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In this publication it is given a brief overview on the state of art of green energy harvesting. Green energy harvesting aims to supply electricity to electric or electronic systems from an energy source present in the environment (e.g., thermal energy (thermoelectricity)) without grid connection or utilisation of batteries. Almost all manufacturing processes ranging from steel to food production generate heat (the so called “waste heat”), as do all machines from jet engines to microprocessors. The possibility of using a thermoelectric (TE) device to capture and to directly convert this waste heat into electric power is a very attractive and valuable approach to improve the overall energy efficiency and, thus, promotes a sustainable future.

AbstractFunctional magnetic resonance imaging (fMRI) is a pivotal tool for mapping neuronal activity in the brain. Traditionally, the observed hemodynamic changes are assumed to reflect the activity of the most common neuronal type: excitatory neurons. In contrast, recent experiments, using optogenetic techniques, suggest that the fMRI-signal instead reflects the activity of inhibitory interneurons. However, these data paint a complex picture, with numerous regulatory interactions, and where the different experiments display many qualitative differences. It is therefore not trivial how to quantify the relative contributions of the different cell types and to combine all observations into a unified theory. To address this, we present a new model-driven meta-analysis, which provides a unified and quantitative explanation for all data. This model-driven analysis allows for quantification of the relative contribution of different cell types: the contribution to the BOLD-signal from the excitatory cells is <20 % and 50-80 % comes from the interneurons. Our analysis also provides a mechanistic explanation for the observed experiment-to-experiment differences, e.g. a biphasic vascular response dependent on different stimulation intensities and an emerging secondary post-stimulation peak during longer stimulations. In summary, our study provides a new, emerging consensus-view supporting the larger role of interneurons in fMRI. AbstractFunctional magnetic resonance imaging (fMRI) is a pivotal tool for mapping neuronal activity in the brain. Traditionally, the observed hemodynamic changes are assumed to reflect the activity of the most common neuronal type: excitatory neurons. In contrast, recent experiments, using optogenetic techniques, suggest that the fMRI-signal instead reflects the activity of inhibitory interneurons. However, these data paint a complex picture, with numerous regul

Electron microscopy touches on nearly every aspect of modern life, underpinning materials development for quantum computing, energy and medicine. We discuss the open, highly integrated and data-driven microscopy architecture needed to realize transformative discoveries in the coming decade.

Efficient exfoliations of bulk molybdenum disulfide (MoS2 ) into few-layered nanosheets in pure phase are highly attractive because of the promising applications of the resulted 2D materials in diversified optoelectronic devices. Here, a new exfoliation method is presented to prepare semiconductive 2D hexagonal phase (2H phase) MoS2 -cellulose nanocrystal (CNC) nanocomposites using grinding-promoted intercalation exfoliation (GPIE). This method with facile grinding of the bulk MoS2 and CNC powder followed by conventional liquid-phase exfoliation in water can not only efficiently exfoliate 2H-MoS2 nanosheets, but also produce the 2H-MoS2 /CNC 2D nanocomposites simultaneously. Interestingly, the intercalated CNC sandwiched in MoS2 nanosheets increases the interlayer spacing of 2H-MoS2 , providing perfect conditions to accommodate the large-sized ions. Therefore, these nanocomposites are good anode materials of potassium-ion batteries (KIBs), showing a high reversible capacity of 203 mAh g-1 at 200 mA g-1 after 300 cycles, a good reversible capacity of 114 mAh g-1 at 500 mA g-1 , and a low decay of 0.02% per cycle over 1500 cycles. With these impressive KIB performances, this efficient GPIE method will open up a new avenue to prepare pure-phase MoS2 and promising 2D nanocomposites for high-performance device applications.

