Bayesian Framework Research Articles

Seismic fragility analysis of nuclear containment structure with Bayesian cloud analysis framework

Nuclear containment structure plays a critical role in safe operation of nuclear power plants. Seismic safety of nuclear power plant structures has become a hot issue in nuclear engineering community following Fukushima nuclear accident. Seismic fragility analysis, as a key element in probabilistic safety assessment of nuclear power plants, is widely used in nuclear power plant structures. This study presents an innovative method for seismic fragility analysis of engineering structures. The proposed methodology combines conventional Cloud analysis method and Bayesian framework. The accuracy and efficiency of the proposed methodology were demonstrated by a single-degree-of-freedom system, heavy computational efforts but high accuracy seismic fragility analysis method was adopted for comparison. Seismic fragility analysis of a typical prestressed concrete nuclear containment structure was analyzed using the proposed method. Results indicated that method proposed in this study can improve the accuracy of seismic fragility analysis. Furthermore, with the enhance of performance level, the increasing ratios for median seismic capacity of the nuclear containment structure was decreasing.

The Ubiquity of Time in Latent-cause Inference.

Humans have an outstanding ability to generalize from past experiences, which requires parsing continuously experienced events into discrete, coherent units, and relating them to similar past experiences. Time is a key element in this process; however, how temporal information is used in generalization remains unclear. Latent-cause inference provides a Bayesian framework for clustering experiences, by building a world model in which related experiences are generated by a shared cause. Here, we examine how temporal information is used in latent-cause inference, using a novel task in which participants see "microbe" stimuli and explicitly report the latent cause ("strain") they infer for each microbe. We show that humans incorporate time in their inference of latent causes, such that recently inferred latent causes are more likely to be inferred again. In particular, a "persistent" model, in which the latent cause inferred for one observation has a fixed probability of continuing to cause the next observation, explains the data significantly better than two other time-sensitive models, although extensive individual differences exist. We show that our task and this model have good psychometric properties, highlighting their potential use for quantifying individual differences in computational psychiatry or in neuroimaging studies.

Hot spots around Sagittarius A*. Joint fits to astrometry and polarimetry

Observations of Sagittarius A* ( in the near-infrared (NIR) show irregular flaring activity. Flares coincide with the astrometric rotation of the brightness centroid and with looping patterns in fractional linear polarization. These signatures can be explained with a model of a bright hot spot, transiently orbiting the black hole. We extend the capabilities of the existing algorithms to perform parameter estimation and model comparison in the Bayesian framework using NIR observations from the GRAVITY instrument, and simultaneously fitting the astrometric and polarimetric data. Using the numerical radiative transfer code we defined several geometric models describing a hot spot orbiting Sgr A*, threaded with a magnetic field, and emitting synchrotron radiation. We then explored the posterior space of our models with a nested sampling code We used Bayesian evidence to make comparisons between the models. We have used 11 models to sharpen our understanding of the importance of various aspects of the orbital model, such as non-Keplerian motion, hot-spot size, and off-equatorial orbit. All considered models converge to realizations that fit the data well, but the equatorial super-Keplerian model is favoured by the currently available NIR dataset. We have inferred an inclination of $ deg, which corroborates previous estimates, a preferred period of $ minutes, and an orbital radius of $ gravitational radii with the orbital velocity of $ times the Keplerian value. A hot spot with a diameter smaller than 5 gravitational radii is favoured. Black hole spin is not constrained well.

Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding

Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments.

Genome-wide association analysis reveals insights into the molecular etiology of dilated cardiomyopathy

Abstract Introduction Dilated cardiomyopathy (DCM) is a primary heart muscle disease characterized by ventricular dilatation and impaired contractility. While considered a Mendelian disorder, recent evidence highlights the important role of common genetic variation. Better characterization of the genetic architecture will provide opportunities for improved diagnosis, risk stratification and biological understanding. Methods We performed a GWAS meta-analysis of 14,256 DCM cases and 1,185,671 controls of European ancestry from 16 studies (GWASDCM). To improve discovery power, we performed multi-trait analysis of GWAS (GWASMTAG) with three genetically correlated and clinically relevant cardiac imaging traits in 36,203 individuals. To identify effector genes at loci, we used a two-step approach: 1) the nearest gene and top 3 genes prioritized by PoPS or V2G were selected as candidates; and 2) totality of evidence including 5 additional methods (coding variant, co-localization with expression, TWAS, ABC-model, and known Mendelian cardiomyopathy genes) was summarized in a single score. We performed rare variant burden analysis (MAF<0.1%) for predicted truncating variants (PTV) in established DCM-causing genes to highlight DCM genetic architecture, and in prioritized GWAS genes, against DCM and quantitative CMR traits in UK Biobank (UKB) and 100,000 Genomes Project (GeL), implemented by REGENIE. A polygenic score (PGS) generated using a Bayesian framework (PRS-CS) was assessed for association with the same outcomes in UKB participants, including among PTV carriers in established DCM-causing genes. Results We identified 80 independent loci, of which 65 were novel (62 loci in GWASDCM, and 54 loci in GWASMTAG) (Figure 1A). High-confidence genes were prioritized at 62 loci, including in genes with established roles in Mendelian cardiomyopathies (TTN, BAG3, FLNC, MYBPC3, FHOD3, ALPK3), and were enriched in key biological processes (Figure 1B). We characterized DCM genetic architecture across the allelic spectrum (Figure 2A). Rare variant burden analysis highlighted novel associations with DCM and quantitative traits for PTVs in NEDD4L and MAP3K7 in UKB that were replicated in GeL (Figure 2B). PGS was associated with DCM and quantitative CMR traits (OR per PGS SD 1.8, P<1x10-16) (Figure 2C). Individuals in the top centile had 4-fold increased risk of having DCM compared with median risk. PGS stratified penetrance of rare variants in 1,546 carriers of pathogenic variants (top quintile 7.3%, bottom quintile 1.7%, P 0.005). Conclusion We have performed the largest DCM GWAS, resulting in 80 significant loci (65 novel), and prioritized genes using a systematic approach. We identify novel DCM-causing genes (NEDD4L and MAP3K7) and generate a PGS that associates with DCM and modulates penetrance in carriers of Mendelian disease-causing rare variants.Figure 1Figure 2

