in Finland, we have conducted a high spatiotemporal resolution analysis. Our main contribution is a wind power map of Finland that indicates the availability of wind power for given load profiles. As a representative example of power load, we consider the load profile of a household appliance. We compare this load profile against the wind power available nearby the weather stations of the Finnish meteorological institute.

We expand on earlier research on the topic by discussing an infinitely repeated game model with a subgame perfect equilibrium strategy profile (SPE) as a solution concept that diminishes incentives to violate speed limits in a carrot and stick fashion. In attempts to construct an SPE strategy profile, the initial state is chosen such that the drivers are playing a mixed strategy whereas the police is not enforcing with certainty. We also postulate a short period version of the repeated game with generalized stage game payoffs. For this game, we construct a multistage strategy profile that is a Nash equilibrium but not an SPE. Some solution candidates are excluded by showing that they do not satisfy a one shot deviation property that is a necessary condition for an SPE profile in a repeated game of perfect information.

Reindeer husbandry in the Arctic region is strongly affected by the local climate. Reindeer herders are used to coping with adverse weather, climate, and grazing conditions through autonomous adaptation. However, todays rapidly changing Arctic environment poses new challenges to the management of herding activities. Finding means for combining traditional and scientific knowledge without depriving any of the systems of its fundamental strengths is hence deemed necessary. We apply a transdisciplinary framework for knowledge co-production involving international researchers and reindeer herders from different cooperatives in northern Finland. Through climate change adaptation stories, we co-explore how climate predictions can inform herders decision making during the herding season. Relevant decisions include the anticipation of summer harvest time, the inopportune periods of cold weather in spring, and insect harassment in summer. The analysis of two different adaptation stories shows that seasonal predictions of temperature for May and June can successfully advise about the likelihood of having an earlier than normal harvest. Likewise, sub-seasonal predictions of temperature during

Children increasingly use applications utilizing Artificial Intelligence / Machine Learning (AI/ML). Given the propensity of such applications to propagate existing social, gender, and racial biases, it becomes imperative to consider designing and developing child-centered AI applications for children. Furthermore, children should have opportunities and skills to critically reflect on current applications and envision and design better AI/ML applications that are ethical, specifically, those that are inclusive and fair. In our work, we focus on child-centered AI and inclusion. Using a two-fanged approach to inclusion and employing design futuring in our research with schools in India and Finland, children critically considered future technology design for all. In this paper, we present three cases of this work: a study with students at a school in New Delhi and two studies with students at schools in Oulu. Our work showcases how to design for inclusion - by designing for all, and how to design inclusively - by inviting children to envision the future, through design futuring approaches.

The digital divide is the gap among population sub-groups in accessing and/or using digital technologies. For instance, older people show a lower propensity to have a broadband connection, use the Internet, and adopt new technologies than the younger ones. Motivated by the analysis of the heterogeneity in the use of digital technologies, we build a bipartite network concerning the presence of various digital skills in individuals from three different European countries: Finland, Italy, and Bulgaria. Bipartite networks provide a useful structure for representing relationships between two disjoint sets of nodes, formally called sending and receiving nodes. The goal is to perform a clustering of individuals (sending nodes) based on their digital skills (receiving nodes) for each country. In this regard, we employ a Mixture of Latent Trait Analyzers (MLTA) accounting for concomitant variables, which allows us to (i) cluster individuals according to their individual profile; (ii) analyze how socio-economic and demographic characteristics, as well as intergenerational ties, influence individual digitalization. Results show that the type of digitalization substantially depends on age, inc

Infections are known to interact as previous infections may have an effect on risk of succumbing to a new infection. The co-dynamics can be mediated by immunosuppression or -modulation, shared environmental or climatic drivers, or competition for susceptible hosts. Research and statistical methods in epidemiology often concentrate on large pooled datasets, or high quality data from cities, leaving rural areas underrepresented in literature. Data considering rural populations are typically sparse and scarce, especially in the case of historical data sources, which may introduce considerable methodological challenges. In order to overcome many obstacles due to such data, we present a general Bayesian spatio-temporal model for disease co-dynamics. Applying the proposed model on historical (1820-1850) Finnish parish register data, we study the spread of infectious diseases in pre-healthcare Finland. We observe that measles, pertussis, and smallpox exhibit positively correlated dynamics, which could be attributed to immunosuppressive effects or, for example, the general weakening of the population due to recurring infections or poor nutritional conditions.

Public sector procurement units in the field of ICT suffer from siloed, application-specific architectures, where each system operates in isolation from others. As a consequence, similar or even identical data is maintained in several different databases hosted by different organizations. Such problems are caused by the lack of standard guidelines and practices that would result in interoperable systems instead of overlapping ones. In the Finnish public sector, enterprise architecture (EA) is a mandatory requirement so that an ecosystem can be formed to overcome the above problems. However, the adoption rates are low, and the focus is often on technology rather than processes and practices. This study investigates the use of EA and its potential in Finnish procurement units through semi-structured interviews. Five procurement units and four vendors participated in the study, and altogether 12 interviews took place.