In this paper, we present the Amsterdam Modeling Suite (AMS), a comprehensive software platform designed to support advanced molecular and materials simulations across a wide range of chemical and physical systems. AMS integrates cutting-edge quantum chemical methods, including Density Functional Theory (DFT) and time-dependent DFT, with molecular mechanics, fluid thermodynamics, machine learning techniques, and more, to enable multi-scale modeling of complex chemical systems. Its design philosophy allows for seamless coupling between components, facilitating simulations that range from small molecules to complex biomolecular and solid-state systems, making it a versatile tool for tackling interdisciplinary challenges, both in industry and in academia. The suite also emphasizes user accessibility, with an intuitive graphical interface, extensive scripting capabilities, and compatibility with high-performance computing environments.

The goal of the NEUROKIT2E project is to create an open-source Deep Learning framework for edge and embedded AI built around an established European value chain. This framework, called AIDGE, supports a wide range of application areas that operate independently and serve a global user community. It provides easy and fast full-stack solutions from Neural Network design and optimization to AI application development all the way down to hardware implementations while enabling code generation for application-specific targets. This platform provides flexibility for academic users in the AI domain to explore and innovate while allowing them the possibility to prototype systems, ensuring their work aligns well with industrial needs. This paper presents the results and achievements of the first part of this three-year project, along with its roadmap and expected outcomes.

Lake Hulun, the fifth-largest lake in China, is a shallow lake (water depth <10 m) with typical wave-dominated landforms developed around the shoreline, with a semi-enclosed bay located in its southern corner. This novel study aims to understand wind-driven hydrodynamics and its related depositional patterns in the data-sparse Lake Hulun. To achieve this, a series of numerical simulations were conducted with a hydrodynamic and sediment transport model. The simulated hydrodynamic patterns are greatly influenced by wind direction shifts but are subject to little impact from wind speed changes which act mainly to accelerate flow. By varying the location and depth of the deepest part of the lake, this study reveals that the location of the depth centre has little impact on the overall hydrodynamic pattern of wind-driven waterbodies. When the wind direction is perpendicular to the long-axis shore, currents around the short-axis shore flow in a direction that follows the wind direction. This study considers the wind-induced longshore currents that are oblique to the long-axis shore as the main driving force in transporting sediments along the shore and erosion of the shoreline. The formation of semi-closed bays in both Lake Hulun, together with its nearby sister lake – Lake Buir – are attributed to the north-west prevailing wind direction. Further exploratory simulations confirmed that prevailing winds tend to induce parallel distributed submerged sediment accumulations in the nearshore zone, challenging the notion of sediment accumulation solely in deep water zones. This study provides valuable insights into the hydro-sedimentary dynamics in wind-driven waterbodies, offering a process-based perspective and contributing to current understanding of the palaeogeography of ancient lake systems.

We propose the concept of a problem-sustaining pattern as a revision of the established concept of mental disorder. The proposed concept preserves valuable features of the established concept, such as recognition of the client’s hardships and scientifically informed justification of specific interventions. However, several assumptions behind the established concept have been widely criticized, both in terms of their clinical and moral normativity as well as their ontological and empirical soundness. We argue that a focus on problem-sustainment allows us to reframe the issue of demarcation in a way that helps avoid stigmatization while clarifying the role of client agency in diagnosis. We also propose a shift toward thinking in terms of patterns of dynamic interaction, which is more in line with current developments in complexity science. We conclude the article with a discussion of further research that would be needed to address various questions raised by our proposal.

