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Leveraging the transition to strategic capitalism:A summary of a Delphi expert-opinion study
No full textFractured geopolitics, shifting alliances, and growing systemic uncertainty define the environment in which Finland must now build and sustain its innovation resilience. This Delphi study was launched to explore how Finland, and Europe more broadly, can navigate these evolving conditions in order to foster a more robust and future-proof innovation system. The findings from the study revealed three interlinked layers of insight: external vulnerabilities, internal capabilities, and strategic positioning. Together, these layers form the underlying structure of this policy brief.The deck begins by outlining both internal and external vulnerabilities shaping Finland’s innovation environment. These range from the erosion of geopolitical trust and increasing threats to talent flows, to the growing dominance of China across critical technologies and value chains. These developments are not merely weak signals or short-term disruptions; rather, they represent structural conditions that fundamentally shape the global playing field for innovation. The middle section highlights an emerging shift from laissez-faire approaches toward what experts increasingly describe as strategic capitalism. Respondents emphasized the importance of not only investing in R&D, but also improving the capacity to absorb and deploy it effectively. They pointed to the need for stronger coordination across policy silos and a move away from episodic interventions toward more sustained, long-term innovation strategies. The discussion of industrial, technology, and innovation policy underscores a central message: ambition alone is insufficient without coherence.The final part of the deck repositions Finland’s innovation system firmly within the European framework. Experts stressed Finland’s growing dependency on EU-level instruments, particularly in funding, regulation, and standard setting, highlighted its potential role in helping to shape Europe’s global technological posture. At the same time, they noted that Finland must remain agile, sharpen its priorities, and engage strategically in Brussels to ensure influence rather than passivity. Across the study, it became clear that resilience should not be understood as a single policy choice or an isolated strategy. Instead, it represents a deeper structural shift in how innovation systems are organized and governed. This deck translates that insight into a coherent narrative - one that begins with risk, moves through leverage, and ultimately points toward action
Capture of <i>Saprolegnia parasitica</i> Spores in Flow-Through Aquaculture:First Observations
No full textSaprolegniosis, typically induced by oomycete Saprolegnia parasitica, is one of the most difficult pathogens in fish and other aquatic animals in freshwater systems. It is especially harmful for the endangered species landlocked salmon (Salmo salar m. sebago). Currently, there are only few alternatives to prevent and treat saprolegniosis occurrences, which can lead to major fish deaths and financial losses at fish farms. In this study, surface-modified cellulose materials were used at an experimental flow-through fish farm rearing landlocked salmon, which often suffers from saprolegniosis occurrences. The results showed that the material's cationic surfaces were able to capture the spores of S. parasitica (experimental part I and part II). The cellulose material was chemically modified with a high density of cationic quaternary ammonium groups, which performed better than a material with a weak cationic charge by amino groups obtained via physisorption of chitosan on the surface, resulting in fewer S. parasitica spores in the rearing tank water (experimental part I). The results are promising and offer a novel method for controlling saprolegniosis occurrences without harmful chemicals. However, certain environmental conditions (in experimental part II) inhibited the detection method (real-time quantitative polymerase chain reaction) used for the detection of S. parasitica. This highlights the need for further method development for the detection of S. parasitica. Overall, the results are promising in terms of reducing S. parasitica spores in rearing water and further controlling saprolegniosis occurrences. More process optimization is required to achieve the method's full potential in industrial scale processes.</p
FilMBot:A High-Speed Soft Parallel Robotic Micromanipulator
No full textSoft robotic manipulators are generally slow despite their great adaptability, resilience, and compliance. This limitation also extends to current soft robotic micromanipulators. Here, we introduce FilMBot, a 3-DOF film-based, electromagnetically actuated, soft kinematic robotic micromanipulator achieving speeds up to 2117 °/s and 2456 °/s in α and β angular motions, with corresponding linear velocities of 1.61 m/s and 1.92 m/s using a 4-cm needle end-effector, 0.54 m/s along the Z axis, and 1.57 m/s during Z-axis morph switching. The robot can reach ∼1.50 m/s in path-following tasks, with an operational bandwidth below ∼30 Hz, and remains responsive at 50 Hz. It demonstrates high precision (∼6.3 μm, or ∼0.05% of its workspace) in path-following tasks, with precision remaining largely stable across frequencies. The novel combination of the low-stiffness soft kinematic film structure and strong electromagnetic actuation in FilMBot opens new avenues for soft robotics. Furthermore, its simple construction and inexpensive, readily accessible components could broaden the application of micromanipulators beyond current academic and professional users.</p
Introducing OpenTextile-NIR: Near-infrared hyperspectral imaging and photography dataset for optical identification of textiles
