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Towards 2040:Collaborative approach in Finnish food systems transition
Full text linkThis article presents experimental research that engaged Finnish stakeholders in discussions on how to transition global food systems toward sustainability by 2040, addressing urgent challenges such as climate change, resource depletion and population growth. The study introduces the “Future Food Court Workshops,” which involved representatives from various sectors, including industry, public institutions, third-sector organizations, educational entities, and consumers. To guide these discussions, the research developed an integrated framework combining social design, foresight, technology, and business perspectives, aiming to anticipate emerging needs and societal transformations. The workshops employed “Five Dimensions of Futures Consciousness” model for qualitative analysis of the stakeholder engagement; the model was used explicitly to understand how participants conceptualized the future of food systems. The analysis revealed, for example, how participants experienced sustainability challenges, their capacity for future-oriented behavior, the impact of present actions on future outcomes and the role of emerging technologies reflecting values of the systems they serve. This research advances the field of futures studies by demonstrating an interdisciplinary approach to engaging stakeholders in sustainable food system transitions, and hopefully offers valuable insights for researchers, policymakers, and practitioners, underscoring the necessity of adopting sustainable practices to address pressing environmental concerns.</p
Towards 2040:Collaborative approach in Finnish food systems transition
No full textThis article presents experimental research that engaged Finnish stakeholders in discussions on how to transition global food systems toward sustainability by 2040, addressing urgent challenges such as climate change, resource depletion and population growth. The study introduces the “Future Food Court Workshops,” which involved representatives from various sectors, including industry, public institutions, third-sector organizations, educational entities, and consumers. To guide these discussions, the research developed an integrated framework combining social design, foresight, technology, and business perspectives, aiming to anticipate emerging needs and societal transformations. The workshops employed “Five Dimensions of Futures Consciousness” model for qualitative analysis of the stakeholder engagement; the model was used explicitly to understand how participants conceptualized the future of food systems. The analysis revealed, for example, how participants experienced sustainability challenges, their capacity for future-oriented behavior, the impact of present actions on future outcomes and the role of emerging technologies reflecting values of the systems they serve. This research advances the field of futures studies by demonstrating an interdisciplinary approach to engaging stakeholders in sustainable food system transitions, and hopefully offers valuable insights for researchers, policymakers, and practitioners, underscoring the necessity of adopting sustainable practices to address pressing environmental concerns.</p
An Intelligent Optimization-Based Residual Negative Magnitude Shaping Scheme for Vibration Control
No full textWith the rapid advancement of modern manufacturing, suppressing residual vibrations in flexible and underactuated systems has become a critical challenge. Input shaping (IS) has garnered attention for its effectiveness in mitigating vibrations and enhancing motion performance. However, existing input shapers typically encounter unavoidable time delays (TDs), modeling inaccuracies, ineffective multimodal suppression and poor adaptability, limiting their control performance. Targeting at overcome these critical issues, this article proposes an intelligent optimization-based residual negative magnitude (NM) shaping vibration (IRV) control scheme with two novel ideas: 1) employing a data-driven differential evolution (DE) algorithm to estimate system errors; and 2) designing a robust particle swarm optimization (PSO)-based residual negative magnitude (PR) shaper to reduce TDs and compensate for modeling inaccuracies in multimodal vibration systems, thereby enhancing control adaptability to diverse system configurations. To validate its performance, eight real-world datasets have been established and made publicly available. Empirical studies demonstrate that the proposed PR shaper outperforms state-of-the-art shapers, and the IRV scheme achieves significant vibration suppression, reducing maximum residual vibrations by at least 9.26% compared to conventional methods. These advancements substantially improve vibration control in precision systems.</p
A probabilistic-driven approach for early design quality risk and crux identification using non-Markovian stochastic Petri nets
No full textQuality risk analysis of high-process-oriented systems, which refers to their ability to achieve required tasks on time, receives little attention during the early conceptual design stage, primarily due to the high level of abstraction when the system form is not yet fully defined. Although several mathematical methods exist to address this issue, they are fragmented across domains and lack a unified integration into early design practice. To address this problem, this paper introduces a novel approach that models design problems as discrete events with output conflict representation, using the non-Markovian stochastic Petri net. The framework is further integrated with mathematical techniques, including semi-Markov performance evaluation, sensitivity analysis, and uncertainty analysis, to quantify quality risks and identify the design crux (the most critical design parameters). By incorporating Monte Carlo simulations, it facilitates designers and engineers with early insights and allows them to compare alternative design specifications. Its applicability is demonstrated through a case study on the conceptual development of a remote maintenance system for the In-Bioshield area of the EU-DEMO fusion power plant. Initial results showed potential in identifying quality risks, addressing key factors contributing to the design problem, and finding optimal design specifications in the early stages
Strategic Marketing Tensions in Sustainable Business Models: A Conceptual Approach Through Customer Value Propositions and Stewardship