Studies on the total cost of ownership (TCO) of electric vehicles (EVs) have become a crucial topic in academic research. As EVs have gained prominence globally, the volume of research on this subject has increased. However, previous review studies have not focused on examining trends in research that estimates the TCO for all types and technologies of EVs. Thus, our study aims to fill this gap by reviewing global trends and results of TCO studies on EVs. Our review employs the method of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically collect and analyse 185 articles from 2011 to 2024. The contributions of our review are fourfold. Firstly, we construct a thematic research map to understand which topics are densely connected, thereby highlighting the core themes and focal points of the research field. As a result, future research should focus on reducing the TCO to accelerate EV adoption. Secondly, we show that the most extensively analysed vehicle technologies are battery electric vehicles (BEVs), while passenger cars are the most studied vehicle type. Thirdly, we differentiate the objectives of the TCO studies, identifying motivation gaps among them. We found that the primary objectives of the reviewed studies are TCO comparisons, followed by battery size and powertrain designs, policy scenario analysis, and TCO model developments. Lastly, we provide the range of TCO estimates for different types and technologies of vehicles. The average TCO for BEVs is found to be higher than that for internal combustion engine vehicles (ICEVs) across passenger cars, buses, trucks, and cargo vans. Electric motorcycles, however, buck this trend, with their average TCO being slightly lower than that of conventional motorcycles. Based on the review results, we propose several recommendations for policy implications and future studies.

Energy Quay Walls (EQWs) are innovative energy geostructures which exchange thermal energy with both soil and open water while providing a structural function. A full-scale EQW with thermally activated sheet piles was tested, measuring 8.4 m in length along a 1.75m deep canal, with the sheet piles embedded 13m into the underlying soil. Two different length heat exchangers were used: shallow (3 m length) loops primarily extracting thermal energy from the open water, and deep (15 m length) loops extracting energy also from the soil. The shallow loops demonstrated a high heat extraction rate per activated surface area (∼200 W/m2 at 8 °C water temperature, compared with ∼60 W/m2 for the deep loops), with their performance closely linked to the open water temperature. The shallow loops did not require time to restore surrounding temperatures, indicating stable long-term performance, yet can extract the least energy at the coldest time periods. In contrast, the deeper loops exhibit greater stability across varying open water temperatures and achieve the highest total energy extraction per quay wall length (∼900 W/m at 8 °C water temperature, compared with ∼600 W/m for the shallow loops). Realistic operation of the deep loops lowered the soil temperature by ∼2 °C.

Nature Communications is publishing an editorial expression of concern on the article “Ballistic superconductivity in semiconductor nanowires”, by H. Zhang et al. On 09 December 2021, the Editorial Staff was alerted by Vincent Mourik and two other researchers to potential problems in the manner in which raw data have been selected, processed and analysed. In response to these concerns, Nature Communications initiated an investigation by contacting the corresponding authors of the article and consulting with two independent experts. The investigation involved technical scrutiny of the additional analyses provided by the corresponding authors, including supplementary data from the repository https://zenodo.org/records/6851435. Based on the evidence presented, the Reviewers endorsed the publication of the correction note appended below. Readers are urged to take this information into consideration when interpreting the data presented in this article. Kun Zuo and Vincent Mourik also informed the editorial staff that they wished to be removed from authorship because in their opinion, the correction does not address the concerns with respect to the data and they do not endorse the validity of the claims and conclusions of the article. The author list in both the PDF and HTML has now been rectified. All authors,with the exception ofKenjiWatanabe and Takashi Taniguchi, disagreewith the publication of this Editorial Expression of Concern.

