Louise Marryat, Senga Robertson, James P. Boardman
et al.
Objectives
Increasing numbers of children are exposed to opioids in pregnancy, either through illicit drugs or prescription opioids. This population has been largely neglected in research, however, primarily due to the challenges of traditional longitudinal follow-up. This research aims to explore the developmental outcomes of children with prenatal opioid exposure.
Method
This study uses linked administrative health data to identify 4,865 Scottish children exposed to opioids in pregnancy (through illicit substance use or prescription methadone/buprenorphine) in 2009-2019, and a further 4,793 children exposed to opioids through chronic pain relief medication. A control group who are demographically similar but with no opioid exposure was matched on age of mother, health board and deprivation level. Health data, including health visitor records and pre-school vision screening, were linked to the cohort. Preschool developmental outcomes were examined using descriptive statistics and regression modelling.
Results
Children exposed to illicit opioids/methadone/buprenorphine had levels of preschool developmental outcomes, including behavioural difficulties, social and emotional problems, and cognitive difficulties, 3-4 times higher than demographically similar children with no exposure (full results will be available for presentation post-disclosure control checking). In addition, children who participated in a universal preschool vision screening program at age 4-5 were around 5 times more likely to have a visual problem identified than demographic controls. Children exposed to opioids through chronic pain medication had higher levels of developmental difficulties, compared with demographic controls, however the levels remained much lower than children in the illicit/prescription methadone/buprenorphine group. Results remained after controlling for confounding factors including smoking and alcohol use in pregnancy, and birthweight.
Conclusions
Results highlight differences in development for children with prenatal opioid exposure, even after controlling for confounding factors. Using multi-sector administrative records to follow-up these children over the lifecourse will allow us to understand their outcomes across health, education, social care and justice, leading to robust evidence to enhance children’s lives.
Objective and Approach
This session describes a doctoral student-led process to align research, policy, and practice partners around the use of integrated data to address a policy priority—the mounting youth mental health crisis. The project partners engaged in a five-year governance process to leverage integrated administrative data from a large health and human service agency in one U.S. state. Qualitative field notes—collected while conducting quantitative analyses and engaging partners—were analyzed to document a realistic timeline and process for ethical, actionable research using cross-sector data.
Results
Five key lessons for partnership-engaged research emerged, emphasizing the importance of adapting to changes in project themes, leveraging strong partnership to address data quality concerns, nurturing existing and new relationships, balancing data access and data privacy, and supplementing administrative data with contextual data.
Conclusions
Though engaging deeply with partners takes substantial time and energy, there are benefits to researchers, policymakers, practitioners, and most importantly, residents whose data are represented. Furthermore, building pathways to legally and ethically access integrated data can promote the efficient, strategic use of data for social policy research, and ultimately, be leveraged to support evidence-based policymaking in an ever-changing world.
Implications
This session underscores the value of forming applied research partnerships and building legal and ethical pathways to access integrated data when tackling complex social policy issues.
Passive human sensing using wireless signals has attracted increasing attention due to its superiorities of non-contact and robustness in various lighting conditions. However, when multiple human individuals are present, their reflected signals could be intertwined in the time, frequency and spatial domains, making it challenging to separate them. To address this issue, this paper proposes a novel system for multiperson detection and monitoring of vital signs (i.e., respiration and heartbeat) with the assistance of space-time-coding (STC) reconfigurable intelligent metasurfaces (RISs). Specifically, the proposed system scans the area of interest (AoI) for human detection by using the harmonic beams generated by the STC RIS. Simultaneously, frequencyorthogonal beams are assigned to each detected person for accurate estimation of their respiration rate (RR) and heartbeat rate (HR). Furthermore, to efficiently extract the respiration signal and the much weaker heartbeat signal, we propose an improved variational mode decomposition (VMD) algorithm to accurately decompose the complex reflected signals into a smaller number of intrinsic mode functions (IMFs). We build a prototype to validate the proposed multiperson detection and vital-sign monitoring system. Experimental results demonstrate that the proposed system can simultaneously monitor the vital signs of up to four persons. The errors of RR and HR estimation using the improved VMD algorithm are below 1 RPM (respiration per minute) and 5 BPM (beats per minute), respectively. Further analysis reveals that the flexible beam controlling mechanism empowered by the STC RIS can reduce the noise reflected from other irrelative objects on the physical layer, and improve the signal-to-noise ratio of echoes from the human chest.
Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente
et al.
The paper centres on an assessment of the modelling approaches for the processing of signals in CW and FMCW radar-based systems for the detection of vital signs. It is shown that the use of the widely adopted phase extraction method, which relies on the approximation of the target as a single point scatterer, has limitations in respect of the simultaneous estimation of both respiratory and heart rates. A method based on a velocity spectrum is proposed as an alternative with the ability to treat a wider range of application scenarios.
