Hasil untuk "Demography. Population. Vital events"

Yu-Heng Sheng, De-Fu Bu, Liang Chen et al.

Tidal Disruption Events (TDEs) release enormous amounts of energy, offering a promising avenue for detecting Population III (Pop III) stars. However, the radiative properties of TDEs of Pop III stars have so far been studied only analytically, relying on many assumptions. Based on our radiative hydrodynamic simulations that follow the evolution of the accretion system for Pop III star TDEs where a $300\ M_{\odot}$ ($M_{\odot}$ is the solar mass) star is disrupted by a $10^{6}\ M_{\odot}$ black hole (BH), we compute the emission properties of the event in rest frame and find that the spectrum peaks in the optical/UV waveband. After accounting for redshift ($z \sim 10$) and extinction effects, we find the observed spectral peak shifts to the infrared, with fluxes exceeding $10^{2}\mathrm{nJy}$-making such events detectable with both the James Webb Space Telescope (JWST) and the Nancy Grace Roman Space Telescope (Roman). The dependence of the observed spectrum on viewing angle is suppressed due to dust extinction. Using our simulation results, we also calculate the radio emission generated by the interaction between the wind and the circumnuclear medium (CNM) and find that a Pop III star TDE can produce an unusually long-lasting, continuously increasing radio flare with a duration greater than $10^4$ days and thus has the potential to be detected in radio wavebands. These results may be helpful to the detection of Pop III stars.

D. Coleman, S. Basten, F. Billari

Yongcun Wei, Yan Xie, Anwu Xuan et al.

Low back pain (LBP) substantially affects quality of life and functional capacity, ranking as a major global cause of disability. While the global burden of LBP has been extensively studied, China’s unique demographic, socioeconomic, and healthcare contexts warrant focused attention. As the world’s most populous nation undergoing rapid urbanization and aging, China presents a distinct landscape for LBP epidemiology. This study aims to chart the temporal shifts in the age- and sex-specific burdens of LBP in China from 1990 to 2021, encompassing incidence, prevalence, and disability-adjusted life years (DALYs). By benchmarking these trends against the worldwide disease burden, this research provides critical insights into how China’s experience aligns with or diverges from global patterns, offering valuable guidance for targeted public health strategies. This study leveraged open-access data from the Global Burden of Disease (GBD) repository, spanning the years 1990 to 2021, to scrutinize the epidemiological profile of LBP in China and across the globe. The analysis encompassed fluctuations in LBP incidence, prevalence, and DALYs. The Joinpoint regression model was employed to determine the average annual percentage change (AAPC) and its associated 95% confidence interval (95% CI), thereby quantifying the trajectory of LBP burden. A multifaceted comparative evaluation was performed to elucidate disparities in LBP burden between China and other regions, examining various aspects such as age, gender, and temporal dynamics. From 1990 to 2021, both China and the world experienced a decline in age-standardized metrics related to LBP. In China, the age-standardized incidence rate (ASIR) decreased from 2,859.383 to 2,342.459 per 100,000, while globally, it fell from 3,534.988 to 3,176.63 per 100,000. Similarly, the age-standardized prevalence rate (ASPR) in China declined from 6,635.488 to 5,342.1 per 100,000, compared to a global reduction from 8,391.582 to 7,463.13 per 100,000. The age-standardized DALYs rate (ASDR) in China also dropped from 749.026 to 603.033 per 100,000, while globally, it decreased from 937.339 to 832.179 per 100,000. Notably, according to the AAPC results, China showed a more pronounced decrease in these metrics compared to the global averages, especially before 2015. Gender differences were evident, with women consistently exhibiting higher incidence, prevalence, and DALYs for LBP across all age groups and years. Age-related disparities were also significant: in 2021, the crude incidence rate (CIR), crude prevalence rate (CPR), and crude DALY rate (CDR) peaked in the 85–89 age group, reflecting the substantial burden of LBP among older adults. However, the highest number of incidence, prevalence, and DALYs was observed in the 55–59 age group, indicating a shift toward middle-aged individuals as a key affected population. Overall, while China’s LBP burden demonstrated a consistent decline, the gender and age patterns suggest a need for tailored public health interventions targeting middle-aged and elderly populations, as well as women who are disproportionately affected. Although China’s LBP burden has declined, it remains significant among middle-aged and elderly populations, with women disproportionately affected. Public health efforts should focus on ergonomic improvements, promoting physical activity, and accessible nonpharmacological treatments. Integrating LBP care into primary healthcare is vital to mitigate its impact and support the aging population.