Engineered heart tissues (EHTs) have shown great potential in recapitulating tissue organization, functions, and cell-cell interactions of the human heart in vitro. Currently, multiple EHT platforms are used by both industry and academia for different applications, such as drug discovery, disease modelling, and fundamental research. The tissues’ contractile force, one of the main hallmarks of tissue function and maturation level of cardiomyocytes, can be read out from EHT platforms by optically tracking the movement of elastic pillars induced by the contractile tissues. However, existing optical tracking algorithms which focus on calculating the contractile force are customized and platform-specific, often not available to the broad research community, and thus hamper head-to-head comparison of the model output. Therefore, there is the need for robust, standardized and platform-independent software for tissues’ force assessment. To meet this need, we developed ForceTracker: a standalone and computationally efficient software for analyzing contractile properties of tissues in different EHT platforms. The software uses a shape-detection algorithm to single out and track the movement of pillars’ tips for the most common shapes of EHT platforms. In this way, we can obtain information about tissues’ contractile performance. ForceTracker is coded in Python and uses a multi-threading approach for time-efficient analysis of large data sets in multiple formats. The software efficiency to analyze circular and rectangular pillar shapes is successfully tested by analyzing different format videos from two EHT platforms, developed by different research groups. We demonstrate robust and reproducible performance of the software in the analysis of tissues over time and in various conditions. ForceTracker’s detection and tracking shows low sensitivity to common incidental defects, such as alteration of tissue shape or air bubbles. Detection accuracy is determined via comparison with manual measurements using the software ImageJ. We developed ForceTracker as a tool for standardized analysis of contractile performance in EHT platforms to facilitate research on disease modeling and drug discovery in academia and industry.

The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected -- information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial–temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.

To better understand the increasing human impact on the water cycle and the feedbacks between hydrology and society, the International Association of Hydrological Sciences (IAHS) organized the scientific decade “Panta Rhei–Everything Flows: Change in hydrology and society” (2013–2022). A key finding is the need to use integrated approaches to assess the co-evolution of human–water systems in order to avoid unintended consequences of human interventions over long periods of time. Additionally, substantial progress has been made in leveraging new data sources on human behaviour, e.g. through text mining of social media posts. Much has been learned about detecting hydrological changes and attributing them to their drivers, e.g. quantifying climate effects on floods. To achieve further progress, we recommend broadening the understanding, the discipline and training activities, while at the same time pursuing synthesis by focusing on key themes, developing innovative approaches and finding sustainable solutions to the world’s water problems.

The European Union's goal of achieving climate neutrality by 2050, outlined in the European Green Deal, is supported by numerous studies providing insights into pathways and emission reduction strategies in the energy sectors. However, model comparisons of such pathways are less common due to the complex nature of climate and energy modelling. Our study brings together integrated assessment models and energy system models under a common framework to develop EU policy scenarios: a Current Trends scenario reflecting existing policies and trends and a Climate Neutrality scenario aligned with the EU's emission reduction target. Both scenarios project reduced final energy consumption by 2050, driven by increased electrification and decreased fossil fuel usage. Electricity consumption increases driven by electrification despite the improved efficiency of electrified technologies. Models align on a shift toward renewables but diverge in technology and fuel choices, reflecting various approaches to reach net-zero energy systems. Furthermore, trade-offs between energy demand and supply mitigation strategies, as well as between renewable energy, e-fuels, and CCS technologies are identified. Considering these model variations, our study highlights the importance of consistent model comparison to offer reliable recommendations to policymakers and stakeholders. We conclude that model diversity is a valuable asset when used sensibly.

Digital counselling can alleviate the burden on healthcare systems and patients. While it has been evaluated as a supplement to standard care or a substitute for follow-up visits, its use for initial triaging and counselling remains unstudied. We developed a Digital Intake Tool (DIT) to facilitate the entire pre-colonoscopy counselling process for FIT-positive participants of a colorectal cancer screening program digitally, replacing the need for physicians. In this multicentre prospective non-inferiority study, we evaluated if the DIT could replace in-person counselling. DIT-counselling resulted in adequately prepared participants in 96.5%, compared to 97.6% after in-person counselling, demonstrating non-inferiority. Outpatient visits were significantly reduced, with only 3.4% requiring face-to-face consultations. Patient experiences were highly positive, without increased psychological distress or anxiety, and effective knowledge transfer. This approach benefits patients and healthcare systems, allowing patients to receive care at home, reducing travel and carbon emissions, while increasing outpatient capacity. ICTRP-registration: NL9315, March 8, 2021.