The COVID-19 pandemic highlighted significant challenges in the allocation of vital healthcare resources. Existing epidemiological models, specifically compartmental models, aimed to predict the spread of the COVID-19 virus and its impact on the population, but they overlooked the influence of \ac{VH} on disease dynamics, including the expected number of hospitalizations and fatalities. We propose improvements to the \ac{SEIR} model for COVID-19 by incorporating the influence of vaccination, \ac{VH}, and resource availability on the disease dynamics. We collect publicly available data and perform data analysis to capture \ac{VH} dynamic changes over time and develop scenario paths for \ac{VH}. We simulate the proposed compartmental model for each \ac{VH} path to explain the impacts of public attitudes toward vaccination, the impacts of healthcare resources on patient outcomes, and the timing of vaccination rollout on the progression and severity of the epidemic. Our analysis demonstrates that reducing \ac{VH} improves health outcomes, reinforcing the importance of addressing \ac{VH} to curb the spread of infectious diseases. Our results show that adequate levels of critical healthcare resources are crucial for minimizing fatalities and also highlight the life-saving impact of timely and effective vaccination programs.
Alba Aguilera, Miquel Albertí, Nardine Osman
et al.
In recent years, computational improvements have allowed for more nuanced, data-driven and geographically explicit agent-based simulations. So far, simulations have struggled to adequately represent the attributes that motivate the actions of the agents. In fact, existing population synthesis frameworks generate agent profiles limited to socio-demographic attributes. In this paper, we introduce a novel value-enriched population synthesis framework that integrates a motivational layer with the traditional individual and household socio-demographic layers. Our research highlights the significance of extending the profile of agents in synthetic populations by incorporating data on values, ideologies, opinions and vital priorities, which motivate the agents' behaviour. This motivational layer can help us develop a more nuanced decision-making mechanism for the agents in social simulation settings. Our methodology integrates microdata and macrodata within different Bayesian network structures. This contribution allows to generate synthetic populations with integrated value systems that preserve the inherent socio-demographic distributions of the real population in any specific region.
Murat Ersalman, Mervi Kunnasranta, Markus Ahola
et al.
Integrated population models (IPMs) are a promising approach to test ecological theories and assess wildlife populations in dynamic and uncertain conditions. By combining multiple data sources into a unified model, they enable the parametrization of versatile, mechanistic models that can predict population dynamics in novel circumstances. Here, we present a Bayesian IPM for the ringed seal (Pusa hispida botnica) population inhabiting the Bothnian Bay in the Baltic Sea. Despite the availability of long-term monitoring data, traditional assessment methods have faltered due to dynamic environmental conditions, varying reproductive rates, and the recently re-introduced hunting, thus limiting the quality of information available to managers. We fit our model to census and various demographic, reproductive, and harvest data from 1988 to 2023 to provide a comprehensive assessment of past population trends, and predict population response to alternative hunting scenarios. We estimated that 20,000 to 36,000 ringed seals inhabited the Bothnian Bay in 2024, increasing at a rate of 3% to 6% per year. Reproductive rates have increased since 1988, leading to a substantial increase in the growth rate up until 2015. However, the re-introduction of hunting has since reduced the growth rate, and even minor quota increases are likely to reduce it further. Our results also support the hypothesis that a greater proportion of the population hauls out under lower ice cover circumstances, leading to higher aerial survey results in such years. In general, our study demonstrates the value of IPMs for monitoring wildlife populations under changing environments, and supporting science-based management decisions.
Rafael Ricardo Rentería Ramos, Alicia María Vitale Alfonso
Se presenta un modelo basado en agentes para estudiar el comportamiento migratorio del departamento de Risaralda, Colombia. Para su construcción se consideraron tres elementos fundamentales en la dinámica poblacional: la toma de decisión de migrar, con el desarrollo de un modelo bayesiano; el componente de atracción, a través de un modelo gravitacional; y la aplicación del enfoque minimalista usando modelos basados en agentes en estudios de la migración por decisión. Los resultados evidencian que la dinámica migratoria del departamento tiene rutas con alta concentración de capital social, que condicionan el arribo y la expulsión poblacional, modificando su estructura.
Human settlements. Communities, Demography. Population. Vital events
Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil
et al.
