The wide adoption of occupational shoulder exoskeletons in industrial settings remains limited. Passive exoskeletons were proved effective in a limited amount of application scenarios, such as (quasi-)static overhead handling tasks. Quasi-active devices, albeit representing an improved version of their passive predecessors, do not allow full modulation of the amount of assistance delivered to the user, lacking versatility and adaptability in assisting various dynamic tasks. Active occupational shoulder exoskeletons could overcome these limitations by controlling the shape of the delivered torque profile according to the task they aim to assist. However, most existing active devices lack compactness and wearability. This prevents their implementation in working environments. In this work, we present a new active shoulder exoskeleton, named Active Exo4Work (AE4W). It features a new flexible shaft-driven remote actuation unit that allows the positioning of the motors close to the wearer’s center of mass while it maintains a kinematic structure that is compatible with the biological motion of the shoulder joint. in vitro and in vivo experiments have been conducted to investigate the performance of AE4W. Experimental results show that the exoskeleton is kinematically compatible with the user’s workspace since it does not constrain the natural range of motion of the shoulder joint. Moreover, this device can effectively provide different types of assistance while the user executes various dynamic tasks, without altering perceived comfort.

We analyze the disclosures of sustainable investing by Dutch pension funds in their annual reports by introducing a novel textual analysis approach using state-of-the-art natural language processing techniques to measure the awareness and implementation of sustainable investing. We find that a pension fund's size increases both the awareness and implementation of sustainable investing. Moreover, we analyze the role of signing a sustainable investment initiative. Although signing this initiative increases the specificity of pension fund statements about sustainable investing, we do not find an effect on the implementation of sustainable investing.

A detailed exploration is presented of the integration of human–machine collaboration in governance and policy decision-making, against the backdrop of increasing reliance on artificial intelligence (AI) and automation. This exploration focuses on the transformative potential of combining human cognitive strengths with machine computational capabilities, particularly emphasizing the varying levels of automation within this collaboration and their interaction with human cognitive biases. Central to the discussion is the concept of dual-process models, namely Type I and II thinking, and how these cognitive processes are influenced by the integration of AI systems in decision-making. An examination of the implications of these biases at different levels of automation is conducted, ranging from systems offering decision support to those operating fully autonomously. Challenges and opportunities presented by human–machine collaboration in governance are reviewed, with a focus on developing strategies to mitigate cognitive biases. Ultimately, a balanced approach to human–machine collaboration in governance is advocated, leveraging the strengths of both humans and machines while consciously addressing their respective limitations. This approach is vital for the development of governance systems that are both technologically advanced and cognitively attuned, leading to more informed and responsible decision-making.

At high elevations on the Greenland ice sheet meltwater percolates and refreezes in place, and hence does not contribute to mass loss. However, meltwater generation and associated surface runoff is occurring from increasingly higher altitudes, causing changes in firn stratigraphy that have led to the presence of near-surface ice slabs. These ice slabs force meltwater to flow laterally instead of percolating downwards. Here we present a simple, physics-based quasi-2-D model to simulate lateral meltwater runoff and superimposed ice (SI) formation on top of ice slabs. Using an Eulerian Darcy flow scheme, the model calculates how far meltwater can travel within a melt season and when it appears at the snow surface. Results show that lateral flow is a highly efficient runoff mechanism, as lateral outflow exceeds locally generated meltwater in all model gridcells, with total meltwater discharge sometimes reaching more than 30 times the average amount of in situ generated melt. SI formation, an important process in the formation and thickening of the ice slabs, can retain up to 40% of the available meltwater, and generally delays the appearance of visible runoff. Validating the model against field- or remote-sensing data remains challenging, but the results presented here are a first step towards a more comprehensive understanding and description of the hydrological system in the accumulation zone of the southwestern Greenland ice sheet.

Accurately quantifying all the components of the surface energy balance (SEB) is a prerequisite for the reliable estimation of surface melt and the surface mass balance over ice and snow. This study quantifies the SEB closure by comparing the energy available for surface melt, determined from continuous measurements of radiative fluxes and turbulent heat fluxes, to the surface ablation measured on the Greenland ice sheet between 2003 and 2023. We find that the measured daily energy available for surface melt exceeds the observed surface melt by on average 18 ± 30 W m−2 for snow and 12 ± 54 W m−2 for ice conditions (mean ± SD), which corresponds to 46 and 10% of the average energy available for surface melt, respectively. When the surface is not melting, the daily SEB is on average closed within 5 W m−2. Based on the inter-comparison of different ablation sensors and radiometers installed on different stations, and on the evaluation of modelled turbulent heat fluxes, we conclude that measurement uncertainties prevent a better daily to sub-daily SEB closure. These results highlight the need and challenges in obtaining accurate long-term in situ SEB observations for the proper evaluation of climate models and for the validation of remote sensing products.

This paper explores the role of integrating behavioral science to refine human-AI interaction, essential for ensuring safety and efficiency. Advocating for empathetic, user-centric design, the paper illustrates how behavioral insights can effectively inform AI-integrated designs, making AI applications more intuitive and ethically aligned with diverse human needs. This approach can ultimately enrich interaction across systems, fostering a more harmonious human-AI synergy.