TOPSE for babies in Norwegian: Examining the reliability of a tool to measure parenting self-efficacy

Abstract Background Parents’ confidence in their parenting abilities and self-efficacy (PSE) is an important factor for parenting practices. The Tool to measure Parenting Self-Efficacy (TOPSE) is a questionnaire created to evaluate parenting programs by measuring PSE. Initially, it was designed for parents with children between 0-6 years old. A modified version specifically for parents of infants aged 0-6 months (TOPSE for babies) is currently being piloted. In this study, we have investigated the reliability of the Norwegian version. Methods The study included 123 parents of children aged 0-18 months who completed a digital version of the TOPSE questionnaire. Professional translators performed the translation from English to Norwegian and a back translation in collaboration with the author group. Mean and standard deviation were calculated for each of the questionnaire’s six domains, and a reliability analysis was conducted using a Bayesian framework for the total sample (parents of children aged 0-18 months) and specifically for the parents of the youngest group of children (0-6 months). Results The overall Bayesian alpha coefficient for the six domains ranged from 0.54 to 0.83 for the entire sample, and from 0.63 to 0.86 for parents with children aged 0-6 months. For two of the domains, one item in each proved to primarily contribute to the low alpha coefficients and removing them improved the reliability, especially for parents of the youngest group of children. Conclusions Reliable tools to assess PSE and evaluate parenting programs is important. The Norwegian version of TOPSE for babies seems to be a reliable tool for measuring PSE, although there are variations across the children’s age groups and domains. Key messages • The findings from this study indicate that TOPSE in Norwegian is a reliable tool for assessing PSE. • While our findings are promising, further research is warranted to establish a more robust evaluation, across both mothers and fathers, parental age and socioeconomic groups.

Implications of age and sex on the diagnostic yield of sudden arrhythmic death syndrome

Abstract Introduction Sudden arrhythmic death syndrome (SADS) refers to an unexplained sudden death with a negative autopsy. An inherited cardiac condition (ICC) may be diagnosed through familial clinical evaluation (FE) or post-mortem genetic testing (molecular autopsy [MA]) or both. We aimed to investigate the impact of age and sex of SADS decedents on the diagnostic yield and aetiology to facilitate a targeted approach to management. Methods Consecutive referrals to a single centre for FE only, MA only or both, following a SADS death were included. First-degree family members underwent comprehensive FE. A panel of 36 cardiac genes was sequenced for MA. A post-mortem diagnosis could be obtained by either FE, MA or both. A Bayesian framework for analysis was performed to identify associations. Results Seven-hundred-and-sixty SADS decedents (66% male; mean age 31±12 years) were investigated. The overall diagnostic yield for an ICC was 37% (32-42%) and 9% (6-12%) for FE and MA cohorts, respectively. In a subset where both MA and FE were performed the diagnostic yield was 45% (38-61%). For each year increase in age, the relative risk of an FE diagnosis of long QT syndrome (LQTS) or Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT) versus remaining unexplained declined by 5.6% (RR 0.94 [0.91-0.98]) and by 11% (RR 0.89 [0.81-0.97]), respectively. Females were more likely to have a diagnosis compared with males by both FE (40% [34-45%] vs 36% [31-41%]) and MA (15% [10-21%] vs 6% [3-8%]). Females (8.1% [4.1-13.4%)], were more likely to be diagnosed with LQTS than males (1.2% [0.2-2.7%]) in the MA cohort. Conclusions After a SADS death, the diagnostic yield of comprehensive FE, MA or both in an expert setting can be up to 45%. Younger age of death is associated with greater likelihood of LQTS and CPVT diagnosis with the highest yield in children and adolescents. Female sex in SADS decedents is associated with a higher yield of LQTS in MA. These data highlight the relative utility of FE and MA depending on age and sex for determining underlying diagnoses following SADS deaths.