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency. In this paper, we present a multi-view approach that combines latent spaces derived from state-of-the-art chemical models. Our approach relies on two pivotal elements: the embeddings derived from MHG-GNN, which represent molecular structures as graphs, and MoLFormer embeddings rooted in chemical language. The attention mechanism of MoLFormer is able to identify relations between two atoms even when their distance is far apart, while the GNN of MHG-GNN can more precisely capture relations among multiple atoms closely located. In this work, we demonstrate the superior performance of our proposed multi-view approach compared to existing state-of-the-art methods, including MoLFormer-XL, which was trained on 1.1 billion molecules, particularly in intricate tasks such as predicting clinical trial drug toxicity and inhibiting HIV replication. We assessed our approach using six benchmark datasets from MoleculeNet, where it outperformed competitors in five of them. Our study highlights the potential of latent space fusion and feature integration for advancing molecular property prediction. In this work, we use small versions of MHG-GNN and MoLFormer, which opens up an opportunity for further improvement when our approach uses a larger-scale dataset.
Ronan Amorim, Emilio Vital Brazil, Faramarz Samavati
et al.
The construction of 3D geological models is an essential task in oil/gas exploration, development and production. However, it is a cumbersome, time-consuming and error-prone task mainly because of the model's geometric and topological complexity. The models construction is usually separated into interpretation and 3D modeling, performed by different highly specialized individuals, which leads to inconsistencies and intensifies the challenges. In addition, the creation of models following geological rules is paramount for properly depicting static and dynamic properties of oil/gas reservoirs. In this work, we propose a sketch-based approach to expedite the creation of valid 3D geological models by mimicking how domain experts interpret geological structures, allowing creating models directly from interpretation sketches. Our sketch-based modeler (Geo-Sketcher) is based on sketches of standard 2D topographic and geological maps, comprised of lines, symbols and annotations. We developed a graph-based representation to enable (1) the automatic computation of the relative ages of rock series and layers, and (2) the embedding of specific geological rules directly in the sketching. We introduce the use of Hermite-Birkhoff Radial Basis Functions to interpolate the geological map constraints, and demonstrate the capabilities of our approach with a variety of results with different levels of complexity.
<b>Background</b>: Demographic researchers have recognized the importance of male partners in reproductive behavior and decision-making. Yet much of the existing literature still relies on female respondents reporting on behalf of their spouses. <b>Objective</b>: The objective of this study is to estimate the accuracy of wives' reports of husbands' fertility preferences in 32 countries in sub-Saharan Africa. <b>Methods</b>: We used couple-level data from Demographic and Health Surveys to evaluate the accuracy of wives' reports of their husbands' fertility preferences in 32 countries in sub-Saharan Africa. We created a measure of accuracy based on each partner's response to a set of fertility preference questions. We examined the overall percentages of wives who were accurate, inaccurate, or uncertain across countries. <b>Results</b>: Despite the fact that most couples were concordant in wanting more children, we found variation in the percentages of wives who were accurate in their proxy reports, ranging from 26Š in Chad to 58Š in Rwanda. By contrast, percentages of wives who were inaccurate were similar; approximately one-third of wives across all countries gave proxy responses that were at odds with their husbands' responses. Large percentages of wives were uncertain of their husbands' fertility preferences, reaching 50Š in Comoros. <b>Conclusions</b>: These findings indicate low levels of spousal discussion of fertility preferences. We encourage survey organizations to invest in collecting data from males directly. <b>Contribution</b>: By demonstrating that majorities of wives across countries either inaccurately perceive or are uncertain of their husband's fertility preferences, the current study justifies collecting data from male partners directly.
El objetivo de este artículo es identificar la metodología de clusterización más apropiada para aplicarse en el sector restaurantero de la Zona Metropolitana de Guadalajara (ZMG). Se llevó a cabo un recuento de las distintas técnicas de clusterización espacial, para después identificar que la más conveniente es la técnica de Kulldorff, la cual fue utilizada para mapear los clústeres de los restaurantes existentes en la metrópoli. Los resultados muestran diez clústeres de restaurantes en la ZMG, siete de ellos con alta concentración de unidades económicas. El presente estudio es innovador respecto a la detección de clústeres en la industria restaurantera de la ZMG.
Human settlements. Communities, Demography. Population. Vital events
Hypergraphs that can depict interactions beyond pairwise edges have emerged as an appropriate representation for modeling polyadic relations in complex systems. With the recent surge of interest in researching hypergraphs, the centrality problem has attracted abundant attention due to the challenge of how to utilize the higher-order structure for the definition of centrality metrics. In this paper, we propose a new centrality method (HGC) on the basis of the gravity model as well as a semi-local HGC (LHGC) which can achieve a balance between accuracy and computational complexity. Meanwhile, two comprehensive evaluation metrics, i.e., a complex contagion model in hypergraphs that mimics the group influence during the spreading process and network s-efficiency based on the higher-order distance between nodes, are first proposed to evaluate the effectiveness of our methods. The results show that our methods can filter out nodes that have fast spreading ability and are vital in terms of hypergraph connectivity.