Michael Mühlichen, G. Doblhammer

Little is known about social gradients in cause-specific infant mortality in the nineteenth century. To our knowledge, this is the first paper to explore this connection for the time prior to the epidemiologic and demographic transitions. We used the church records of Rostock, an important port city on the Baltic coast in northern Germany, and prepared and merged the baptismal and burial registers of its largest parish (St. Jakobi) for the periods 1815–1836 and 1859–1882. Based on individual-level data (N = 16,880), we classified the fathers’ occupations into three social classes and estimated cause-specific infant mortality risks for these groups using event history analysis. We found a clear social gradient in neonatal and post-neonatal mortality. This gradient was driven by waterborne diseases and convulsions, suggesting severe nutritional and sanitation deficits among the lower social classes even before the city began to struggle with worsening living environments following industrialisation and population growth in the second half of the nineteenth century. Our results also suggest that deteriorating environmental conditions affect all parts of the population, leading to an increase of infant mortality rates in all social classes. Improvements in nutritional and sanitary conditions may thus reduce the risk of infant death from infectious diseases.

Alisher Turubayev, Anna Shopova, Fabian Lange et al.

As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the data that could negatively impact prediction performance. Previous works have introduced numerous time-series imputation techniques. Nevertheless, more comprehensive work is needed to compare a representative set of methods for imputing ICU vital signs and determine the best practice. In reality, ad-hoc imputation techniques that could decrease prediction accuracy, like zero imputation, are still used. In this work, we compare established imputation techniques to guide researchers in improving the performance of clinical prediction models by selecting the most accurate imputation technique. We introduce an extensible and reusable benchmark with currently 15 imputation and 4 amputation methods, created for benchmarking on major ICU datasets. We hope to provide a comparative basis and facilitate further ML development to bring more models into clinical practice.

Yonathan Eder, Emma Zagoury, Shlomi Savariego et al.

With the rising prevalence of cardiovascular and respiratory disorders and an aging global population, healthcare systems face increasing pressure to adopt efficient, non-contact vital sign monitoring (NCVSM) solutions. This study introduces a robust framework for multi-person localization and vital signs monitoring, using multiple-input-multiple-output frequency-modulated continuous wave radar, addressing challenges in real-world, cluttered environments. Two key contributions are presented. First, a custom hardware phantom was developed to simulate multi-person NCVSM scenarios, utilizing recorded thoracic impedance signals to replicate realistic cardiopulmonary dynamics. The phantom's design facilitates repeatable and rapid validation of radar systems and algorithms under diverse conditions to accelerate deployment in human monitoring. Second, aided by the phantom, we designed a robust algorithm for multi-person localization utilizing joint sparsity and cardiopulmonary properties, alongside harmonics-resilient dictionary-based vital signs estimation, to mitigate interfering respiration harmonics. Additionally, an adaptive signal refinement procedure is introduced to enhance the accuracy of continuous NCVSM by leveraging the continuity of the estimates. Performance was validated and compared to existing techniques through 12 phantom trials and 12 human trials, including both single- and multi-person scenarios, demonstrating superior localization and NCVSM performance. For example, in multi-person human trials, our method achieved average respiration rate estimation accuracies of 94.14%, 98.12%, and 98.69% within error thresholds of 2, 3, and 4 breaths per minute, respectively, and heart rate accuracies of 87.10%, 94.12%, and 95.54% within the same thresholds. These results highlight the potential of this framework for reliable multi-person NCVSM in healthcare and IoT applications.

Wanzhe Xu, Yutong Dai, Yitao Yang et al.

Accurate multivariate time-series prediction of vital signs and laboratory results is crucial for early intervention and precision medicine in intensive care units (ICUs). However, vital signs are often noisy and exhibit rapid fluctuations, while laboratory tests suffer from missing values, measurement lags, and device-specific bias, making integrative forecasting highly challenging. To address these issues, we propose OmniTFT, a deep learning framework that jointly learns and forecasts high-frequency vital signs and sparsely sampled laboratory results based on the Temporal Fusion Transformer (TFT). Specifically, OmniTFT implements four novel strategies to enhance performance: sliding window equalized sampling to balance physiological states, frequency-aware embedding shrinkage to stabilize rare-class representations, hierarchical variable selection to guide model attention toward informative feature clusters, and influence-aligned attention calibration to enhance robustness during abrupt physiological changes. By reducing the reliance on target-specific architectures and extensive feature engineering, OmniTFT enables unified modeling of multiple heterogeneous clinical targets while preserving cross-institutional generalizability. Across forecasting tasks, OmniTFT achieves substantial performance improvement for both vital signs and laboratory results on the MIMIC-III, MIMIC-IV, and eICU datasets. Its attention patterns are interpretable and consistent with known pathophysiology, underscoring its potential utility for quantitative decision support in clinical care.

