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Supplementing XYR1-mutated Trichoderma reesei strain cultivation with (SO2-ethanol-water) softwood pulp improves cellulase production
No full textThe cellulolytic enzyme cost remains a major bottleneck in converting lignocellulose, especially softwoods, into fuels and chemicals. The aim of this study was to evaluate possibilities to increase enzyme production efficiency by using SO 2-ethanol-water (SEW) pretreated softwood pulp with a Trichoderma reesei strain that expresses a mutant form of the main transcriptional regulator, XYR1, of cellulase- and hemicellulase genes leading to loss of glucose repression/carbon catabolite repression. The (hemi)cellulase enzyme cocktail of this strain was improved by expressing three heterologous enzymes, a beta-glucosidase, a CEL6 (CBH2) exoglucanase and a lytic polysaccharide mono‑oxygenase. Seven bioreactor cultivations were performed using glucose and different cellulose supplementations and glucose feed strategies. We showed that adding 3 %-w/v cellulose to the glucose medium and starting the glucose feed when the glucose was consumed from the batch medium, improved the protein production rate by over 80 % during the first five days compared to total absence of cellulose. With only 3 % cellulose addition to the batch phase, we estimate that over one third of time and total carbon source, including cellulose, could be saved compared to a production process without cellulosic substrate supplementation. Additionally, enzymes produced with SEW pulp in 119 h and those produced with glucose alone in 193 h both achieved 90 % glucose conversion when used for SEW pulp hydrolysis at a protein loading of 4–5 mg/g cellulose. Herein, we have shown that the M2883 strain can produce more than 29 FPU/mL of the complete set of cellulase enzymes both with and without cellulose supplementation.</p
Investigating tritium retention in tungsten coated plasma facing components from the divertor region of the Joint European Torus (JET) after ITER like-wall campaigns
No full textTritium retention is a critical aspect of plasma-facing wall component performance in fusion reactors as well as reactor safety due to radiological risks it may pose. It is also of importance in the case of tungsten, including tungsten composites, which are selected as first wall and divertor material at devices such as ITER due to its high melting point and mechanical strength. This study aims to investigate surface characteristics, tritium retention behaviour and effect of baking on tungsten composite plasma-facing wall components from Joint European Torus (JET) divertor region and contribute to the understanding of tritium trapping within them. Three ITER-like wall (ILW) experimental campaigns involved exposing tungsten-molybdenum coated carbon fibre composite (CFC) samples to deuterium-deuterium (D-D) plasma discharges at various operating conditions, including different plasma densities, temperatures, and exposure times. The plasma-facing surfaces were characterized using scanning electron microscopy (SEM) in combination with energy-dispersive x-ray spectroscopy (EDX) and tritium retention was assessed using thermal desorption spectroscopy (TDS) and full combustion. Baking cycle was simulated by keeping the sample at 350℃ for 100 h, followed by TDS and full combustion. Results indicate tritium retention varying from 2 to 120∙1012 T atoms/plasma facing surface cm2. A deposition layer was found to be present for most samples analysed in this study ranging from 0 to 58 µm in thickness. For Tile 0 an increase in tritium retention was observed by the increase in the thickness of the deposition layer, whilst for Tile 1 deposition was not found to be the main source of retention. Tritium desorption temperatures were found to be higher than that proposed for baking at ITER − for Tile 0 tritium desorption peaks at about 540-640℃, while for tile 1 it is generally lower, but with a larger deviation ranging from 350 up to 570℃.</p
Futures of Everyday Life:A Qualitative Content Analysis of Future Personas in Scenarios
No full textScenario reports, holding a long-standing tradition in foresight and futures studies, act as an essential document for organizations to prepare for possible, plausible, and alternative futures. Focusing on descriptions and representations of everyday life, we examined 29 future persona narratives from six publications—covering a wide field from public to private sector—through qualitative content analysis. Our guiding question is: How can anthropological perspectives such as cultural relativism or postcolonial discourses contribute to an in-depth, qualitative interpretation depictions of future everyday life? Acknowledging anthropology's colonial origins and its growing commitment to the interests of indigenous and other marginalized groups, we offer alternative readings of prominent scenario reports. Our findings suggest that scenario reports, in addition to anticipating possible futures, construct certain futures based on a systematic analysis of empirical data but also speculative interpretation. The results of these interpretative acts often appear elitist, stereotypical, and technocratic, often replicating dominant societal narratives rather than fostering substantive shifts in how the future is imagined. We therefore call for a more polyphonic representation of futures in scenario