This Roadmap surveys the diversity of different approaches for characterising, modelling and designing metamaterials. It contains articles covering the wide range of physical settings in which metamaterials have been realised, from acoustics and electromagnetics to water waves and mechanical systems. In doing so, we highlight synergies between the many different physical domains and identify commonality between the main challenges. The articles also survey a variety of different strategies and philosophies, from analytic methods such as classical homogenisation to numerical optimisation and data-driven approaches. We highlight how the challenging and many-degree-of-freedom nature of metamaterial design problems call for techniques to be used in partnership, such that physical modelling and intuition can be combined with the computational might of modern optimisation and machine learning to facilitate future breakthroughs in the field.

TiO₂ nanoparticles (NPs) are extensively used in various applications, highlighting the importance of ongoing research into their effects. This work belongs among rare whole-body inhalation studies investigating the effects of TiO₂ NPs on mice. Unlike previous studies, the concentration of TiO₂ NPs in the inhalation chamber (130.8 μg/m³) was significantly lower. This 11-week study on mice confirmed in vivo the presence of TiO₂ NPs in lung macrophages and type II pneumocytes including their intracellular localization by using the electron microscopy and the state-of-the-art methods detecting NPs' chemical identity/crystal structure, such as the energy-dispersed X-ray spectroscopy (EDX), cathodoluminescence (CL), and detailed diffraction pattern analysis using powder nanobeam diffraction (PNBD). For the first time in inhalation study in vivo, the alterations in erythrocyte morphology with evidence of echinocytes and stomatocytes, accompanied by iron accumulation in spleen, liver, and kidney, are reported following NP's exposure. Together with the histopathological evidence of hyperaemia in the spleen and kidney, and haemosiderin presence in the spleen, the finding of NPs containing iron might suggest the increased decomposition of damaged erythrocytes. The detection of TiO₂ NPs on erythrocytes through CL analysis confirmed their potential systemic availability. On the contrary, TiO₂ NPs were not confirmed in other organs (spleen, liver, and kidney); Ti was detected only in the kidney near the detection limit.

Dual reporters encoding two distinct proteins within the same mRNA have had a crucial role in identifying and characterizing unconventional mechanisms of eukaryotic translation. These mechanisms include initiation via internal ribosomal entry sites (IRESs), ribosomal frameshifting, stop codon readthrough and reinitiation. This design enables the expression of one reporter to be influenced by the specific mechanism under investigation, while the other reporter serves as an internal control. However, challenges arise when intervening test sequences are placed between these two reporters. Such sequences can inadvertently impact the expression or function of either reporter, independent of translation-related changes, potentially biasing the results. These effects may occur due to cryptic regulatory elements inducing or affecting transcription initiation, splicing, polyadenylation and antisense transcription as well as unpredictable effects of the translated test sequences on the stability and activity of the reporters. Unfortunately, these unintended effects may lead to misinterpretation of data and the publication of incorrect conclusions in the scientific literature. To address this issue and to assist the scientific community in accurately interpreting dual-reporter experiments, we have developed comprehensive guidelines. These guidelines cover experimental design, interpretation and the minimal requirements for reporting results. They are designed to aid researchers conducting these experiments as well as reviewers, editors and other investigators who seek to evaluate published data.

We present our experimental results on ultrasound transducers based on Photonic Integrated Circuits. We have fabricated and tested devices based on Mach Zehnder Interferometers and Ring Resonators, in the thick-Silicon on Insulator platform (VTT, Finland) and Si3N4 platform (Ligentec, Switzerland). We have obtained a Noise Equivalent Pressure which is two orders of magnitude lower than conventional State-Of-The-Art transducers, clearly demonstrating the huge potential of this concept.