Siddharth Mangalik, Ojas Deshpande, Adithya V. Ganesan et al.

Estimating community-specific mental health effects of local events is vital for public health policy. While forecasting mental health scores alone offers limited insights into the impact of events on community well-being, quasi-experimental designs like the Longitudinal Regression Discontinuity Design (LRDD) from econometrics help researchers derive more effects that are more likely to be causal from observational data. LRDDs aim to extrapolate the size of changes in an outcome (e.g. a discontinuity in running scores for anxiety) due to a time-specific event. Here, we propose adapting LRDDs beyond traditional forecasting into a statistical learning framework whereby future discontinuities (i.e. time-specific shifts) and changes in slope (i.e. linear trajectories) are estimated given a location's history of the score, dynamic covariates (other running assessments), and exogenous variables (static representations). Applying our framework to predict discontinuities in the anxiety of US counties from COVID-19 events, we found the task was difficult but more achievable as the sophistication of models was increased, with the best results coming from integrating exogenous and dynamic covariates. Our approach shows strong improvement ($r=+.46$ for discontinuity and $r = +.65$ for slope) over traditional static community representations. Discontinuity forecasting raises new possibilities for estimating the idiosyncratic effects of potential future or hypothetical events on specific communities.

Noorlailahusna Mohd Yusof, S. Mat Yasin

The rapid growth of ageing populations globally necessitates age-friendly urban environments to enhance older adults’ well-being. In Malaysia, the proportion of individuals aged 60 and above is projected to reach 15% by 2030. To address this, Taiping was selected as the first pilot project for age-friendly city development, reflecting Malaysia’s commitment to the World Health Organization’s (WHO) Age-Friendly Cities framework. This study investigates the role of activity centres, clubhouses, mosques, and community halls as hubs for social interaction and community engagement among older adults in Taiping. Using a qualitative approach, semi-structured interviews with 12 participants revealed that these centres promote physical, mental, and social well-being through diverse activities such as group exercises, religious gatherings, and cultural events. Findings highlight the critical role of activity centres in fostering social connections, reducing loneliness, and enhancing community support systems. Taiping’s initiative aligns with global trends while addressing the unique cultural and demographic context of Southeast Asia. The study highlights the need for policymakers to prioritise funding and integrate activity centres into urban planning strategies. By ensuring accessibility, offering diverse programs, and promoting intergenerational engagement, activity centres can serve as vital components of resilient, age-friendly communities and contribute to achieving sustainable development goals.

Lara Bister, Peter Eibich, Roberta Rutigliano

Abstract Public parental leave schemes aim to facilitate women’s reconciliation of family and employment after their transition into motherhood. While parental leave policies underwent several reforms over the past decades, adapting to changing female labour market participation and family cultures, the available entitlements are not tailored to women’s individual circumstances and needs. It remains unclear how these affect the women’s parental leave uptake, particularly the leave length. In this paper, we followed an exploratory and descriptive approach to study the selection of women into different parental leave lengths with changing public parental leave entitlements in Germany and according to their individual characteristics. We use data from the German Statutory Pension Fund on 29,001 women born between 1955 and 1984 who had their first child between 1991 and 2016 at the ages 20–39. We estimate linear regression and discrete-time proportional hazard models to examine associations between women’s characteristics and their length of leave. We identify the effects of two major parental leave reforms in Germany in 1992 and 2007 in a Regression Discontinuity Design. Our results show that the general extension of available parental leave entitlements in 1992 increased the likelihood of women’s parental leave uptake between 25 and 36 months. For women who became mothers at an older age, had a high income before transitioning into motherhood, or with higher education; however, the likelihood of parental leave uptake of 2 months increased. The reform of 2007 led to an increased likelihood of leave uptake longer than 2 months for these women. These findings suggest that women with a higher labour market attachment have responded more strongly to the changes in parental leave benefits in Germany.

Lewis Hotchkiss, Emma Squires, Timothy Rittman et al.

The Dementias Platform UK (DPUK) Data Portal holds over 60 cohort datasets from over 3 million participants with a range of multi-modal data including neuroimaging and genomics. This has meant we have seen an increasing interest in the development of AI models with the potential for clinical implementation. However, this presents a unique challenge to disclosure control when it comes to assessing these AI models for release due to the risk of attacks such as membership inference, model inversion or even just vulnerabilities in the models like overfitting. This is why we hosted a series of workshops bringing together members of the public, expert researchers, and data owners across the UK to build a framework for allowing the safe development and release of AI models trained on sensitive healthcare data. From this, we have put together recommendations and guidelines for the use of privacy-preserving techniques in AI models to protect patient privacy, and to allow the safe deployment of these models outside of trusted research environments. We also identified the unique challenges to privacy and implementation of privacy-preserving techniques in AI models regarding the use of complex data such as neuroimaging and genomics. This framework has created a path forward for supporting safe AI model development which takes into consideration rapidly evolving ethical, legal and privacy considerations.

