Examining use of restaurant nutrition information among adults living in England engaged in disordered eating or weight management efforts

Background Calorie labels on menus are a popular policy initiative to help consumers to reduce their energy intake. Consumers trying to improve their diet may benefit from the labels if they help inform eating decisions. However, it is not well known how some groups, such as those with disordered eating, may view a calorie labelling policy and engage with nutritional labels in England. Methods Using 2021 International Food Policy Study data from 2,898 adults in England, we assessed policy support for menu labels, participants’ noticing and influence of nutrition information, and behavioural impacts based on nutrition information labelling at restaurants. Using logistic regression models, we explored whether there were differences in responses between participants engaged in disordered eating or weight management efforts compared to those who engaged in neither. Results A majority (56.9%) of participants supported a calorie labelling policy. People reporting disordered eating behaviours had greater odds of noticing, being influenced by, and changing behaviour because of nutritional information compared with participants not engaging in disordered eating or weight management. Participants with a preoccupation with thinness had greater odds of supporting calorie labelling, whereas those engaging in binge eating did not differ in odds and those reporting self-induced vomiting had lower odds of supporting the policy. Compared to the reference group, weight loss and weight maintenance groups had greater odds of being influenced by nutrition information and of eating out less often. Of the weight management groups, only participants reporting weight loss efforts had greater odds of ordering something different and of supporting a calorie labelling policy. Conclusion As one of the first quantitative studies in England to examine engagement with restaurant nutrition information among adults with disordered eating and weight management efforts, this study indicates that nutrition information had differential impacts among those with disordered eating or engaging in weight management. England implemented a mandatory calorie labelling policy for the out-of-home food sector after the current data were collected; future studies should examine how the policy influences eating decisions and consider potential impacts on people with disordered eating

Jacobian Scopes: token-level causal attributions in LLMs

Preprint versionLarge language models (LLMs) make nexttoken predictions based on clues present in their context, such as semantic descriptions and incontext examples. Yet, elucidating which prior tokens most strongly influence a given prediction remains challenging due to the proliferation of layers and attention heads in modern architectures. We propose Jacobian Scopes, a suite of gradient-based, token-level causal attribution methods for interpreting LLM predictions. Grounded in perturbation theory and information geometry, Jacobian Scopes quantify how input tokens influence various aspects of a model's prediction, such as specific logits, the full predictive distribution, and model uncertainty (effective temperature). Through case studies spanning instruction understanding, translation, and in-context learning (ICL), we demonstrate how Jacobian Scopes reveal implicit political biases, uncover word-and phrase-level translation strategies, and shed light on recently debated mechanisms underlying in-context time-series forecasting. To facilitate exploration of Jacobian Scopes on custom text, we open-source our implementations and provide a cloud-hosted interactive demo at https://huggingface.co/spaces/ Typony/JacobianScopes

Direct evaluation of the electrocardiographic spatial QRS-T angle without the need for orthogonal transformation

Increased electrocardiogram (ECG) spatial QRS-T wave angle is a recognised risk factor. Standard evaluation of the angle requires deriving orthogonal ECG leads, either by general transformation matrices into XYZ leads or by singular value decomposition (SVD). This study shows that the transformation is not needed, and that the spatial QRS-T angle can be calculated directly from the original ECG leads. The direct computation was tested using long-term 12-lead ECGs of 523 healthy volunteers (259 females). A total of 659,313 individual 10-second ECG samples were obtained providing 7,350,733 individual beats which were analysed both by the direct method using 8 algebraically independent leads and by the conventional XYZ and SVD transformations. On average, the results of the direct non-transformation method were closer to the SVD-based results (averaged differences below 1 degree) than to the XYZ-based results (averaged differences below 2 degrees). The subject-specific regressions to the underlying heart rate showed that the proposed direct method was significantly more reproducible (p < 0.0001) and that it showed more compact variability within individual ECG samples (p < 0.0001). Thus, the study shows not only that the QRS-T angle can be computed without any orthogonal transformation but that the results of the direct computation are also more precise