No full textThis dataset presents the first open-access collection of near-infrared hyperspectral imaging (NIR-HSI) data for the optical identification of textiles, with a focus on supporting research in sensor-based textile sorting and recycling. The dataset comprises hyperspectral images, RGB photographs, and detailed metadata, including fibre composition and colour, for 71 post-industrial textile samples, collected in Finland. Over 11 million spectra are included in the hyperspectral images, with more than 6 million annotated, providing a robust foundation for machine learning and data analysis. In addition, we provide a single representative NIR spectra and RGB value for each sample in order to accommodate classic spectroscopic analysis.Used garments were sourced from a partner company specializing in end-of-life textile management, with ground truth information on fibre composition obtained from suppliers. Small pieces of each garment were measured using Specim SWIR 3 hyperspectral camera and photographed with high-resolution mobile phone camera (Samsung Galaxy A52). The dataset is organized into folders containing raw and processed data, including ENVI-format hyperspectral images, RGB images, as well as CSV files with mean spectra, mean RGB values, and sample metadata. An example Python script is provided to facilitate data access and processing.Potential reuse scenarios include classification of textiles by material or colour, prediction of natural fibre content, image segmentation, algorithm development for spectral classification, and use as a reference spectral library. The dataset’s comprehensive structure and open availability address the limitations of previous research, which often relied on small or non-public datasets, and is intended to accelerate advances in optical identification technologies for textile recycling
CeOx-functionalized Pd nanoparticles on single-walled carbon nanotubes for alkaline hydrogen oxidation reaction
No full textThe sluggish kinetics of hydrogen oxidation reaction (HOR) in alkaline electrolytes highlight the strong need to develop next-generation catalyst materials for anion-exchange membrane fuel cells (AEMFC) anodes. In this study, CeOx is sequentially deposited on Pd nanoparticles supported on single-walled carbon nanotubes (SWNT) via atomic layer deposition (ALD). The obtained Pd@CeOx SWNT16 ALD cycles catalyst shows an excellent alkaline HOR performance with a specific exchange current of 166 mA mg−1Pd. This is three times higher than the commercially available Pd catalyst and the highest among reported Pd/CeOx catalyst materials with different CeOx overlayer coverages. By combining the X-ray diffraction, X-ray photoelectron spectroscopy, X-ray absorption spectroscopy and high-resolution scanning transmission electron microscopy, we confirm that the activity improvement is due to the highly conductive SWNT support enabling the fabrication of high surface area Pd clusters and CeOx overlayer. These methods reveal that the oxidation state of Ce is varying from Ce3+ to Ce4+ in relation to the CeOx overlayer thickness and the number of ALD cycles. Density functional theory calculations show that the presence of Ce/CeOx increases the diversity and population of Pd active sites with improved activity in its vicinity leading to enhanced overall catalytic performance. Moreover, this work provides a new perspective to develop highly active alkaline HOR catalysts for AEMFC
SMR Core Depletion and Spent Fuel Characterization with Nodal Code Ants
No full textTo complement the burnup capability of Serpent Monte Carlo transport code in the Kraken reactor simulator framework, a reduced-order approach in nodal code Ants is developed for fuel inventory calculations. The method is based on a microscopic depletion model, which is capable of tracking nuclide concentrations on the node level in 3D coupled core simulations. This enables explicit modeling of local irradiation conditions, which is not practical for Monte Carlo transport in regular engineering applications. This study demonstrates the Ants micro-depletion method in the simulation of three fuel cycles of a boron-free pressurized water reactor. Comparison to a Serpent 3D Monte Carlo simulation shows that Ants can calculate best-estimate nuclide inventories usable for spent fuel transport, storage, and final disposal. Based on the results, the micro-depletion method is an attractive option for spent fuel analysis
From electricity to economy-wide Net-Zero:Comparative analysis of global energy pathways
No full textThe transition to Net Zero (NZ) energy systems has become a global priority for combating climate change and achieving sustainable energy systems. This paper presents a systematic review of national NZ electricity and energy system studies, with a primary focus on country-based modeling practices rather than purely academic literature. The review highlights how NZ pathways across nations reflect their unique energy resources, policy priorities, technological capabilities, and socio-economic contexts. Drawing from studies conducted after the Paris Agreement, the paper classifies NZ work into four categories: (I) clean electricity (CE) system studies, (II) NZ electricity system studies, (III) economy-wide NZ studies, and (IV) worldwide NZ economy studies. For each category, it examines the modeling frameworks used, the treatment of key technologies, and the integration of policy and market considerations. The analysis highlights significant differences in assumptions, time horizons, technology portfolios, and the treatment of grid reliability, flexibility, and seasonal balancing. By synthesizing results from multiple countries, including the United States, Japan, South Korea, China, the UK, Sweden, Thailand, France, Canada, Australia, Colombia, Indonesia, Vietnam, and EU member states, the paper identifies common challenges such as long-duration storage needs, transmission expansion, and operational stability under high inverter-based resource penetration. The findings reveal that while renewable energy and storage dominate most NZ strategies, achieving reliable and cost-effective transitions will require integrated planning across sectors, coordinated infrastructure investment, and context-specific policy design. This country-comparative perspective offers insights for policymakers, system planners, and researchers seeking to adapt global NZ strategies to national realities.</p
