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A Uniform Framework for Climate Change Adaptation of Critical Infrastructure Using Nature-Based Solutions
No full textWith climate change expected to intensify hazards across Europe, empowering communities and strengthening local adaptation is urgent. The challenge is bolstering the resilience of critical infrastructure (CI), which faces substantial risks. Transitioning from predominantly “grey” infrastructure to integrated “green-grey” solutions provides an effective way to safeguard societal and infrastructural assets against hazards and environmental degradation. Although several frameworks developed by international networks and regional authorities exist, they often fail to fully address the nuanced challenges of CI climate proofing, disaster risk reduction, and biodiversity protection. In response to these limitations and to address key societal challenges, the work here introduces an innovative, integrative blueprint framework. This framework synthesises existing approaches to CI climate adaptation, systematically strengthening resilience with nature-based solutions (NBS). The framework is partially applied and validated through the Public-Private-Civil Partnership (PPCP®) approach, and operationalised in two climatically distinct but heatwave-prone regions: Egaleo (Greece) and Helsinki (Finland). These Labs have promoted more inclusive policymaking by supporting collaboration among key stakeholders, encouraging knowledge sharing and co-designing strategies to advance NBS implementation for heatwave mitigation. The approach facilitated the design of interconnected activities and simplified technical details. Adapting methods to local needs, such as site visits and participatory mapping, has led to concrete outcomes. The prefeasibility analysis outcomes and the targeted NBS-based strategies identified from these areas ensure that solutions are culturally relevant, technically feasible, and collectively owned, incorporating local knowledge and fostering long-term sustainability
Transition of dominating roles from dislocations to stacking faults enables superior mechanical properties of CoCrNi alloys
No full textGenerally, balanced strength and ductility can be achieved by tailoring crystal defects for face-centered cubic (FCC) alloys, in which dislocations play a critical role. This study investigates the deformation mechanisms and strain-hardening behavior of CoCrNi alloys (45Ni, 33Ni, 24Ni) with different stacking fault energies (SFEs). The 45Ni and 33Ni exhibit a dislocation-dominated deformation mechanism. In contrast, stacking faults (SFs) dominate in the 24Ni alloy, which is closely related to the very low SFE. SFs not only strengthen the FCC matrix but also promote the hexagonal close-packed (HCP) phase nucleation. The overall effect of the nanoscale thickness and the significant volume fraction of the HCP phase leads to a sustained high strain-hardening rate. In addition, the product of ductility and strength of 24Ni is significantly higher than that of equiatomic CoCrNi alloy and 316 L stainless steel at the critical grain size of ∼0.7 μm. These findings provide new insights for further improving the mechanical properties of FCC alloys.</p
Kiinnostus fuusioenergiaa kohtaan kasvaa
No full textFuusioenergiaa on pitkään pidetty tulevaisuuden suurena toivona - vaihtoehtona, jonka avulla voidaan tuottaa mittavia määriä puhdasta energiaa. Viime vuosina alalla on nähty enemmän kehitystä kuin useisiin aiempiin vuosikymmeniin yhteensä: uusia maailmanennätyksiä on syntynyt, ja yli 50 uutta yritystä on perustettu
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
Exploring new cellular agriculture-based value chains via an analysis on potential feedstock sources in Finland
No full textThe aim of this study was to examine cellular agriculture-based value chains in Finland with two specific objectives: 1) to estimate the potential of selected Finnish agri-food industry side streams and agri-biomasses as the source of carbon for microbial protein production and 2) to identify the barriers and enabling factors related to four cellular agriculture-based value chains based on the Finnish feedstocks sources for fermentation. By evaluating the carbohydrate content of 13 plant-based biomass streams (molasses, brewers spent grain, distillers spent grain, sugar beet stalk, sugar beet pulp, oat husk, wheat bran, rapeseed cake, potato cell juice, potato peels and residues, potato tops, straw, surplus grass) as a sugar source for fermentation, the total microbial protein production potential was calculated annually at ca. 290 000 and 360 000 tons for precision and biomass fermentation, respectively. Among the agricultural and food industry streams, straw and oat husk biomass could theoretically supply feedstock for 211 000 and 22 000 tons of protein per year by biomass fermentation, respectively. This is a substantial amount, e.g. when considering 120 000 tons of protein needed annual by Finnish population. The qualitative part of the study elaborated barriers and opportunities of the biotechnology-based production processes using four value chain concepts with distinct feedstock source (grass, bran/husk, sawdust and greenhouse residues) as case examples. The qualitative analysis concluded that, in addition to bioprocess development for reducing production costs, key factors for ensuring well-functioning cellular agriculture business models include resolving agricultural feedstock pre-processing and logistics, optimized facility location, and access to renewable energy