Vision article: new advances in gas-solids fluidized beds for novel thermochemical reaction processes and energy storage

This vision article accompanies a Special Issue of Applied Thermal Engineering dedicated to the 14th International Conference on Circulating Fluidized Bed Technology (CFB-14), held in Taiyuan, China from 21 to 24 July 2024. Drawing upon a collection of contributions from the conference, which have been included in the Special Issue, and a selection of recent studies in the published literature, this article synthesizes recent advances in gas-solids fluidized bed technology, with an emphasis on circulating fluidized beds for thermochemical conversion processes and energy storage, highlighting emerging trends and critical research directions. The article also explores carbon management pathways, including biomass co-firing, oxy-fuel combustion, and chemical looping combustion, emphasizing their roles in reducing CO₂ emissions and enhancing process efficiency. Based on this review and an identification of the state-of-the-art in CFB technology, the article provides a perspective for scientists, researchers and engineers that outlines key challenges, gaps, and identifies promising directions for future research and innovation. Challenges related to advanced experimental methods and measurement accuracy, model development and validation, novel reactor design and technology scale-up, and operational flexibility are discussed. The integration of artificial intelligence and machine learning with physical models and multimodal sensing is identified as a transformative direction for real-time optimization, predictive control, and digital twin development. The paper concludes with a forward-looking perspective that underscores the need for advanced in-situ measurement techniques, multi-scale, multi-physics simulation frameworks, and hybrid AI-enhanced approaches to enable the next generation of efficient, flexible, and sustainable fluidized bed technologies for clean energy conversion and carbon neutrality

Multi-Level Monte Carlo training of neural operators

Operator learning is a rapidly growing field that aims to approximate nonlinear operators related to partial differential equations (PDEs) using neural operators. These rely on discretization of input and output functions and are, usually, expensive to train for large-scale problems at high-resolution. Motivated by this, we present a Multi-Level Monte Carlo (MLMC) approach to train neural operators by leveraging a hierarchy of resolutions of function dicretization. Our framework relies on using gradient corrections from fewer samples of fine-resolution data to decrease the computational cost of training while maintaining a high level accuracy. The proposed MLMC training procedure can be applied to any architecture accepting multi-resolution data. Our numerical experiments on a range of state-of-the-art models and test-cases demonstrate improved computational efficiency compared to traditional single-resolution training approaches, and highlight the existence of a Pareto curve between accuracy and computational time, related to the number of samples per resolution

Quantifying and regionalizing land use impacts on catchment response times with high-frequency observations

Land use and land cover change (LUCC) can affect the hydrological response time of rivers. However, it is difficult to generate robust and quantitative evidence of this impact at the catchment scale. This lack of evidence also affects the development of rainfall-runoff models to make ex-ante predictions. Here, we analyze high-frequency observational data from a network of pairwise catchments in the tropical Andes and find a statistically significant impact of intensive land use on the hydrological response time, which can be used for regionalization. First, we isolated individual rainfall response events from 5-minute precipitation and discharge time series of 16 catchments (8 pairs). We then fitted unit hydrographs on these events to estimate the catchment response times. These response times were subsequently regionalized by, first, applying a forward stepwise regression to select statistically significant catchment characteristics including land use and land cover, then, fitting a linear mixed-effects model with the selected characteristics to account for within-site variability between pairs. We find that catchments with intensive land use have a significantly quicker response than their natural counterparts. Differences were often sub-hourly, highlighting the value of high-frequency monitoring. Forward stepwise regression identified only catchment area and intensive land use percentage as statistically significant predictors. Model coefficients show that, even when considering other catchment characteristics, increasing intensive land use percentage decreases response times. This study provides solid evidence and a robust methodology to quantify the impacts of LUCC on catchment hydrology

The protective stepping response in patients with bilateral vestibular hypofunction