High-Resolution Synchrotron µXRD and µXRF for Local Phase and Elemental Analysis in Suspension Plasma Sprayed Environmental Barrier Coatings
No full textSuspension plasma spraying (SPS) enables the fabrication of environmental barrier coatings (EBCs) with complex multilayer architectures; however, degradation in such systems often initiates locally at buried interfaces, making it difficult to resolve using conventional laboratory-scale characterization techniques. In this work, the applicability of synchrotron-based micro-x-ray diffraction (µXRD), combined with micro-x-ray fluorescence (µXRF), is evaluated for the characterization of SPS-deposited ytterbium disilicate (YbDS) EBCs. An as-sprayed YbDS coating was investigated as a baseline case to examine differences between conventional XRD and spatially resolved µXRD, while an annealed and CMAS-exposed YbDS coating was studied as a service-relevant case to probe localized phase evolution. The samples were selected from previously optimized SPS process conditions and are not intended for direct comparison. Laboratory-scale XRD provided global phase information, whereas µXRD enabled layer-specific phase identification and resolved localized interfacial features. In the as-sprayed condition, µXRD confirmed phase-pure YbDS, resolved the crystallinity of individual coating layers, and verified the absence of unintended interfacial reaction phases that are not accessible by conventional XRD. In the annealed + CMAS-exposed coating, µXRD and µXRF revealed the formation of a calcium–ytterbium–silicate oxyapatite phase confined to the YbDS/Si interface, highlighting the localized nature of CMAS-induced degradation. These results demonstrate that synchrotron microanalysis provides valuable complementary insight for probing localized phase evolution in thermally sprayed EBC systems.</p
Recent advancements in artificial intelligence - driven breast cancer molecular subtypes classification using multi-omics: A comprehensive review
Full text linkBreast cancer is one of the heterogeneous diseases comprising various molecular subtypes. All molecular subtypes have different characteristics and behave differently to treatment response, prognosis and therapy. Accurate and precise classification of breast cancer molecular subtypes is crucial to know how breast cancer behaves, grows and responds to treatment and prognosis on the molecular level. There are a few existing studies conducted on breast cancer molecular subtypes classification, either using mono-omics or multi-omics, while lacking systematic comparisons of both. However, there is a need to know the performance and differences of mono-omics and multi-omics high-throughput technologies for breast cancer molecular subtypes classification, including the taxonomy, heterogeneity, causes, risk factors and unique molecular characterization. Therefore, to overcome these issues, this comprehensive review provides a structured synthesis of the current state of research on breast cancer molecular subtypes classification. Artificial Intelligence (AI)-driven Machine Learning (ML) and Deep Learning (DL) models, are employed for breast cancer molecular subtypes classification, mainly focusing on mono-omics and multi-omics. The analysis of this review shows that multi-omics technologies have great potential for the accurate and precise classification of breast cancer compared to mono-omics. It not only provides a detailed structure of the breast cancer molecular subtypes but also provides a comprehensive view of tumor progression, growth dynamics, aggressiveness, and underlying biological mechanisms. The correct integration of multi-omics data types and variants plays a significant role in classifying breast cancer molecular subtypes. Based on the extensive analysis of the existing studies, some of the main challenges that still exists remain in the classification of breast cancer molecular subtypes, include high dimensionality of multi-omics data, overfitting, data imbalance, models overperformance on minority classes, high correlation and overlapping, computational complexity, accurate integration of multi-omics data types and variants, analysis of the misclassification patterns and accurate classification of breast cancer molecular subtypes
Corrigendum to “A Novel Data-Driven Input Shaping Method Using Residual Impulse Vector Via Unscented Kalman Filter”:[Knowledge-Based Systems (2025), Volume 329, Part B, November 2025, 114385] (S0950705125014248), (10.1016/j.knosys.2025.114385)
No full textThe authors regret that there is an error in the affiliation listing. The first affiliation is currently merged as one entry, but it needs to be split into two separate ones to accurately reflect the first author's dual affiliations and, most importantly, to comply with the mandatory graduation requirements of her university. Requested Correct Format: Weiyi Yanga b, Yuqi Lic, Mingsheng Shanga b, Shuai Lid e, Shiping Wenf a. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China b. Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China c. Institute of Computing Technology, Chinese Academy of Sciences, China d. Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland e. VTT-Technology Research Center of Finland, 90570 Oulu, Finland f. Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology, Australia The authors would like to apologise for any inconvenience caused.</p