Recent advancements in artificial intelligence - driven breast cancer molecular subtypes classification using multi-omics: A comprehensive review
Get PDFBreast 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
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
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
Feasibility of evacuation from the front line using unmanned ground vehicles during platoon-level defensive combat
No full textIntroduction Advancements in technology and intelligence, as well as deliberate targeting of medical personnel and vehicles, have made casualty extraction increasingly hazardous. The Russo-Ukrainian War has further demonstrated that the rapid development of unmanned technologies may also enable novel approaches. Although some of these systems have been deployed, reporting on their performance is scarce and understandably incomplete, which limits their evidence-based and effective integration with fighting forces. This paper addresses this gap by presenting preliminary findings on potential ranges of evacuation unmanned ground vehicles (UGVs) utilisation. Methods A virtual simulation experiment was conducted, where a platoon defended against a mechanised infantry company. The experiment was a repeated military exercise with different groups of participants. The defending force had evacuation UGVs, which were placed close behind the defensive line. The aim was to determine whether UGVs could survive long enough to support evacuation and whether evacuation could be carried out before the conflict ended. Furthermore, the availability of UGVs and the likelihood that an evacuation attempt could avoid enemy interference were assessed. The experiment involved 470 participants divided into 11 groups. Each participant completed four combat scenarios. Players of each group switched sides and environments. In total, 44 instances of skirmishes were fought in a virtual simulation environment. Results The simulation results indicated UGV loss rate of 53%. Evacuations were attempted in 45% of skirmishes. Furthermore, 81% of initiated evacuation attempts were successful. Conclusions The experiment provided estimates of evacuation UGV loss rates near the defence line amid active conflict. It also offered evidence on the feasibility of initiating evacuation before the active conflict had fully ceased, and the likelihood of the moving evacuation vehicle encountering enemy fire. These findings can guide decisions on whether the risk of losing small evacuation vehicles and their equipment is acceptable when deployed near front lines.</p
TRIM:Thermal Auto-Compensation for Resistive In-Memory Computing
No full textin-memory computing (IMC) has emerged as one of the most promising architectures to efficiently compute artificial intelligence tasks on hardware, particularly deep neural networks (DNNs). IMC can make use of analog computation principles alongside emerging nonvolatile memories (eNVM) technologies, potentially offering several orders of magnitude increased energy efficiency compared to generic processing units. Yet, the use of analog circuitry, potentially integrated with emerging technologies post-processed on top of silicon wafers, increases the susceptibility of hardware to a large spectrum of variations, for instance manufacturing, noise or temperature sensitivity. Hence, this susceptibility can hamper the large-scale deployment of IMC circuits into the market. To tackle the reliability of analog resistive-based IMC circuits regarding temperature variations, this article presents TRIM, a thermal on-chip auto-compensation method aimed at fully calibrating first-order temperature effects. TRIM is designed to maintain the computational accuracy of IMC cores in DNN applications over a wide temperature range, while being highly scalable and adaptable. In essence, the temperature compensation is realized through a complementary-to-absolute-temperature (CTAT) voltage reference integrated inside a voltage regulator and applied at the zero reference node of a multiplying digital-to-analog converter (MDAC), eliminating the need for external circuits or look-up table. The proposed methodology is demonstrated on a proof-of-concept 65 nm CMOS resistive IMC column. Measurement results showcase that the proof-of-concept auto-compensation system significantly enhances inference and multiply-and-accumulate (MAC) operation accuracy of any first-order resistive crossbar column, achieving inference accuracy recovery of 100% over a temperature range of –20 °C to 60 °C and a 91.3% improvement in MAC operation accuracy, with an area overhead of 2% and power overhead of < 0.02%.</p