writing and foresight work that can produce more discontinuous and transformative images of the future. We understand polyphonic representations as coined by various independent, predominant as well as subaltern perspectives on the same issue at stake while being offered the same amount of space. Therefore, as we will indicate in our analysis, most of the reports referred to are rather monophonic and do not offer discuptive perspectives on the future of everyday life. As an avenue of methodological development, we propose a more nuanced and comprehensive perception of culture and social structures in scenario narrative writing. In addition, ethnographic methods could increase our understanding of how futures are collaboratively constructed and produced by different actors and their respective backgrounds and knowledge in scenario processes.</p
Microbial metabolism in deep terrestrial subsurface communities - amino acids as biosignatures
No full textThe deep terrestrial subsurface (DTS) biosphere consists of a variety of distinct microbial taxa, mostly bacterial. The mechanisms by which microbes dynamically manage the uptake and concurrent utilization of nutrients within the DTS environments remain largely unexplored. Here, we examined the utilization patterns of amino acids and other polar metabolites in cultured DTS bacterial communities to investigate the adaptive responses and metabolic pathways employed under varying nutrient conditions to gain insight into how environmental shifts impact the metabolism of these communities. Previously, we found that changes in growth conditions affected the composition and size of the bacterial communities enriched from these oligotrophic, anoxic environments and induced changes in the production of primary and secondary metabolites. In the present study, metabolic fingerprinting was used to investigate the primary and secondary metabolite utilization and main metabolic pathways present in the enriched DTS bacterial consortium originating from the deep bedrock of the Fennoscandian Shield. We found that especially amino acids were predominantly degraded under different nutrient conditions. Notably, the degradation of phenylalanine and valine constituted a 'core' metabolic process that remained unaffected by variations in available nutrients within this community. Further, the most significant metabolic pathways employed were those connected to phenylalanine, cysteine and methionine.</p
Public policies for the circular economy – examples of European policies and references for Brazil
No full textSelf-supervised representation learning for cloud detection using Sentinel-2 images
Get PDFThe unavoidable presence of clouds and their shadows in optical satellite imagery hinders the true spectral response of the Earth underlying surface. Accurate cloud and cloud shadow detection is therefore a crucial preprocessing step for optical satellite images and any downstream analysis. Various methods have been developed to address this critical task and can be broadly categorized in physical rule-based methods and learning based methods. In recent years, machine learning based methods, particularly deep learning frameworks, have proven to outperform physical rule-based models. However, these approaches are mostly fully supervised and require a large amount of pixel-level annotations whose obtention is costly and time consuming. In this work, we propose to deal with cloud and cloud shadow detection in optical satellite images using self-supervised representation learning, a machine learning paradigm that focuses on extracting relevant representations from unlabeled data, which can then be used as an effective starting point to fine-tune models with few labeled data in a supervised fashion. These approaches were shown to perform competitively with fully supervised methods without the requirement of large annotation datasets. Particularly, we assessed two self-supervised representation learning methods that use different philosophies about self-supervision: Momentum Contrast (MoCo), based on contrastive learning and DeepCluster, based on clustering. Using two publicly available Sentinel-2 cloud datasets, namely WHUS2-CD+ and CloudSEN12, we show that MoCo and DeepCluster, trained with only 25% of the annotated data, can perform better than physical rule-based methods such as FMask and Sen2Cor, weakly supervised methods and even several fully supervised methods. These results point out the strong applicability of self-supervised representation learning methods to the task of cloud and cloud shadow detection with self-supervised pretraining leading to fine-tuned models that outperform industry standards and achieve near state-of-the-art performances with a fraction of the data
Direct-to-Device Connectivity for Aviation:Opportunities for Integrated CNS Services
