This article employs a mixed-method approach – combining a literature review, descriptive statistical analysis, correlation testing, and the GIS for data visualization – to analyze the effects of geopolitical changes and political instability on tourism in Kosovo. Given Kosovo’s goal of establishing tourism as a leading economic sector, the aim of this article is to determine whether and to what extent geopolitical factors influence its development. Despite persistent regional tensions and limited international recognition, Kosovo’s tourism has remained resilient and shown gradual growth, primarily driven by diaspora and regional visitors. The findings reveal no statistically significant correlation between annual geopolitical events and tourist arrivals, indicating that the tourism industry has adapted to low-level instability. The study highlights a potential for sustained tourism growth in Kosovo in the future.
We present a physically-grounded population model for optical tidal disruption events (TDEs) that combines first-principles hydrodynamic simulations of stellar disruption with statistical inference of the underlying stellar and black hole populations. The model's prediction of peak luminosity is based directly on recent global simulations that follow the disruption self-consistently and contains no tunable parameters related to the emission physics. We construct the predicted joint distribution of peak luminosity and black hole mass, including both full and partial disruptions, and compare it to a sample of observed TDEs using Bayesian inference and Markov chain Monte Carlo sampling. We find that the model reproduces the distribution in the ($M_{BH},L_{peak}$) plane for the bulk of the observed TDE population with good statistical consistency. The data strongly favor an old stellar population, with a sharp suppression of stars above $M_* \simeq 1.5 - 2 M_\odot$. They also indicate that, at fixed stellar mass, the volumetric TDE rate is nearly independent of black hole mass. Partial disruptions contribute a substantial fraction ($\sim 30\%$) of detected events in flux-limited samples and are essential for reproducing the observed distribution. The inferred population properties are robust to different approximations to the stellar mass-radius relation, although the event rate at high luminosity is sensitive to the form of this relation for massive stars. We predict a large population of difficult to detect low luminosity TDEs, implying that the true volumetric TDE rate may exceed that inferred from present samples by up to an order of magnitude.
Theoni Stathopoulou, Eirini Adamopoulou, Lina Zirganou-Kazolea
et al.
Abstract Equitable educational opportunities, in terms of access to quality education and resources in the host countries, are crucial to the individual personal development of refugee minors, promoting psychosocial well-being and resilience despite significant adversity. Meanwhile, participation in the education system of the host country facilitates social integration. This qualitative study explored teachers’ views on factors influencing access to education and educational experiences of refugee students 12- to 18‐year old in Greece. Two focus groups were used to examine teachers’ views on the positive and negative factors affecting refugee minors’ access to education and educational experiences. Thematic analysis resulted in three main themes: (a) institutional barriers, (b) the pivotal role of an inclusive school culture, and (c) the reception class as a transitional space. Findings indicate that meaningful inclusion in terms of access to education, appropriate teaching, and a supportive learning environment remains elusive in Greece for students with refugee backgrounds, given that their educational experiences are usually limited to participation in reception classes designed almost exclusively to teach the host country’s language (Greek). Although, reception classes can indeed serve as an effective transitional space for refugee minors, facilitating school achievement, well-being, and social integration, they must be complemented by a long-term and broadened education policy that actively promotes refugee minors’ social inclusion in regular school classes alongside their native peers.
Objectives
Prior research suggests better academic outcomes among children with Down syndrome (DS) enrolled in mainstream compared to special schools. During public engagement, parents reported feeling pressure from schools to transfer their child from mainstream to special school. We investigated characteristics associated with school transfer, with specific attention to resource deprivation.
Method
We followed a cohort of children with DS from birth through year 6, derived from the Education and Child Health Insights from Linked Data database, which links data from NHS-funded hospitals and state-funded schools in England. We used logistic regression to estimate children’s odds of enrolling in mainstream vs. special school in year 1. For children in mainstream school in year 1, we calculated cox proportional hazard ratios for time-to-school-transfer by three deprivation measures--Income of Deprivation Affecting Children Index (IDACI), free school meal eligibility, and maternal age at birth—controlling for sex, multimorbidity, and academic assessments.
Results
We identified 3,025 children with DS born in English hospitals from 1 September 2003 through 31 August 2008. In year 1, 68% of children enrolled in mainstream school, with 15% more children from the least deprived quintile of IDACIs enrolled in mainstream school compared to the most deprived quintile. By year 6, only 50% of children remained in mainstream school, and the risk of transfer from mainstream to special school was higher among children with higher deprivation, by all measures. Multimorbidity, poorer academic assessment scores, and male sex were also associated with a higher risk of transfer to special school. Boys were 20% more likely to enrol in special school in year 1 and were 40% more likely than girls to transfer to special school.