DiSSCo (Distributed System of Scientific Collections) is a research infrastructure (RI) under development, which will provide services for the global research community to support and enhance physical and digital access to the natural history collections in Europe. These services include training, support, documentation and e-services. This talk will focus on the e-services and will give an overview of the current status, roadmap and first results as an introduction to the next talks in the session, which focus on some of the services in more detail and the standards work undertaken in Biodiversity Information Standards (TDWG) to enable them. The RI community will provide the envisioned e-services, which will use the novel FAIR Digital Object (FDO) infrastructure serving digital specimens from the European collections. The infrastructure will provide integrated data analysis, enhanced interpretation, annotation and access services for community curation and visualisation. The FDO infrastructure enables specimen data to be (re-)connected with genomic, geographical, morphological, taxonomic and environmental information through the digital specimen, making them Digital Extended Specimens. A large number of user stories have been collected through the DiSSCo-linked projects ICEDIG, SYNTHESYS+ and DiSSCo Prepare, to guide which e-Services to build and what functionality to provide. These user stories are publicly available in a github repository. The e-services are developed based on the user stories and prioritization provided by collection providers and the scientific community. A variety of mechanisms are used to collect input: surveys, workshops, roundtables and workpackage meetings, and feedback from users that have already been using beta versions of some of the services. DiSSCo aims to become operational in 2026 but several of the services are already being piloted or implemented. Experimental services and demonstrators are publicly available through DiSSCo Labs for testing and feedback. By connecting the specimen data with derived and related information in a FAIR way (Findable, Accessible, Interoperable and Reusable), the e-services will accelerate biodiversity discovery and support novel research questions. The FDO infrastructure has a data model that also integrates the PROV Ontology (PROV-O), which allows for the e-services to capture activities and improve the visibility of researcher contributions. This vision towards FAIR and high quality data is essential for community curation of the specimen data and making better use of the limited number of experts available. To provide the DiSSCo e-services in a FAIR way, the data derived from the natural history collections in Europe needs to be integrated as one virtual collection. The data has to be findable and accessible as soon as it is being created for services like a Specimen Data Refinery prior to publication in a facility like GBIF (Global Biodiversity Information Facility). This requires new standards for describing collections and specimen data. Standards being created to fill these gaps are TDWG CD (Collection Descriptions) and TDWG MIDS (Minimum Information about a Digital Specimen). The DiSSCo e-Services vision brings the data, standards, and processes together to serve the user community.

Accompanying material, text, data and figures for the article de Vargas et al., 'Eukaryotic plankton diversity in the sunlit ocean', Science 348, 1261605 (2015), doi: 10.1126/science.1261605

Our third newsletter including information about our main achievements during the second 6 months project period.

Cyberattacks are increasing in number and diversity in nature daily, and the tendency for them is to escalate dramatically in the forseeable future, with critical infrastructures (CI) assets and networks not being an exception to this trend. As time goes by, cyberattacks are more complex than before and unknown until they spawn, being very difficult to detect and remediate. To be reactive against those cyberattacks, usually defined as zero-day attacks, cyber-security specialists known as threat hunters must be in organizations’ security departments. All the data generated by the organization’s users must be processed by those threat hunters (which are mainly benign and repetitive and follow predictable patterns) in short periods to detect unusual behaviors. The application of artificial intelligence, specifically machine learning (ML) techniques (for instance NLP, C-RNN-GAN, or GNN), can remarkably impact the real-time analysis of those data and help to discriminate between harmless data and malicious data, but not every technique is helpful in every circumstance; as a consequence, those specialists must know which techniques fit the best at every specific moment. The main goal of the present work is to design a distributed and scalable system for threat hunting based on ML, and with a special focus on critical infrastructure needs and characteristics.

The challenges of precision neutrino physics require measurements of absolute neutrino cross sections at the GeV scale with exquisite (1%) precision. This precision is presently limited by the uncertainties on neutrino flux at the source; their reduction by one order of magnitude can be achieved monitoring the positron production in the decay tunnel originating from the $K_{e3}$ decays of charged kaons in a sign and momentum selected narrow band beam. This novel technique enables the measurement of the most relevant cross sections for CP violation ($ν_e$ and $\overlineν_e$) with a precision of 1% and requires a special instrumented beam-line. Such non-conventional beam-line will be developed in the framework of the ENUBET Horizon-2020 Consolidator Grant, recently approved by the European Research Council. The project, the first experimental results on ultra-compact calorimeters that can be embedded in the instrumented decay tunnel and the advances on the simulation of the beamline are presented. We also discuss the detector and accelerator activities that are planned in 2016-2021.