157082 research outputs found
Sort by
Crowdedness, Mispricing, Crashes, and Spikes
No full textThis study proposes “reflexive crowdedness” as a mechanism through which order flow can become toxic at ultra-high frequencies (UHFs). Crowdedness, a coordination problem arising from the inability of traders to accurately gauge competition, leads to significant unbalanced mispricing in the form of liquidity costs. This mispricing is amplified by (reflexive) feedforward loops between liquidity and price components and can accumulate rapidly when high-speed traders engage. We develop an empirical framework to examine this mechanism in UHF trading. Results on trades of Dow 30 stocks show that reflexive crowdedness triggers speculative algorithmic trading and drives order flow toxicity and market instability at high frequencies. We formulate a UHF measure of reflexive crowdedness and find it predicts various UHF phenomena, including flash crashes and spikes, more reliably than price volatility and the Volume Synchronised Probability of Informed Trading (VPIN). This makes this measure highly relevant to investors, traders, market operators, and regulators
Generating Older Active Lives Digitally (GOALD): Exploring Older Adults’ Views of Digital Technology for Physical Activity
No full textBackground: The use of digital technology for supporting physical activity (PA) in older adults has increased recently despite the paucity of research exploring perceptions and age-appropriateness of these technologies. Methods: Older adults (n = 120) residing in care homes (n = 45) and living independently (n = 75) in England and Scotland, as well as care home staff (n = 30) and young adults (n = 7) appraised a variety of different PA technologies (eg, online and digital platforms, exergames, and virtual reality headsets) through a multiphase approach. Technologies were presented as a “menu” to participants to select at interactive sessions. Feedback was collected through focus groups, interviews, codesign workshops, and field notes, all analyzed thematically. Participant characteristics including PA levels and familiarity with technology were collected via questionnaire at baseline and after appraising selected technologies. Results: Qualitative findings explored 6 overarching themes: (1) recognizing the potential of technology, (2) suitability of PA technology, (3) barriers to using PA technology, (4) motivation to engage with digital technology, (5) content suggestions for future PA technology, and (6) preferences for PA technology delivery. PA engagement and prior experience with digital technologies varied greatly at baseline. Overall, participants’ perceptions and appraisal of the PA technologies varied according to their context and setting, prior experience with technology, and PA engagement. Quantitative data were challenging to gather, with complete data available from only 18% out of those consented. Conclusions: Older adults in this study demonstrated a keen interest in digital technologies for PA, but context- and health-related barriers for engagement with these tools need addressing
IO-K-Means:Iterative Optimization for Centroids in K-Means
No full textK-means is a widely used unsupervised learning algorithm, but its clustering performance can be heavily influenced by the choice of initial centroids and easily falls into local optima. To address these limitations, we propose IO-K-means, an improved K-means algorithm based on iterative centroid optimization. First, instead of random initialization, we employ reverse nearest-neighbor relationships (RNNs) to select higher-quality initial centroids, ensuring a more representative starting point. Second, we introduce an iterative refinement mechanism: in each iteration, a novel within-cluster compactness measure identifies which centroids require adjustment, and two operations—interconnection and perturbation—are applied to fine-tune their positions. Through multiple iterations, the algorithm progressively improves centroid placement, ultimately assigning data points to their nearest centroids for final clustering. To verify the proposed IO-K-means, we make experiments on 16 real-world datasets. The experimental results show that IO-K-means outperforms the state-of-the-art (SOTA) extensions of K-means especially including Nie’s work (TKDE2022).</p
Construction and mobility analysis of networks of deployable even-sided equilateral polygons
No full textDeployable planar mechanisms represent a classical category of deployable systems with broad applications in civil engineering, space exploration, metamaterials, and beyond — either as standalone structures or as compositional units. Despite significant progress in recent decades, designing such mechanisms remains challenging due to their overconstrained nature. This paper addresses the construction and mobility analysis of networks of deployable even-sided equilateral polygons (NDEEPs). Two approaches are proposed: the link-connection method and the joint-connection approach. Three classes of one degree-of-freedom (DOF) NDEEPs and one class of 2-DOF NDEEPs are introduced. Since conventional formulas for the mobility analysis are not applicable to NDEEPs derived via the link-connection method, a mobility analysis method based on a minimal set of constraint equations is proposed. The geometric characteristics of these NDEEPs are also identified. Two classes of NDEEPs derived from tessellations differ fundamentally from hinged tessellations. Additionally, two novel tessellations composed of three types of semi-regular equilateral polygons are discovered as a by-product. This work complements existing research on expanding polygon arrays, hinged tessellations, and deployable networks, contributing to the broader study of overconstrained mechanisms
Facilitating the Emergence of Assistive Robots to Support Frailty:Psychosocial and Environmental Realities
No full textWhile assistive robots have much potential to help older people with frailty-related needs, there are few in use. There is a gap between what is developed in laboratories and what would be viable in real-world contexts. Through a series of co-design workshops (61 participants across 7 sessions) including those with lived experience of frailty, their carers, and healthcare professionals, we gained a deeper understanding of everyday issues concerning the place of new technologies in their lives. A persona-based approach surfaced emotional, social, and psychological issues. Any assistive solution must be developed in the context of this complex interplay of psychosocial and environmental factors. Our findings, presented as design requirements in direct relation to frailty, can help promote design thinking that addresses people’s needs in a more pragmatic way to move assistive robotics closer to real-world use.</p
