87191 research outputs found
Sort by
The Feasibility of Using Fe XXIII Metastable Transitions as a Density Diagnostic for Low-mass X-Ray Binary Disk Winds
Get PDFAssessment of tick populations associated with capybaras in natural reserves and human-modified environments with or without invasive plants in the state of São Paulo, Brazil
Get PDFIn southeastern Brazil, capybaras (Hydrochoerus hydrochaeris) are main hosts of the ticks Amblyomma dubitatum and Amblyomma sculptum; the latter has great medical importance for being the main vector of Rickettsia rickettsii, the agent of Brazilian spotted fever (BSF). This study aimed to quantify ticks in areas invaded and not invaded by exotic plants (Hedychium coronarium, Cenchrus purpureus or Megathyrsus maximus) within capybaras’ living areas in São Paulo state. Tick populations were quantified in 24 capybara habitats, eight in natural reserves and 16 human-modified landscapes; the latter were composed of eight BSF-endemic areas and eight BSF-nonendemic areas. In the 24 areas, ticks were sampled in plots of natural forests and plots with at least one of the three invasive plants. Both presence and density of A. sculptum were higher in human-modified landscapes, while the presence and density of A. dubitatum tended to be similar among human-modified landscapes and natural reserve areas. The two tick species were collected both in areas colonized by invasive plants (H. coronarium, M. maximus, and/or C. purpureus) and in areas of non-invaded vegetation. However, a higher density of A. dubitatum was observed in areas of H. coronarium and a lower density of A. sculptum in this type of vegetation. Within human-modified landscapes, there was a higher density of A. sculptum in BSF-endemic areas when compared to non-endemic areas, unlike A. dubitatum, which remained at an almost constant density and presence among BSF-endemic and non-endemic areas. Plant invasions may have some role in the ecology of capybara-associated ticks
Understanding User Behaviour and Predicting Charging Costs: A Machine Learning Approach to Support Electric Vehicle Adoption Decisions
Get PDFThe increasing adoption of electric vehicles (EVs) necessitates a comprehensive understanding of charging patterns and user behaviour to enable future transportation infrastructure to be planned and designed to meet user needs. This study uses machine learning to predict the costs of EV charging sessions and analyse user behaviour to support strategic planning and decision‐making. We examined data that included factors such as total energy consumption and charging duration, and compared three models: linear regression, random forest, and gradient boosting. The gradient boosting model performed the best, with a mean squared error of 0.041 and an R ‐squared ( R 2 ) of 0.91. Additionally, the analysis of user behaviour revealed peak charging times between 6:00 PM (18:00) and 9:00 PM (21:00), with the majority of sessions occurring on weekdays, particularly Wednesdays. Most users preferred charging infrastructures within a 10‐mile radius. These insights not only enhance the understanding of current EV charging behaviours but also provide valuable information for local authorities and decision‐makers in transportation planning and infrastructure development. By integrating predictive modelling and behavioural analysis, this research offers a novel and robust framework for designing EV charging networks, addressing user needs, and advancing the sustainability of urban transportation systems. This approach not only supports the efficient deployment of charging infrastructures but also introduces the concept of charging comfort by aligning infrastructure development with real user needs. Unlike traditional methods that overlook user preferences and waiting times, our model integrates behavioural analysis to improve the overall user experience. By quantifying when, where, and how users prefer to charge their vehicles, this framework supports not only infrastructure optimisation but also enhances user satisfaction, a key factor in accelerating EV adoption and reducing the environmental burden of urban mobility
A typology of schools across the four nations of the United Kingdom: Class, race and geography
Get PDFIn this paper we analyse the hierarchical field of schools across the United Kingdom during the transition to university and suggest that there are five socially distinct clusters of schools. Our five‐cluster typology of UK schools is composed of an established group of elite private and state schools, schools for the white rural and suburban middle class, schools serving the (post‐)industrial and predominantly white working class in small towns, multi‐racial middle and working‐class schools and super‐diverse state schools of the precarious working class. To produce this typology, we used Higher Education Statistics Authority data to create aggregate pseudo‐school populations from university students who would have been in their final year of school or college between 2014/15 and 2017/18. Unlike previous analyses of UK school segregation that focus on Free School Meals, we use actual parental social class data to enable more granular analysis of class and occupation. We use principal components analysis, followed by clustering techniques, to examine how institutional