Objective: Protective stepping following postural instability is a defence mechanism that prevents falls. Vestibular patients have increased risk of falling but little is known about their stepping response. Here, we investigate whether the protective stepping response is preserved in patients with bilateral vestibular hypofunction (BVH). Methods: This cross-sectional study, conducted in a balance-research facility, measured body sway and protective stepping responses during a dynamic postural task (platform oscillations at different velocities). Patients diagnosed with BVH were recruited from neuro-otology clinics. As stepping may be dictated by instability perception, objective sway and subjective instability were also analysed for each participant. Results: 12 patients (4 males, age:65.1, SD:14.2 years) and 12 healthy age and gender matched controls (age:64.8, SD:5.3) were recruited. Patients swayed more than controls (t:-2.153,p=0.03,d=-0.39) and showed marginally steeper objective-subjective instability curves than controls (t:-2.082,p=0.053,d=-0.85), meaning they felt slightly more unstable than controls for the same amount of sway. However, stepping velocity thresholds (t:-1.013,p=0.324,d=0.45) and latencies (t:0.062,p=0.951,d=-0.02) were not different between patients and controls. Discussion: These results indicate that the protective stepping response is preserved in BVH patients, hence not critically dependent on vestibular input. Only chronic patients were included, which limits the generalisation of the results to acute phases of the vestibular loss

Human-robot cooperation for teleoperation in robotic surgery

This thesis investigates data-driven methods for implementing human-robot interaction in the da Vinci Research Kit (dVRK) platform, with a particular focus on skill modeling, integration of autonomy in the surgical workflow, and real-time performance assessment. As robotic surgery becomes increasingly prevalent, there is a growing need for intelligent systems that can both assist human operators and also understand and adapt to their actions in real-time. This work addresses this challenge by developing a high performing control framework through a sequence of studies that span through the different types of controllers in the spectrum of teleoperative human-robot cooperation and skill assessment: shared control, supervisory control, and assistive control. Early chapters focus on shared control and supervisory control frameworks, introducing novel methods for learning bimanual surgical trajectories from demonstrations, enabling robots to generalise motion patterns from expert behavior. The subsequent work explores control paradigms that modulate robotic assistance in a data-driven approach, balancing autonomy and human input to achieve both higher performances and lower perceived workload. Objective evaluations demonstrate that adaptive assistance can benefit novice users without significantly impeding expert performance. Building upon these foundations, the last chapter shifts focus to real-time surgical skill assessment using deep learning models trained on multimodal inputs. These systems are designed to provide frame-level predictions of technical skill, enabling continuous feedback during task execution. Across all studies, this thesis emphasises the integration of expert data, task structure, and real-time capabilities to build responsive surgical systems that can fit into the surgical workflow. The contributions are validated with user studies and supported by extensive experimentation on datasets. User studies with participants show improved performance and perceived workload throughout all experiments when using the proposed control systems. Together, these contributions advance the state-of-the-art in human-robot cooperation and provide a foundation for intelligent systems in surgical assistance.Open Acces

Perspectives on the future of skin-prosthetic interactions

Skin is filled with a rich network of sensory neurons and consequently skin interaction with, and perception of the external environment, are a continual phenomenon experienced by all people in society. For medical devices such as prosthetics, the mechanical interaction between skin and the device is an essential aspect for device function, but it must be minimised to ensure continued skin health which is inherently intertwined with biological processes. Numerous factors influence the interface properties of skin. For example hydration, which influences skin morphology and composition, in turn influencing skin mechanics that may lead to tissue inflammation and skin injury. Here we review the current state-of-the-art in skin medical device tribology with a particular focus on skin-prosthetic interactions. We split this article into traditional approaches to skin friction and a biology first approach to skin friction, with a paradigm shift of skin as an engineered material. The field of tribology has historically been an interdisciplinary field comprising engineers, chemists and physicists, but future developments are needed in skin biology to drive meaningful change. We envisage that the integration of both clinical and biological perspectives will drive future innovations towards improved medical device interactions with the skin, when paired with engineering perspectives