We consider the dynamics of two coupled three-species population patches, incorporating the Allee Effect, focussing on the onset of extreme events in the coupled system. First we show that the interplay between coupling and the Allee effect may change the nature of the dynamics, with regular periodic dynamics becoming chaotic in a range of Allee parameters and coupling strengths. Further, the growth in the vegetation population displays an explosive blow-up beyond a critical value of coupling strength and Allee parameter. Most interestingly, we observe that beyond a threshold of coupling strength and Allee parameter, the population densities of all three species exhibit non-zero probability of yielding extreme events. The emergence of extreme events in the predator populations in the patches is the most prevalent, and the probability of obtaining large deviations in the predator populations is not affected significantly by either the coupling strength or the Allee effect. In the absence of the Allee effect the prey population in the coupled system exhibits no extreme events for low coupling strengths, but yields a sharp increase in extreme events after a critical strength of coupling. The vegetation population in the patches display a small finite probability of extreme events for strong enough coupling, only in the presence of Allee effect. Lastly we consider the influence of additive noise on the continued prevalence of extreme events. Very significantly, we find that noise suppresses the unbounded vegetation growth that was induced by a combination of Allee effect and coupling. Further, we demonstrate that noise mitigates extreme events in all three populations, and beyond a noise level we do not observe any extreme events in the system at all. This finding has important bearing on the potential observability of extreme events in natural and laboratory systems.
We describe a Bayesian formalism for analyzing individual gravitational-wave events in light of the rest of an observed population. This analysis reveals how the idea of a "population-informed prior" arises naturally from a suitable marginalization of an underlying hierarchical Bayesian model which consistently accounts for selection effects. Our formalism naturally leads to the presence of "leave-one-out" distributions which include subsets of events. This differs from other approximations, also known as empirical Bayes methods, which effectively double count one or more events. We design a double-reweighting post-processing strategy that uses only existing data products to reconstruct the resulting population-informed posterior distributions. Although the correction we highlight is an important conceptual point, we find it has a limited impact on the current catalog of gravitational-wave events. Our approach further allows us to study, for the first time in the gravitational-wave literature, correlations between the parameters of individual events and those of the population.
In 2009 the cross artform group, Last Man to Die, presented a series of performances using new interfaces and networked performance to integrate the three artforms of its members (actor, Hanna Cormick, visual artist, Benjamin Forster and percussionist, Charles Martin). This paper explains our artistic motivations and design for a computer vision surface and networked heartbeat sensor as well as the experience of mounting our first major work, Vital LMTD.
Colors of Type Ia supernovae in the first few days after explosion provide a potential discriminant between different models. In this paper, we present $g-r$ colors of 65 Type Ia supernovae discovered within 5 days from first light by the Zwicky Transient Facility in 2018, a sample that is about three times larger than that in the literature. We find that $g-r$ colors are intrinsically rather homogeneous at early phases, with about half of the dispersion attributable to photometric uncertainties ($σ_\mathrm{noise}\simσ_\mathrm{int}\sim$ 0.18 mag). Colors are nearly constant starting from 6 days after first light ($g-r\sim-0.15$ mag), while the time evolution at earlier epochs is characterized by a continuous range of slopes, from events rapidly transitioning from redder to bluer colors (slope of $\sim-0.25$ mag day$^{-1}$) to events with a flatter evolution. The continuum in the slope distribution is in good agreement both with models requiring some amount of $^{56}$Ni mixed in the outermost regions of the ejecta and with "double-detonation" models having thin helium layers ($M_\mathrm{He}=0.01\,M_\odot$) and varying carbon-oxygen core masses. At the same time, six events show evidence for a distinctive "red bump" signature predicted by "double-detonation" models with larger helium masses. We finally identify a significant correlation between the early-time $g-r$ slopes and supernova brightness, with brighter events associated to flatter color evolution (p-value=0.006). The distribution of slopes, however, is consistent with being drawn from a single population, with no evidence for two components as claimed in the literature based on $B-V$ colors.
As the number of gravitational-wave transient detections grows, the inclusion of marginally significant events in gravitational-wave catalogs will lead to increasing contamination from false positives. In this paper, we address the question of how to carry out population studies in light of the fact that some fraction of marginally significant gravitational-wave events are of terrestrial origin. We show that previously published estimates of $p_\text{astro}$, the probability that an event is of astrophysical origin, imply an effective noise likelihood, which can be used to take into account the uncertain origin of marginal events in population studies. We derive a formalism to carry out population studies with ambiguous gravitational-wave events. We demonstrate this formalism using events from the LIGO/Virgo Gravitational-Wave Transient Catalog 1 (GWTC-1) as well as events from the Venumadhav et al. "IAS catalog." We derive posterior distributions for population parameters and discuss how they change when we take into account $p_\text{astro}$. We provide updated individual-event posterior distributions by including population information.