Conclusion
Children with DS experiencing higher deprivation were less likely to sustain enrolment in mainstream school than their less-deprived peers. This trend may reflect local schools’ access to specialist support and parents’ access to resources and ability to self-advocate. Further evidence on the educational needs of children with DS is needed.
Objectives
The presentation will consider:
• How to engage effectively with government on your research
• Routes into government, how to interact with policy and analytical systems, including Areas of Research Interest
• Different types of effective policy impact
Methods
The workshop will cover practical tips on how to achieve policy impact.
The session will include a short presentation from the ADR Scotland Engagement and Impact team and potential interview style input from a SCADR researcher on successful research impact on policy (TBC).
Results
ADR Scotland Engagement and Impact team will highlight the range of routes and focus on one or two aspects of their work including ADR Scotland Data with Impact series which supports collaboration between government and academia. The SCADR researcher (TBC) will outline research undertaken, how this was impactful and how this was achieved.
Conclusion
Outputs will include provision of top tips on achieving policy impact across a range of contexts including practical sources of support.
Objectives
Being open and transparent with the public around research with data is a real challenge, especially given the language typically used. Our aim was to provide an easy tool for both administrators to update, and intuitive for the general public to use.
Method
The web-based tool that was developed needed to provide answers quickly and easily. To address this issue, members of the public were consulted to compile a list of research terms that needed breaking down. The terms were then categorised into subject areas and given additional taxonomies to allow users to drill-down and filter the terms. Text-based search functionality was added to broadly cover the Term Names and their Definitions. Another function was created to allow users to submit their own words or terms to the tool’s administrators.
Results
The resulting interactive glossary was very effective in quickly helping members of the public understand unfamiliar terms and demystify aspects of data research. Allowing the public to submit any terms they couldn’t find allowed for continuous feedback and for administrators to keep the tool fresh with new and relevant content.
Conclusion
This approach could easily be adapted for any research area, and enhance public buy-in. Constant communication with the public was key to really maximising the tool’s utility.
Alignment techniques have become central to ensuring that Large Language Models (LLMs) generate outputs consistent with human values. However, existing alignment paradigms often model an averaged or monolithic preference, failing to account for the diversity of perspectives across cultures, demographics, and communities. This limitation is particularly critical in health-related scenarios, where plurality is essential due to the influence of culture, religion, personal values, and conflicting opinions. Despite progress in pluralistic alignment, no prior work has focused on health, likely due to the unavailability of publicly available datasets. To address this gap, we introduce VITAL, a new benchmark dataset comprising 13.1K value-laden situations and 5.4K multiple-choice questions focused on health, designed to assess and benchmark pluralistic alignment methodologies. Through extensive evaluation of eight LLMs of varying sizes, we demonstrate that existing pluralistic alignment techniques fall short in effectively accommodating diverse healthcare beliefs, underscoring the need for tailored AI alignment in specific domains. This work highlights the limitations of current approaches and lays the groundwork for developing health-specific alignment solutions.
Megan Masterson, Kishalay De, Christos Panagiotou
et al.
Most tidal disruption events (TDEs) are currently found in time-domain optical and soft X-ray surveys, both of which are prone to significant obscuration. The infrared (IR), however, is a powerful probe of dust-enshrouded environments, and hence, we recently performed a systematic search of NEOWISE mid-IR data for nearby, obscured TDEs within roughly 200 Mpc. We identified 18 TDE candidates in galactic nuclei, using difference imaging to uncover nuclear variability amongst significant host galaxy emission. These candidates were selected based on the following IR light curve properties: (1) $L_\mathrm{W2}\gtrsim10^{42}$ erg s$^{-1}$ at peak, (2) fast rise, followed by a slow, monotonic decline, (3) no significant prior variability, and (4) no evidence for AGN activity in WISE colors. The majority of these sources showed no variable optical counterpart, suggesting that optical surveys indeed miss numerous obscured TDEs. Using narrow line ionization levels and variability arguments, we identified 6 sources as possible underlying AGN, yielding a total of 12 TDEs in our gold sample. This gold sample yields a lower limit on the IR-selected TDE rate of $2.0\pm0.3\times10^{-5}$ galaxy$^{-1}$ year$^{-1}$ ($1.3\pm0.2\times10^{-7}$ Mpc$^{-3}$ year$^{-1}$), which is comparable to optical and X-ray TDE rates. The IR-selected TDE host galaxies do not show a green valley overdensity nor a preference for quiescent, Balmer strong galaxies, which are both overrepresented in optical and X-ray TDE samples. This IR-selected sample represents a new population of dusty TDEs that have historically been missed by optical and X-ray surveys and helps alleviate tensions between observed and theoretical TDE rates and the so-called missing energy problem.