Full text linkSustainable business models (SBMs) inherently involve tensions, which are contradictory or misaligned demands that companies must consider simultaneously. However, there is a gap in the literature regarding the relevance and linkage of these tensions to strategic marketing considerations, including positioning, competitiveness, differentiation, and a company's interaction with customers. This study aims to identify a set of tensions that arise in the strategic marketing of SBMs and to explore how these tensions can be responded to by companies. The study adopts a conceptual methodology, applying customer value propositions (CVPs) as a structured strategic marketing lens to explore tensions. Further, stewardship is suggested as an ontological approach that shapes the strategic marketing responses to tensions for the collective good of future generations. The resulting framework outlines how companies can embrace SBM tensions, including their hierarchical intensity, make sense of complexity and address the dominance of unsustainable models through strategic marketing mechanisms.</p
Depolymerisation of γ-Valerolactone Organosolv Lignins with Unsupported Molybdenum-Based Catalysts
No full textLignin is an attractive feedstock for a wide variety of applications ranging from aromatic chemicals and transportation fuels to resins and coatings. Emerging biorefinery concepts, like the organosolv process, enable the separation of all the lignocellulose components, and moreover, produce lignins of high quality and purity susceptible to valorisation by depolymerisation. In this work, we focus on the depolymerisation of lignins obtained by γ-valerolactone (GVL) organosolv fractionation of four biomass feedstocks, eucalyptus, white birch, sugarcane bagasse and Scots pine. We demonstrate that lignins extracted with the GVL process are depolymerised using unsupported molybdenum-based catalysts under reductive conditions in supercritical ethanol. As a result, over 90% yields of low-molecular-weight lignin oils are obtained with minimal char formation, yields of the aromatic monomers being 7–16 wt%. Furthermore, the design of experiments method is used to analyse the effect of depolymerisation conditions, catalyst, hydrogen loading and temperature, on the yields and properties of the product fractions. Notably, we show that the properties of the lignin oils and monoaromatics can be tuned towards the targeted application by modifying the depolymerisation conditions.</p
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
HTGR TRISO Fuel and Graphite Waste Management Strategies
No full textThis paper presents a review of possible high-temperature gas-cooled reactor (HTGR) fuel and graphite cycle back-end routes performed in the Euratom GEMINI 4.0 project based on previous European, International Atomicc Energy Agency, Nuclear Energy Agency, and diverse national research programs. It considers different variants of tristructural-isotropic (TRISO) fuel (enriched uranium, Pu, Th/233U fissile and fertile to fissile cycles, with oxide or oxicarbide fuel material options) for block-type cores. It identifies the remaining research and development issues. As the Finnish ONKALO® (Posiva Oy) repository is currently almost operable (i.e. operating license application submitted by Posiva), being therefore the first high-level waste disposal facility worldwide, its waste acceptance criteria are taken as a reference for the direct disposal of HTGR fuel elements. Spent fuel management for HTGRs is strongly impacted by the large graphite and coating volumes compared to the small portion of the fissile kernel. Therefore, a separation of the fuel compacts from the graphite block has been investigated. Disposal of spent fuel compacts separately from the graphite blocks is indeed expected to significantly reduce the HLW volumes. Processes for separating the TRISO particles from the compacts are also considered, as well as the packages for disposal of compacts or TRISO particles. For the direct disposal or disposal of separated compacts or particles, the corrosion leach resistance of the fuel kernels and of the TRISO coating layers has a crucial impact on the performance assessment of TRISO fuel disposal. Specific leach and corrosion tests on irradiated TRISO particles and fuel compacts are indispensable for establishing an optimized disposal concept. An ultimate step of separation can be to extract the fuel kernels from the TRISO coatings. It can be considered as a head-end step for reprocessing, the feasibility of which is examined. On the other hand, irradiated graphite management is a specific challenge for all graphite-moderated reactors due to the large associated volume, the specific contamination, and the degradation caused by neutron irradiation. Long-lived activation products, such as 14C and 36Cl, lead to the categorization of irradiated graphite as an intermediate-level waste in most countries. Therefore, treatment methods for reducing and/or stabilizing such isotopes for achieving a lower waste category, at a much lower disposal cost, are assessed. Reuse and refabricating options for irradiated graphite would be an interesting strategy toward a closed HTGR graphite cycle.</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
From Leaves to Breezes: Machine learning based prediction of nitrogen dioxide concentration from surrounding urban greenery and meteorological, spatial, and traffic characteristics in Berlin, Germany
No full textThis study compares two machine learning models, a Random Forest (RF) and a spatial Graph Neural Network (GNN), for predicting nitrogen dioxide (NO) concentrations across diverse urban conditions in Berlin, Germany. Therefore, both models use information on local land-use characteristics, meteorological conditions, and seasonal greenery, which enables a post-hoc analysis of high-concentration scenarios under varying environmental factors. Unlike most previous approaches to air-pollution estimation, this study explicitly considers the interaction between urban greenery and its seasonal variation. The analysis is based on a self-curated, high-resolution site-level environmental dataset that captures hourly NO observations from sixteen monitoring stations across Berlin in 2023 with detailed land-use, traffic, and architectural data obtained from the Berlin Geoportal. This dataset is supplemented with multiple meteorological records from the Deutscher Wetterdienst (DWD). While both models achieve comparable accuracy (R 0.6), the GNN shows a tendency toward less variation of predictive accuracy across test sites, suggesting potential spatial robustness. For explainability, only the RF model allows for local interpretability via Shapley values, which indicate that urban greenery helps mitigate NO levels depending on seasonal changes in leaf area. However, additional statistical testing does not support this observed trend. Beyond the conducted assessment, this research contributes a comprehensive environmental dataset that links air quality, land-use, and meteorological variables at hourly resolution. This resource supports future investigations into how environmental and spatial factors jointly influence pollutant dispersion and decomposition in urban environments