A large-scale framework for deriving tidal flat topography from SWOT data
No full textTidal flat topography is a fundamental attribute affecting inundation dynamics, sediment transport, and ecosystem functioning, yet accurate and spatially consistent large-scale monitoring remains challenging. Here, we leveraged satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission to develop a novel, large-scale framework for deriving tidal flat topography from SWOT data, and demonstrated its capability by generating a high-accuracy, national-scale elevation dataset for China. By combining a percentile-based aggregation of multi-temporal water-surface elevation observations with a tide-constrained, adaptive best-quantile (best-q) reconstruction strategy, followed by linear interpolation for gap filling, we improved both vertical accuracy and spatial completeness. Validation against airborne LiDAR, GNSS-RTK surveys, and ICESat-2 photon data demonstrates robust performance across diverse coastal settings, achieving RMSE = 0.34–0.47 m and R2 = 0.81–0.88 at a horizontal resolution of 100 m. Compared with existing large-scale digital elevation models (DEMs), the SWOT-derived topography not only improves vertical accuracy by over 80% but also providing substantially more complete spatial coverage of tidal flat elevations. Spatial analyses reveal pronounced latitudinal gradients, with higher tidal flats concentrated in low-latitude regions and extensive low-lying flats dominating northern estuarine and deltaic systems. This study establishes a scalable framework for tidal-flat elevation retrieval and provides a foundational dataset to support coastal monitoring and sustainable management
PVT and Phase Behavior of Petroleum Fluids
No full textFlow assurance can be expressed as the coupling of multiphase flow and fluid phase behavior. It requires understanding multiphase flow as well as fluid properties, fluid compositions, pressure-volume-temperature (PVT) characteristics, and other fluid phase behavior from the reservoir to the downstream processing facility through the life of the field to prevent upset conditions. A good understanding of the phase behavior and fluid properties allows a flow assurance engineer to develop technically feasible and economically optimal set of strategies to overcome a variety of flow assurance problems, ensuring a successful and safe operation. In this chapter, after a short review of the nature and chemistry of petroleum fluids, the thermodynamic behavior of pure compounds and the various types of reservoir fluids will be described. A section is then dedicated to laboratory thermodynamic experiments such as PVT analysis and finally some empirical correlations and equations of state used to simulate the phase behavior of the hydrocarbons and calculate thermophysical properties will be presented.</p
Gas Hydrates
No full textThe term gas hydrates refers to nonstoichiometric species belonging to the group of clathrates. They should be distinguished from the stoichiometric hydrates common to inorganic chemistry, for example, CaCl2.6H2O (antarcticite). Gas hydrates, or clathrate hydrates, are a group of ice-like, crystalline inclusion compounds, which form through the combination of water and suitably sized molecules, typically under low temperature and elevated pressure conditions. The petroleum industry is facing a flow assurance challenge with hydrate deposits in subsea pipelines where hydrate often forms at inaccessible locations. Hydrate formation could be a serious threat to the safe and economical operation of production/transportation facilities. One of the problems, other than blockage, is the movement of the hydrate plugs in the pipeline at high velocity, which can cause rupture in the pipeline and therefore create a severe safety and environmental hazard. Therefore an understanding of how, when, and where hydrates form is necessary to overcome hydrate problems. These questions have become all the more crucial since deepwater fields have been discovered or brought into production, where these fields are perfect candidates to encounter hydrate-forming conditions. This chapter answers these crucial questions and provides significant information on the best method to prevent and remediate hydrates in hydrocarbon production and transportation systems.</p
Machine learning models for volume and weight estimation in breast reconstruction planning
No full textBackgroundAccurate estimation of breast volume and weight is critical for post-mastectomy reconstruction. Existing methods are frequently costly or complex. We developed a machine learning framework that leverages demographic and anthropometric data to address these challenges.MethodsWe collected data from 199 patients between 2021 and 2023. The workflow comprised data collection, pre-processing, feature selection, model training, and performance evaluation. Three feature selection techniques were applied: domain expert knowledge, Spearman's rank correlation, and the Boruta algorithm. Each feature set was used to train linear regression, random forest regression, and support vector regression models. Model performance was evaluated using the coefficient of determination (R2) and Pearson's correlation coefficient. Significant correlations were identified between breast volume or weight and key patient characteristics, such as BMI, breast cup size, ptosis severity, and anthropometric measurements.ResultsThe optimal linear regression model, which incorporated both domain-expert and statistically selected features, achieved R2 values of 81.8% for breast volume and 72% for breast weight.ConclusionThe results indicate that integrating demographic and anthropometric data with machine learning yields an accurate, interpretable, and accessible method for preoperative breast assessment. In contrast to conventional imaging or mathematical models, this approach eliminates costs related to imaging equipment, relies on routinely collected clinical data, reduces the need for specialized equipment and training, and enables rapid integration into existing clinical workflows. By overcoming the limitations of traditional methods, the proposed model provides a practical, efficient, and cost-effective solution for clinical practice
Access Repository Dashboard
Do you manage Heriot Watt Pure? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!