Recent years have witnessed the great advance of bioradar system in smart sensing of vital signs (VS) for human healthcare monitoring. As an important part of VS sensing process, VS measurement aims to capture the chest wall micromotion induced by the human respiratory and cardiac activities. Unfortunately, the existing VS measurement methods using bioradar have encountered bottlenecks in making a trade-off between time cost and measurement accuracy. To break this bottleneck, this letter proposes an efficient recursive technique (BERT) heuristically, based on the observation that the features of bioradar VS meet the conditions of Markov model. Extensive experimental results validate that BERT measurement yields lower time costs, competitive estimates of heart rate, breathing rate, and heart rate variability. Our BERT method is promising us a new and superior option to measure VS for bioradar. This work seeks not only to solve the current issue of how to accelerate VS measurement with an acceptable accuracy, but also to inspire creative new ideas that spur further advances in this promising field in the future.
Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave (GW) signals requires a model of the source population distribution. In previous searches using the PyCBC framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation (KDE) with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBH with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, 8 additional candidate events above an inverse false alarm rate (IFAR) threshold 0.5 yr are identified.
Survival regression is widely used to model time-to-events data, to explore how covariates may influence the occurrence of events. Modern datasets often encompass a vast number of covariates across many subjects, with only a subset of the covariates significantly affecting survival. Additionally, subjects often belong to an unknown number of latent groups, where covariate effects on survival differ significantly across groups. The proposed methodology addresses both challenges by simultaneously identifying the latent sub-groups in the heterogeneous population and evaluating covariate significance within each sub-group. This approach is shown to enhance the predictive accuracy for time-to-event outcomes, via uncovering varying risk profiles within the underlying heterogeneous population and is thereby helpful to device targeted disease management strategies.
Jenny Mathew, Jaclyn A Pagliaro, Sathyanarayanan Elumalai
et al.
Background Post–COVID-19 condition is emerging as a new epidemic, characterized by the persistence of COVID-19 symptoms beyond 3 months, and is anticipated to substantially alter the lives of millions of people globally. Patients with severe episodes of COVID-19 are significantly more likely to be hospitalized in the following months. The pathophysiological mechanisms for delayed complications are still poorly understood, with a dissociation seen between ongoing symptoms and objective measures of cardiopulmonary health. COVID-19 is anticipated to alter the long-term trajectory of many chronic cardiovascular and pulmonary diseases, which are common among those at risk of severe disease. Objective This study aims to use a single, integrated device—MouthLab, which measures 10 vital health parameters in 60 seconds—and a cloud-based proprietary analytics engine to develop and validate the Aidar Decompensation Index (AIDI), to predict decompensation in health among patients who previously had severe COVID-19. Methods Overall, 200 participants will be enrolled. Inclusion criteria are patients in the US Department of Veterans Affairs health care system; “severe” COVID-19 infection during the acute phase, defined as requiring hospitalization, within 3-6 months before enrollment; aged ≥18 years; and having 1 of 6 prespecified chronic conditions. All participants will be instructed to use the MouthLab device to capture daily physiological data and complete monthly symptom surveys. Structured data collection tables will be developed to extract the clinical characteristics of those who experience decompensation events (DEs). The performance of the AIDI will depend on the magnitude of difference in physiological signals between those experiencing DEs and those who do not, as well as the time until a DE (ie, the closer to the event, the easier the prediction). Information about demographics, symptoms (Medical Research Council Dyspnea Scale and Post-COVID-19 Functional Status Scale), comorbidities, and other clinical characteristics will be tagged and added to the biomarker data. The resultant predicted probability of decompensation will be translated into the AIDI, where there will be a linear relationship between the risk score and the AIDI. To improve prediction accuracy, data may be stratified based on biological sex, race, ethnicity, or underlying clinical characteristics into subgroups to determine if there are differences in performance and detection lead times. Using appropriate algorithmic techniques, the study expects the model to have a sensitivity of >80% and a positive predicted value of >70%. Results Recruitment began in January 2023, and at the time of manuscript submission, 204 patients have been enrolled. Publication of the complete results and data from the study is expected in 2025. Conclusions The focus on identifying predictor variables using a combination of biosensor-derived physiological features should enable the capture of heterogeneous characteristics of complications related to post–COVID-19 condition across diverse populations. Trial Registration ClinicalTrials.gov NCT05220306; https://clinicaltrials.gov/study/NCT05220306