inequalities between schools intersect with uneven geographies of class and race across the United Kingdom. We suggest that there are more complex hierarchies that move beyond historical binary perspectives on schooling as selective/comprehensive, private/state, working/middle class. Our findings suggest two major contributions to how we understand inequalities and hierarchies between schools. First, we find a more complex, geographically varied and socially and ethnically distinctive multipartite system of schooling across England, Scotland, Wales and Northern Ireland. Second, viewed from a geographical perspective, our findings suggest there is a spatial division of schooling across the United Kingdom. Our typology combines an institutional lens on the school system with a geographical understanding of how local geographies of race and class shape schools and colleges in ways that transcend as well as reinforce national or regional boundaries
Enhancing Active Channel Delineation in Alluvial Rivers using Monthly Aggregation of Sentinel-2 Imagery 2 3
No full textThe active channel of alluvial rivers delineates areas of geomorphic activity over a defined time window. While increasing satellite data availability enables monthly active channel delineations, multi-year analyses often rely on temporal aggregates (e.g., annual medians) to reduce computational costs and intra-annual variability. The potential of monthly information to improve active channels delineation and geomorphic interpretation remains largely unexplored. In this work, we delineated active channels for the Po River (Italy) by aggregating monthly Sentinel-2 classifications of river water and sediment bars into annual frequency maps at 10 m resolution. Annual aggregation mitigated monthly sediment underestimation (12%) but also amplified model overestimation biases (15%). Monthly classification persistence (e.g., classified as active channel for more than N months/year) was then used to reduce these errors and produce active channel areas that closely match those manually delineated from 30 cm orthophotos. The spatiotemporal variability of monthly classifications also show that the active channel area of dynamic reaches can vary ∼50% over the year. These changes revealed areas most prone to water-stage fluctuations, sediment transport, as well as zones seasonally or progressively colonized by vegetation—patterns hidden in single orthophotos or annual medians. Less dynamic reaches, by contrast, showed minimal differences between annual and monthly-based delineation methods. These findings emphasize the importance of adapting temporal aggregation to the river type and process analysed, with sub-annual resolutions better capturing, in dynamic rivers, seasonal and progressive active channel reconfigurations, along with their interaction with sediment and vegetation dynamics
Mental Health Help-Seeking in the Himalaya: Shifting Ecologies of Care in Post-Earthquake Nepal
No full text
Mapping review sentiment to SERVQUAL across cultures: a bilingual analysis using a seed-guided topic model
No full textOnline reviews rarely reveal which aspects of service quality drive sentiment and how these patterns differ across cultures, limiting their value for global operations. This study addresses this gap by examining how sentiments map onto SERVQUAL dimensions across cultures using bilingual Airbnb reviews. Leveraging a novel seed-guided classification and filtering approach (LDA and DFC) to isolate SERVQUAL-relevant texts, we compare dimension-level sentiment distributions. Results show that Western visitors place more (positive and negative) sentiment on service assurance, whereas Chinese visitors express more positive sentiments on service empathy and more negative sentiments on service tangibles. Accordingly, we develop culture-based service improvement strategies. Our study adds to the literature by demonstrating how to extract specific service knowledge from the sentiment distribution of interest and by showing that targeting sentiment to predefined service dimensions yields actionable insights for service operations. It also highlights the value of bilingual analysis in cross-cultural comparative research
Offshore wind power forecasting via trend-aware just-in-time learning with nearest neighbors
No full textAccurate offshore wind power forecasting is vital for secure grid operation and cost-effective system dispatch but remains challenging due to the high volatility and non-stationarity of offshore environments. Existing forecasting models often rely on offline training and external meteorological data, limiting their adaptability to rapid variations in wind power. This study proposes a trend-aware just-in-time learning (tJITL) framework that integrates trend similarity into an online autor-egressive exogenous (ARX) model. The method dynamically constructs local models online by selecting trend-consistent samples from historical data, thereby capturing transient dynamics without the need for model retraining or external variables. Experimental results demonstrate that the proposed tJITL framework provides a reliable and data-efficient solution for online offshore wind power forecasting, with strong potential for application in intelligent power system operations
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