Olaoluwayimika Olugbenle, Logan Drake, Naveenkumar G. Venkataswamy et al.

We present our work on leveraging low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users. These videos utilize photoplethysmography (PPG), commonly used to measure vital signs like heart rate. While prior research predominantly utilizes high-frame-rate, multi-wavelength PPG sensors (e.g., infrared, red, or RGB), our preliminary findings demonstrate that both user identification and vital sign extraction are achievable with the low-frame-rate data we collected. Preliminary results are promising, with low error rates for both heart rate estimation and user authentication. These results indicate promise for effective biometric systems. We anticipate further optimization will enhance accuracy and advance healthcare and security.

Wei Ren, Jiannong Cao, Huansheng Yi et al.

Multi-point vital sign monitoring is essential for providing detailed insights into physiological changes. Traditional single-sensor approaches are inadequate for capturing multi-point vibrations. Existing contact-based solutions, while addressing this need, can cause discomfort and skin allergies, whereas noncontact optical and acoustic methods are highly susceptible to light interference and environmental noise. In this paper, we aim to develop a non-contact, multi-point vital sign monitoring technique using MIMO radar, focused on physically differentiating and precisely measuring chest-wall surface vibrations at multiple points induced by cardiopulmonary mechanical activity. The primary challenges in developing such a technique involve developing algorithms to extract and separate entangled signals, as well as establishing a reliable method for validating detection accuracy. To address these limitations, we introduce MultiVital, a wireless system that leverages mmWave Multiple-input Multiple-output (MIMO) radar for synchronous multi-point vital sign monitoring. It integrates two reference modalities: five-channel seismocardiography (SCG) sensors and a one-channel electrocardiogram (ECG) electrode, enabling comprehensive radar-based research and performance validation across multiple physiological metrics. Additionally, we have developed a multi-modal signal processing framework, consisting of a radar signal processing module, an SCG calibration module, and a spatial alignment scheme. To evaluate the radar signal processing module, we conducted mathematical derivation and simulation. The experimental results indicate that the noncontact MultiVital system achieves multi-point synchronous monitoring with high precision, highly consistent with the results from reference modalities.

S. Mills, J. Lee, S. Boulidam et al.

Dolores Pereira Gómez, Sergio Hernández Gutiérrez

Mining and quarrying were important economic activities in Europe in past centuries, but during the 20th century, raw materials became vital to societal development. Mining has been subject to fluctuations related to wars, economic crises, and advances in environmental rights. A series of events led some European countries, such as Spain, to assume a leading position in the market for certain raw materials, such as tungsten. However, most of Europe’s mines have been abandoned. This paper considers several postindustrial heritage sites that can used to illustrate how metal and stone were extracted in past centuries. Such sites have become a tourist attraction in the context of heritage in some countries. This area in western Spain, which contains ancestral quarries that helped build the architectural heritage of UNESCO World Heritage Cities and artisanal mines that contributed to building the economy of an extremely poor population, has a story to tell. All these mines and quarries can be used to explain the cultural heritage of the area as part of a postindustrial heritage landscape.

Ivana Magdalenić, Mirjana Devedžić

Vincenzo Falco, Daniele Cuntrera, Massimo Attanasio

Xiang Zhang, Yu Gu, Huan Yan et al.

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

Gabriel Beltrão, Udo Schroeder

Continuous monitoring of vital signs is of paramount importance. These critical physiological parameters play a crucial role in the early detection of conditions that affect the well-being of a patient. However, conventional contact-based devices are inappropriate for long-term monitoring. Besides mobility restrictions, they can cause epidermal damage, and even lead to pressure necrosis. In this paper we present a selection of recent works towards enabling the contactless monitoring of vital signs using radar devices. The selected contributions are threefold: an algorithm for recovering the chest wall movements from radar signals; a random body movement and interference mitigation technique; and a robust and accurate adaptive estimation framework. These contributions were tested in different scenarios, spanning ideal simulation settings, real data collected while imitating common working conditions in an office environment, and a complete validation with premature babies in a critical care environment.

Martin Andrzejak, T. Knight, Carolin Plos et al.