Herein, the effect of the Ru:Ni bimetallic composition in dual-function materials (DFMs) for the integrated CO2 capture and methanation process (ICCU-Methanation) is systematically evaluated and combined with a thorough material characterization, as well as a mechanistic (in-situ diffuse reflectance infrared fourier-transform spectroscopy (in-situ DRIFTS)) and computational (computational fluid dynamics (CFD) modelling) investigation, in order to improve the performance of Ni-based DFMs. The bimetallic DFMs are comprised of a main Ni active metallic phase (20 wt%) and are modified with low Ru loadings in the 0.1–1 wt% range (to keep the material cost low), supported on Na2O/Al2O3. It is shown that the addition of even a very low Ru loading (0.1–0.2 wt%) can drastically improve the material reducibility, exposing a significantly higher amount of surface-active metallic sites, with Ru being highly dispersed over the support and the Ni phase, while also forming some small Ru particles. This manifests in a significant enhancement in the CH4 yield and the CH4 production kinetics during ICCU-Methanation (which mainly proceeds via formate intermediates), with 0.2 wt% Ru addition leading to the best results. This bimetallic DFM also shows high stability and a relatively good performance under an oxidizing CO2 capture atmosphere. The formation rate of CH4 during hydrogenation is then further validated via CFD modelling and the developed model is subsequently applied in the prediction of the effect of other parameters, including the inlet H2 concentration, inlet flow rate, dual-function material weight, and reactor internal diameter.

The Ganymede Laser Altimeter (GALA) on the Jupiter Icy Moons Explorer (JUICE) mission, is in charge of a comprehensive geodetic mapping of Europa, Ganymede, and Callisto on the basis of Laser range measurements. While multiple topographic profiles will be obtained for Europa and Callisto during flybys, GALA will provide a high-resolution global shape model of Ganymede while in orbit around this moon based on at least 600 million range measurements from altitudes of 500 km and 200 km above the surface. By measuring the diurnal tidal deformation of Ganymede, which crucially depends on the decoupling of the outer ice shell from the deeper interior by a liquid water ocean, GALA will obtain evidence for (or against) a subsurface ocean on Ganymede and will provide constraints on the ice shell thickness above the ocean. In combination with other instruments, it will characterize the morphology of surface units on Ganymede, Europa, and Callisto providing not only topography but also measurements of surface roughness on the scale of the laser footprint, i.e. at a scale of about 50 m from 500 km altitude, and albedo values at the laser wavelength of 1064 nm. GALA is a single-beam laser altimeter, operating at a nominal frequency of 30 Hz, with a capability of reaching up to 48 Hz. It uses a Nd:YAG laser to generate pulses with pulse lengths of 5.5 ± 2.5 ns. The return pulse is detected by an Avalanche Photo Diode (APD) with 100 MHz bandwidth and the signal is digitized at a sampling rate of 200 MHz providing range measurements with a sub-sample resolution of 0.1 m. Research institutes and industrial partners from Germany, Japan, Switzerland and Spain collaborated to build the instrument. JUICE, conducted under responsibility of the European Space Agency (ESA), was successfully launched in April 2023 and is scheduled for arrival at the Jupiter system in July 2031. The nominal science mission including multiple close flybys at Europa, Ganymede, and Callisto, as well as the final Ganymede orbit phase will last from 2031 to 2035. In May 2023 GALA has completed its Near-Earth Commissioning, showing full functionality of all units. Here we summarize the scientific objectives, instrument design and implementation, performance, and operational aspects of GALA.

The unique properties of two-dimensional (2D) materials bring great promise to improve sensor performance and realise novel sensing principles. However, to enable their high-volume production, wafer-scale processes that allow integration with electronic readout circuits need to be developed. In this perspective, we review recent progress in on-chip 2D material sensors, and compare their performance to the state-of-the-art, with a focus on results achieved in the Graphene Flagship programme. We discuss transfer-based and transfer-free production flows and routes for complementary metal-oxide-semiconductor integration and prototype development. Finally, we give an outlook on the future of 2D material sensors, and sketch a roadmap towards realising their industrial and societal impact.