Resumo Este trabalho se propõe a verificar o impacto das concessões à iniciativa privada dos serviços de saneamento sobre o acesso à água e ao esgoto tratados, bem como sobre as tarifas cobradas por esses serviços. O presente estudo se faz relevante à luz das recentes alterações legislativas no setor, à guisa da Lei n. 14.026/2020, e da necessidade de universalização do saneamento. Como método, foi utilizado o modelo diferenças em diferenças, para dados de 3.536 municípios brasileiros retirados do Sistema Nacional de Informações sobre Saneamento (SNIS), abrangendo o período de 1998 a 2019. Os resultados mostram impacto positivo e estatisticamente significativo dos prestadores privados (em relação aos públicos) sobre o acesso aos serviços de água, esgoto e tratamento de esgoto. Ademais, foi observado impacto positivo e estatisticamente significativo na tarifa praticada quando da concessão à iniciativa privada dos serviços, em comparação aos preços cobrados por prestadores públicos.
This study focuses on changes in the length of paid parental leave in Austria, France and Hungary between the 1960s and the first decade of the 2000s. Its aim is to analyse to what extent extensions and reductions of the duration of paid parental leave affect mothers’ labour market entry after childbirth. For each country, periods according to the different policy reforms are analysed and data from the Generations and Gender Survey are used to account for changes in mothers’ labour market entry.
Scholars have argued that too long periods of paid parental leave might act as work-reducing policy in that they hinder women’s labour market entry while short leaves have positive effects on labour market participation and wages. This phenomenon is studied in three countries with very different conciliation policies.
Results for Austria show that the introduction of more flexibility into the leave legislation in 2008 increased mothers’ relative risks of labour market entry especially among higher educated women. In France, the extension of paid leave for second born children in 1994 reduced labour market entry. We found however, no significant effect of the 1986 reform for third children and the 2004 reform introducing a paid leave of six months for first-born children. In Hungary, labour market entry of mothers was influenced by structural changes which accompanied the political transition of 1989 and resulted in longer leave take up of mothers. Labour market entry before the transition was more intensive than ever since that.
* This article belongs to a special issue on “Demographic Developments in Eastern and Western Europe Before and After the Transformation of Socialist Countries”.
Urban groups. The city. Urban sociology, City population. Including children in cities, immigration
Particles measured in large gradual solar energetic particle (SEP) events are believed to be predominantly accelerated at shocks driven by coronal mass ejections (CMEs). Ion charge state and composition analyses suggest that the origin of the seed particle population for the mechanisms of particle acceleration at CME-driven shocks is not the bulk solar wind thermal material, but rather a suprathermal population present in the solar wind. This suprathermal population could result from remnant material accelerated in prior solar flares and/or preceding CME-driven shocks. In this work, we examine the distribution of this suprathermal particle population in the inner heliosphere by combining a magnetohydrodynamic (MHD) simulation of the solar wind and a Monte-Carlo simulation of particle acceleration and transport. Assuming that the seed particles are uniformly distributed near the Sun by solar flares of various magnitudes, we study the longitudinal distribution of the seed population at multiple heliocentric distances. We consider a non-uniform background solar wind, consisting of fast and slow streams that lead to compression and rarefaction regions within the solar wind. Our simulations show that the seed population at a particular location (e.g., 1 au) is strongly modulated by the underlying solar wind configuration. Corotating interaction regions (CIRs) and merged interactions regions (MIRs) can strongly alter the energy spectra of the seed particle populations. In addition, cross-field diffusion plays an important role in mitigating strong variations of the seed population in both space and energy.
Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life. However, mainstream event detection models require high-quality expert human annotations of triggers, which are often costly and thus deter the application of ED to new domains. Therefore, in this paper, we focus on low-resource ED without triggers and aim to tackle the following formidable challenges: multi-label classification, insufficient clues, and imbalanced events distribution. We propose a novel trigger-free ED method via Derangement mechanism on a machine Reading Comprehension (DRC) framework. More specifically, we treat the input text as Context and concatenate it with all event type tokens that are deemed as Answers with an omitted default question. So we can leverage the self-attention in pre-trained language models to absorb semantic relations between input text and the event types. Moreover, we design a simple yet effective event derangement module (EDM) to prevent major events from being excessively learned so as to yield a more balanced training process. The experiment results show that our proposed trigger-free ED model is remarkably competitive to mainstream trigger-based models, showing its strong performance on low-source event detection.
Massive black holes in the centers of galaxies today must have grown by several orders of magnitude from seed black holes formed at early times. Detecting a population of intermediate mass black holes (IMBHs) can provide constraints on these elusive BH seeds. Here we use the large volume, cosmological hydrodynamical simulation Astrid, which includes IMBH seeds and dynamical friction to investigate the population of IMBH seeds. Dynamical friction is largely inefficient at sinking and merging seed IMBHs at high-z. This leads to an extensive population (several hundred per galaxy) of wandering IMBHs in large halos at z~2. A small fraction of these IMBHs are detectable as HLXs, Hyper Luminous X-ray sources. Importantly, at z ~ 2, IMBHs mergers produce the peak of GW events. We find close to a million GW events in Astrid between z=2-3 involving seed IMBH mergers. These GW events (almost all detectable by LISA) at cosmic noon should provide strong constraints on IMBH seed models and their formation mechanisms. At the center of massive galaxies, where the number of IMBHs can be as high as 10-100, SMBH-IMBH pairs can form. These Intermediate mass ratio inspirals (IMRIs) and extreme mass ratio inspirals (EMRIs), will require the next generation of milli-muHz space-based GW interferometers to be detected. Large populations of IMBHs around massive black holes will probe their environments and MBH causal structure.
Abstract How animals use their range can have physiological, ecological, and demographic repercussions, as well as impact management decisions, species conservation, and human society. Fidelity, the predictable return to certain places, can improve fitness if it is associated with high‐quality habitat or helps enable individuals to locate heterogenous patches of higher‐quality habitat within a lower‐quality habitat matrix. Our goal was to quantify patterns of fidelity at different spatial scales to better understand the relative plasticity of habitat use of a vital subsistence species that undergoes long‐distance migrations. We analyzed a decade (2010–2019) of GPS data from 240 adult, female Western Arctic Herd (WAH) caribou (Rangifer tarandus) from northwest Alaska, U.S.A. We assessed fidelity at 2 spatial scales: to site‐specific locations within seasonal ranges and to regions within the herd's entire range by using 2 different null datasets. We assessed both area and consistency of use during 6 different seasons of the year. We also assessed the temporal consistency of migration and calving events. At the scale of the overall range, we found that caribou fidelity was greatest during the calving and insect relief (early summer) seasons, where the herd tended to maximally aggregate in the smallest area, and lowest in winter when the seasonal range is largest. However, even in seasons with lower fidelity, we found that caribou still showed fidelity to certain regions within the herd's range. Within those seasonal ranges, however, there was little individual site‐specific fidelity from year to year, with the exception of summer periods. Temporally, we found that over 90% of caribou gave birth within 7 days of the day they gave birth the previous year. This revealed fairly high temporal consistency, especially given the spatial and temporal variability of spring migration. Fall migration exhibited greater temporal variability than spring migration. Our results support the hypothesis that higher fidelity to seasonal ranges is related to greater environmental and resource predictability. Interestingly, this fidelity was stronger at larger scales and at the population level. Almost the entire herd would seek out these areas with predictable resources, and then, individuals would vary their use, likely in response to annually varying conditions. During seasons with lower presumed spatial and/or temporal predictability of resources, population‐level fidelity was lower but individual fidelity was higher. The herd would be more spread out during the seasons of low‐resource predictability, leading to lower fidelity at the scale of their entire range, but individuals could be closer to locations they used the previous year, leading to greater individual fidelity, perhaps resulting from memory of a successful outcome the previous year. Our results also suggest that fidelity in 1 season is related to fidelity in the subsequent season. We hypothesize that some differences in patterns of range fidelity may be driven by seasonal differences in group size, degree of sociality, and/or density‐dependent factors. Climate change may affect resource predictability and, thus, the spatial fidelity and temporal consistency of use of animals to certain seasonal ranges.