No full textSatellites are a key component of aeronautical telecommunication networks for supporting communication, navigation, and surveillance (CNS) services. Satellite communication, also known as SATCOM, acts as a bridge between aircraft and terrestrial infrastructure and establishes air-to-ground data (A2G) datalinks to connect aircraft with the air navigation service providers. For example, SwiftBroadband-Safety (SB-S) has been emerged to provide a global, secure, broadband IP connection for both operations and safety communications to aircraft, which can support CPDLC and ADS-C services with the same safety services, helping airlines to be ready for future air traffic management evolutions. Iridium, partnership with Aireon, supports satellite-based automatic dependent surveillance – broadcast (ADS-B) in oceanic areas. Global navigation satellite system (GNSS) is the underlying technology that enables safe navigation and provides precise location data for ADS-B [1].As an important milestone, Airbus and OQ Technology through their fruitful collaboration have demonstrated the feasibility of connecting an unmanned aircraft, carrying a 5G user equipment, to a low-Earth orbit (LEO) satellite running a full stack of 5G base station. Recently, many major airlines started to offer Starlink connectivity to passengers enabling Internet services including browsing and 4K video streaming. Despite these advancements, small unmanned aircraft may not be able to benefit from traditional satellite systems, whose terminals are often large and energy-hungry.Direct-to-Device (D2D) connectivity is an emerging concept in new space era satellite communications [3]. D2D connects compact consumer devices e.g., smartphones, wearables, and machine type device directly to Earth orbiting satellites without relying on terminal or mediator gateways. Due to small device form factors and high energy efficiency, D2D appears as a promising solution for meeting the CNS service requirements of unmanned aircraft system (UAS). Particularly, D2D devices due to small form factor and low energy consumption can be mounted on the unmanned aircraft and could provide satellite-based A2G links to support CNS services. D2D links can enable beyond-line-of-sight coverage over remote and oceanic regions, augments GNSS accuracy through correction data and 5G NTN support, and facilitates transmission of ADS-B and ADS-C, enabling situational awareness,and resilient CNS operations for UAS
Integrating nuclear Small Modular Reactors into low-carbon energy systems:an illustration using a recent European R&D initiative
No full textThe race to develop Small Modular Reactors (SMRs) is in full swing around the world. SMRs are nuclear reactors with a power output of a few hundred MWe incorporating high modularisation and standardisation by design, thus facilitating economies of in-series production. SMR technologies have the potential to strongly contribute to decarbonisation of the energy sector but are yet to be deployed. Considered at a local or regional scale, SMRs can be fully integrated in innovative hybrid energy systems (HES), including variable renewables and nuclear energy in the form of electricity, heat or hydrogen, energy storage systems, heat networks, and power grids. These systems must operate flexibly to ensure the stability of energy networks. These integrated energy systems are currently under development, however, in Europe, studies on such systems remain limited. In this context, a European Industrial Alliance on Small Modular Reactors, launched by the European Commission in 2024, pointed out significant R&D gaps to be tackled to make these energy systems ready for deployment. Therefore, TANDEM, a Euratom-funded project was carried out between 2022 and 2025 to help fill these gaps. The project has delivered methodologies and tools for the assessment of HES and validated and demonstrated them on case studies for decarbonisation. The project enabled first evaluation considerations of the technical performance and economic viability of such systems. It then covered nuclear safety aspects and environmental impact. Finally, it investigated citizen engagement and Education & Training needs to prepare the workforce required for developing and deploying these energy systems.</p
Large-scale forest resource mapping with spatial gaps in the training data:Comparison of different modeling approaches
No full textForest attribute maps are essential for supporting local decision-making regarding forest resource use. Such maps are produced by combining remote sensing and field data through various modeling approaches. When mapping across large areas, spatial gaps in field data used for model training are common. Our study evaluates the performance of three methods—k-Nearest Neighbor (k-NN), Random Forests (RF), and Multi-Layer Perceptron (MLP)—for forest resource mapping across Norway, Sweden, and Finland in an experimental setup with respect to availability of field data around the target area. Models were trained with sample plot sizes (N) ranging from 100 to 3000. RF consistently produced the most accurate predictions in terms of relative bias and RMSE. While spatial gaps in the training data (radius: 7–141 km) affected %RMSE of broad-leaved above ground biomass (AGB), they had minimal impact on %RMSE of both local and country-level predictions of total AGB and volume. For RF with N=3000, %RMSE of total AGB ranged between 53%–55% in Finland and Sweden, and 70%–72% in Norway across gap sizes. However, %bias increased for local predictions across the whole study region with larger gaps: RF with N=500 showed bias of −12%–12% (7 km gap) and −17%–28% (78 km gap). Similarly, country-level %bias of total AGB for Norway increased from −1.7% to −3.7% with larger gaps. In conclusion, spatial gaps in training data can significantly affect bias in predictions. Therefore, forest attribute maps should always be accompanied by metadata describing the training data used.</p