Turku University of Applied Sciences (abbr. TUAS, Finnish Turun ammattikorkeakoulu) is a multidisciplinary higher education institution, located in the city of Turku and Salo in the Southwest Finland. The institute began operations as a temporary polytechnic in autumn 1992. Before 2006-01-10, the institution carried the English name of Turku Polytechnic.At the moment, the establishment has approximately 9,600 students and 700 members of staff, making it one of the largest universities of applied sciences in Finland.In Finland Universities of Applied Sciences (UAS) have the mission to train professionals with emphasis on labour market needs and conduct research and development which supports instruction and promotes regional development in particular. The education in UAS emphasises co-operation with the business, industry and service sectors at the regional level in particular.TUAS offers education in four fields of study and altogether in over 70 degree programmes, both Bachelor and Master studies. Most of the degree programmes are conducted only in Finnish. TUAS provides also training and consulting services for both individuals and organizations in the public and private sector and coordinates or acts as a partner in over 200 RDI projects yearly.
Generalization of imitation-learned navigation policies to environments unseen in training remains a major challenge. We address this by conducting the first large-scale study of how data quantity and data diversity affect real-world generalization in end-to-end, map-free visual navigation. Using a curated 4,565-hour crowd-sourced dataset collected across 161 locations in 35 countries, we train policies for point goal navigation and evaluate their closed-loop control performance on sidewalk robots operating in four countries, covering 125 km of autonomous driving. Our results show that large-scale training data enables zero-shot navigation in unknown environments, approaching the performance of policies trained with environment-specific demonstrations. Critically, we find that data diversity is far more important than data quantity. Doubling the number of geographical locations in a training set decreases navigation errors by similar to 15%, while performance benefit from adding data from existing locations saturates with very little data. We also observe that, with noisy crowd-sourced data, simple regression-based models outperform generative and sequence-based architectures.
As populations age globally, wearable health technologies offer promising solutions to support autonomy and well-being among older adults. This study explored the adoption of smart wearable systems, such as wristbands and chest sensors, for remote health monitoring among 352 older adults (aged 60–99) in Northern Portugal. Results showed that 74.4
Photovoltaic (PV) production grows rapidly in the Nordics, but literature on calculating long-term PV performance losses remains limited in these conditions, leading to inaccurate yield estimations and economic losses. To fill this knowledge gap, this contribution (1) shows typical PV performance characteristics, (2) obtains performance loss rates (PLRs) from multiple systems, and (3) assesses the best practices of PLR calculation under these conditions. For these objectives, we compared various methods for all steps of PLR calculation, including data filters, performance metrics, aggregation intervals, and statistical models for five systems (with 2.5-6 years of data) across Finland, from 60 degrees to 67 degrees N. Dark winters and snow coverage caused gaps and anomalies to the performance time series during wintertime, resulting in unrealistic PLRs. Excluding these anomalies with performance threshold filter improved the results. As the different methods can drastically affect the PLR value, an ensemble method consisting of an assessment of average PLR with over 700 calculation approaches was validated to be essential in identifying robust PLRs. Using this ensemble method, the system PLR estimates ranged from 0.41%/year to 2.65%/year. The data and codes are made publicly available, contributing to open access of PV datasets and allowing wider application of the PLR methodology.
This systematic review, conducted according to PRISMA guidelines and registered in PROSPERO (CRD420251055299), examined the use of wearable technologies for promoting physical activity (PA) in adults aged 60 years and older. Searches across five databases (PubMed, Scopus, Web of Science, CINAHL, Cochrane) identified 2438 records, of which only six randomized controlled trials published between 2021 and 2025 met inclusion criteria, with sample sizes ranging from 36 to 551 participants and mean ages between 65 and 79 years. Given the small number of included studies, findings should be interpreted as preliminary.The studies ranged from the standalone use of commercial trackers (Fitbit, Polar, ActiGraph) to multicomponent interventions combining wearables with physiotherapist feedback, telephone counseling, web-based platforms, or interactive cognitive-motor training. Wearables used alone, as in the REACT trial, produced small or non-significant PA effects. In contrast, interventions integrating devices with personalized feedback, professional support, or digital platforms, such as PROMOTE and TASMANIA, were associated with more consistent improvements in PA, physical function, and cognitive outcomes. Multicomponent programs, such as PEER and ICMT, reported broader benefits, including cognition, balance, and reductions in sedentary behavior, though these findings derive from individual trials and require replication.Risk of bias, assessed with the Cochrane Risk of Bias tool version 2 (RoB 2.0), was rated as "some concerns" for five studies and low for only one, mainly due to gaps in randomization reporting, missing data, and lack of preregistration.Tentatively, and based on a very limited evidence base, wearables may have greater impact when embedded within broader behavioral systems, incorporating feedback, coaching, or interactive components, rather than when used in isolation as passive monitoring tools. Adherence and psychosocial outcomes appeared related to comfort and perceived usefulness among older adults, though larger and more robust trials are needed to confirm these patterns.
Background: Knee joint effusion might indicate injury even without bony changes. Automated detection from radiographs could improve the sensitivity of AI algorithms. Purpose: To compare two commercially available AI algorithms, BoneView and RBfracture, in detecting knee joint effusion. Material and Methods: This retrospective study collected 123 lateral knee radiographs. Detection of knee joint effusion by both AI algorithms was compared with two board-certified radiologists with arbitration. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and interobserver agreement (Cohen's Kappa) were calculated. 95% confidence intervals (CI) assessed robustness. McNemar's tests compared sensitivity and specificity between AI algorithms. Results: Knee joint effusion was present in 56% of radiographs. BoneView demonstrated a sensitivity of 0.42 (95% CI: 0.31-0.54), specificity of 1.00 (95% CI: 0.93-1.00), PPV of 1.00 (95% CI: 0.88-1.00), NPV of 0.57 (95% CI: 0.47-0.67), and accuracy of 0.68 (95% CI: 0.59-0.75). RBfracture demonstrated a sensitivity of 0.75 (95% CI: 0.64-0.84), specificity of 0.91 (95% CI: 0.80-0.96), PPV of 0.91 (95% CI: 0.81-0.96), NPV of 0.74 (95% CI: 0.63-0.83), and accuracy of 0.82 (95% CI: 0.74-0.88). Cohen's Kappa was 0.49 (95% CI: 0.35-0.63), indicating moderate agreement between the two AI algorithms. Adding knee joint effusion detection to fracture/dislocation predictions improved sensitivity. Conclusions: Two commercially available AI algorithms demonstrated different operating points for knee joint effusion detection: BoneView achieved high specificity, while RBfracture achieved higher sensitivity. Combining injury and effusion predictions increased sensitivity at the cost of specificity.