The comprehensive overview of Neogene lithostratigraphy in the eastern Netherlands dates back to the mid-1970s. In the present study, continuous gamma-ray logs and in situ sediment core samples from six boreholes in the area allowed palynological and mollusc analyses and wireline log-based correlation. These investigations were aimed at updating and revising the existing Neogene lithostratigraphy within the Stratigraphic Nomenclature of the Netherlands by integrating litho-, bio-, and sequence-stratigraphic approaches. The analytical results yielded the establishment of new holo- and lectostratotype sections, together with cross-border interregional correlation. The lithostratigraphic revision resulted in the modified definition of the Aalten, Eibergen and Delden members and the definition of two new proposed members: the Dale and Doetinchem members. The Ticheloven bed, removed in earlier studies, is proposed to be reinstated, and the Stemerdink Bed is upgraded in its hierarchical status to the Stemerdink member. All lithostratigraphic units are included in the present Miocene subdivision of the Groote Heide and Diessen formations, which include three recognisable unconformities: the Early-Miocene Unconformity (EMU), Mid-Miocene Unconformity (MMU) and Late-Miocene Unconformity (LMU). The new, revised, reintroduced and existing local lithostratigraphic units and sequences are discussed with their counterparts in the south and southeast of the Netherlands, in Germany and Belgium, and from this a regionally consistent framework has emerged of the regional Neogene lithostratigraphy.

We recently reported on the radio-frequency attenuation length of cold polar ice at Summit Station, Greenland, based on bi-static radar measurements of radio-frequency bedrock echo strengths taken during the summer of 2021. Those data also allow studies of (a) the relative contributions of coherent (such as discrete internal conducting layers with sub-centimeter transverse scale) vs incoherent (e.g. bulk volumetric) scattering, (b) the magnitude of internal layer reflection coefficients, (c) limits on signal propagation velocity asymmetries (‘birefringence’) and (d) limits on signal dispersion in-ice over a bandwidth of ~100 MHz. We find that (1) attenuation lengths approach 1 km in our band, (2) after averaging 10 000 echo triggers, reflected signals observable over the thermal floor (to depths of ~1500 m) are consistent with being entirely coherent, (3) internal layer reflectivities are ≈–60$\to$–70 dB, (4) birefringent effects for vertically propagating signals are smaller by an order of magnitude relative to South Pole and (5) within our experimental limits, glacial ice is non-dispersive over the frequency band relevant for neutrino detection experiments.

A wide variety of language skills has been shown to be compromised in children from low socioeconomic status (SES). However, few studies have investigated the effect of SES on language development in infants. The aim of this study is two-fold: to investigate when the first SES-effects on language can be observed and to explore the effects of three variables often claimed to be linked to SES – gestational duration, stress and parent-child interaction – on language development. Parents/caregivers of 539 Dutch-acquiring infants aged 8-13 months from mid to high SES backgrounds completed a questionnaire including the LENA Developmental Snapshot (Gilkerson et al., 2017a) and the Brigance Parent-Child Interaction Scale (Glascoe & Brigance, 2002). No association was found between SES and language development. However, the results suggest that corrected age and parent-child interaction positively influence language development at this early age.

Ground penetrating radar (GPR) has been extensively used in glaciology to infer glacier's ice thickness, liquid water content, water drainage pathways, and other properties. The interpretation of such GPR data is not always straightforward and for temperate glaciers, the signal is often affected by strong scattering and attenuation. It has often been suggested that such effects originate from englacial water inclusions, since water and ice have a large contrast in their di-electric permittivity. To investigate such effects quantitatively, we perform an extensive numerical modeling study of GPR signals. By exploring how different liquid water contents (LWC) and water-inclusions size affect the GPR signal, we show that their effects are much larger than the potential presence of a wet snowpack or a heterogeneous distribution of ice permittivity. In particularly, we show that the presence of such water inclusions is a necessary and sufficient condition for reproducing the typical characteristics of GPR data acquired in the field. Further, we find that for 25 MHz GPR antennas, a bulk LWC $\gtrsim$ 0.2%, associated with decimeters-scale water inclusions already limits bedrock detectability for ice thicknesses $\gtrsim 100$ m. Since these values are typical for Alpine glaciers, they clarify why the quality of GPR data is often poor in such environments.

We introduce a conjecture on Virasoro constraints for the moduli space of stable sheaves on a smooth projective surface. These generalise the Virasoro constraints on the Hilbert scheme of a surface found by Moreira and Moreira, Oblomkov, Okounkov and Pandharipande. We verify the conjecture in many nontrivial cases by using a combinatorial description of equivariant sheaves found by Klyachko.

Part of the generalizability issues that haunt controlled lab experiment designs in psychology, and more particularly in psycholinguistics, can be alleviated by adopting corpus linguistic methods. These work with natural data. This advantage comes at a cost: in corpus studies, lexemes and language users can show different kinds of skew. We discuss a number of solutions to bolster the control.