With the rise of teleworking during the past decades, the impacts of teleworking on job satisfaction have been extensively debated. Teleworking might benefit workers by improving work-life balance and emotional well-being, but it also brings considerable challenges. This study empirically investigates the impacts of teleworking on workers’ enjoyment across daily working episodes and job satisfaction and its gendered patterns, using Ordinary Least Squares regressions and the latest nationally representative time-use survey data in the UK. Moreover, it uses the Karlson/Holm/Breen (KHB) decomposition method to examine the role of enjoyment at work in mediating the associations between teleworking and job satisfaction. Overall, this study yields two major findings. First, among men, teleworkers tend to have higher levels of enjoyment at work and job satisfaction, but this is not the case for women. Second, around 46% of teleworking’s positive impacts on men’s job satisfaction can be explained by higher levels of enjoyment at work. Taken together, by integrating different theoretical perspectives on teleworking, gender and emotional well-being, this study provides interdisciplinary insights into the nuanced social consequences of teleworking, highlights the disadvantaged position of women in the use of teleworking, and demonstrates the need to enhance emotional well-being in future labour market policies.

Following the significant success of the transformer in NLP and computer vision, this paper attempts to extend it to 3D triangle mesh. The aim is to determine the shape’s global representation using the transformer and capture the inherent manifold information. To this end, this paper proposes a novel learning framework named Navigation Geodesic Distance Transformer (NGD-Transformer) for 3D mesh. Specifically, this approach combined farthest point sampling with the Voronoi segmentation algorithm to spawn uniform and non-overlapping manifold patches. However, the vertex number of these patches was inconsistent. Therefore, self-attention graph pooling is employed for sorting the vertices on each patch and screening out the most representative nodes, which were then reorganized according to their scores to generate tokens and their raw feature embeddings. To better exploit the manifold properties of the mesh, this paper further proposed a novel positional encoding called navigation geodesic distance positional encoding (NGD-PE), which encodes the geodesic distance between vertices relatively and spatial symmetrically. Subsequently, the raw feature embeddings and positional encodings were summed as input embeddings fed to the graph transformer encoder to determine the global representation of the shape. Experiments on several datasets were conducted, and the experimental results show the excellent performance of our proposed method.

The analysis of 3D meshes with deep learning has become prevalent in computer graphics. As an essential structure, hierarchical representation is critical for mesh pooling in multiscale analysis. Existing clustering-based mesh hierarchy construction methods involve nonlinear discretization optimization operations, making them nondifferential and challenging to embed in other trainable networks for learning. Inspired by deep superpixel learning methods in image processing, we extend them from 2D images to 3D meshes by proposing a novel differentiable chart-based segmentation method named geodesic differential supervertex (GDSV). The key to the GDSV method is to ensure that the geodesic position updates are differentiable while satisfying the constraint that the renewed supervertices lie on the manifold surface. To this end, in addition to using the differential SLIC clustering algorithm to update the nonpositional features of the supervertices, a reparameterization trick, the Gumbel-Softmax trick, is employed to renew the geodesic positions of the supervertices. Therefore, the geodesic position update problem is converted into a linear matrix multiplication issue. The GDSV method can be an independent module for chart-based segmentation tasks. Meanwhile, it can be combined with the front-end feature learning network and the back-end task-specific network as a plug-in-plug-out module for training; and be applied to tasks such as shape classification, part segmentation, and 3D scene understanding. Experimental results show the excellent performance of our proposed algorithm on a range of datasets.

The transition to first parenthood is a critical juncture at which both paid and unpaid work become more gendered within couples. From a life course perspective, parenthood is not a discrete event but a process that couples traverse together across successive phases. Far less is known, however, about how the gendered division of labour and its implications for the within-couple pay gap evolve across subsequent phases of parenthood. Using longitudinal data from UK dual-earner couples (1996–2023), this study examines gender dynamics across four parenthood phases using couple-level fixed-effects and Karlson–Holm–Breen (KHB) mediation analysis. First, we find that the initial shock of parenthood is not temporary. Gendered divisions become entrenched in the established phase and re-intensify with subsequent births, while male partners’ labour contribution remains virtually unchanged. Second, the KHB decomposition reveals that the asymmetric division of labour significantly accounts for shifts in female partners’ relative hourly pay across phases: The female partner’s share of paid work operates as a suppressor that partially offsets negative direct effects, whereas her share of housework further reinforces relative pay disadvantage. This study contributes by (1) providing a multi-phase, couple-level account of how gendered arrangements unfold as families move through parenthood, bearing on ongoing debates between resource-based and gender-based explanations of couples’ division of labour, and (2) testing the mediation pathway linking parenthood phases to within-couple pay inequality and identifying when across the parenthood trajectory the reallocation of labour most strongly accounts for this inequality.

Generative AI (GenAI) has diffused rapidly across Europe, yet who feels able touse it—and who is willing to work with it—remains poorly understood. Using thelatest CRONOS-3 panel of the probability-based European Social Survey (N =7,556; 11 European countries, fielded in 2025), we model six separate measuresof GenAI literacy and use frequency, together with acceptance of GenAI as anassistant and as a peer in the workplace, using country-clustered estimators withwild-bootstrap inference. Acceptance is strongly role-differentiated: a majorityaccept GenAI as an assistant, but only one in ten as a peer. Both competence andacceptance are sharply stratified by education, age, and gender—and the genderdivide is the most consistent pattern of all: women trail men on every one of thesix literacy and use measures and on both acceptance outcomes, and the deficitappears in behavioural use as well as in self-assessed skill, so it cannot be reducedto gendered self-assessment alone. GenAI literacy and use frequency in turnstrongly predict acceptance in both roles. Cross-national variation is substantial,but with only 11 countries no association between national media, scholarly, orAI-talent environments and individual outcomes survives strict small-cluster andmultiple-testing corrections, so national context is treated descriptively. GenAIcompetence and workplace acceptance thus emerge as socially stratified from theoutset—with women consistently furthest behind—an early-forming divide thatrisks hardening as GenAI becomes workplace infrastructure.