The proposal of a 15-minute city composite index through integrating GPS trajectory data-inferred urban function attraction based on the Bayesian framework

The chrono-urbanism framework suggests that urban life quality decreases with increased time spent in transportation, particularly motorized modes. The 15-min city concept, aligned with chrono-urbanism, emphasizes the importance of reaching essential urban functions within a short active travel time and has gained global attention. This study aims to develop a 15-min city composite index score (CIS) using the chrono-urbanism framework. The CIS integrates spatial accessibility to six key urban functions, providing a holistic assessment of the 15-min city status in Wuhan, China. Urban function accessibility is computed based on visitor volume inferred from GPS trajectory data using the Bayesian framework. Results reveal CIS hotspots in Hongshan, Wuchang, Jiangan, and Qingshan districts, with Hongshan having the highest concentration. However, Wuhan faces challenges in achieving comprehensive 15-min city status, as hotspots are mainly concentrated in specific areas like university towns and traditional city centers. The proposed assessment approach is applicable to accurately evaluate the 15-min status in other urban contexts using GPS trajectory data. The study's findings can assist policymakers in understanding CIS hotspot distribution and developing future planning policies to enhance the overall 15-min city status.

A network meta-analysis of different acupuncture therapy in the treatment of poststroke cognitive impairment and dementia.

Poststroke cognitive impairment and dementia (PSCID) is a major cause of stroke-related morbidities and mortalities. Over the last few years, there has been growing evidence supporting the effectiveness of needle-related treatments in PSCID. Our goal was to rate the included therapies and assess the clinical effectiveness of various needle-related treatments in patients with PSCID. We searched PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Chinese Biomedical Literature Service System (SinoMed), Wanfang, FDA.gov, and ClinicalTrials.gov. A mix of subject terms and free words was used to search the databases. The retrieval period was from the inception date of the database to February, 2023. We included SRs and MAs from acupuncture RCTs of patients with PSCID. The Cochrane Risk Assessment Scale was used to evaluate the risk of bias in the included studies. State 17.0 was used for network meta-analysis in accordance with the Bayesian framework. There were 34 studies total of 2690 patients. The cumulative ranking curve (SUCRA) revealed that CT + CFT + EA was the most efficient intervention to improve (Mini-Mental State Examination, MMSE) efficiency, followed by CT + CFT + AP to improve (Montreal Cognitive Assessment, MoCA) efficiency, CT + CFT + ACU for improving (Activities of Daily Living scale, ADL) scores, and CT + CFT + EA to improve clinical efficiency. The results show that Different acupuncture methods can improve cognitive function and daily living ability in patients with PSCID. Network meta-analysis revealed that both CT + CFT + ACU and CT + CFT + EA appeared to be more beneficial for daily living activities, while CT + CFT + EA and CT + CFT + AP appeared to be more helpful for cognitive performance in patients with PSCID. Treatments including acupuncture are safer and have a reduced incidence of negative side effects.

Evidence to inform spatiotemporal management of a western Pacific Ocean tuna purse seine fishery.

Fisheries can profoundly impact co-occurring species exposed to incidental capture. Spatiotemporal fisheries management holds substantial potential to balance socioeconomic benefits with ecological costs to threatened bycatch species. This study estimated the effect of the spatial and temporal distribution of effort by a western Pacific Ocean tuna purse seine fishery on catch rates of target and at-risk species by fitting spatially explicit generalized additive multilevel regression models within a Bayesian inference framework to observer data. Mean field prediction surfaces defined catch rate hotspots for tunas, silky sharks, rays, and whale sharks, informing the design of candidate area-based management strategies. Due to limited sample sizes, odontocete and marine turtle catch rate geospatial patterns were summarized using simple 2D hexagonal binning. Effort could be focused in two areas within core fishing grounds to reduce overlap with hotspots for silky sharks, rays, and whale sharks without affecting target catch. Effort could be shifted outside of core fishing areas to zones with higher target tuna catch rates to reduce overlap with hotspots for at-risk species. Sparse and small marine turtle and whale shark hotspots occurred across the fishing grounds. Results did not identify opportunities for temporally dynamic spatial management to balance target and at-risk catch rates. Research on the economic and operational viability of alternative spatial management strategies is a priority. A small subset of sets had disproportionately large odontocete captures. Real-time fleet communication, move-on rules, and avoiding sets on dolphin schools might reduce odontocete catch rates. Managing set association type and mesh size present additional opportunities to balance catch rates of at-risk and target species. Employing output controls that effectively constrain the fishery would alter the spatial management strategy to focus fishing within zones with the lowest ratio of at-risk bycatch to target tuna catch. Findings inform the design of alternative spatial management strategies to avoid catch rate hotspots of at-risk species without compromising the catch of principal market species.

Bayesian Multilayered Mediation Analysis for Cancer Pharmacogenomics

ABSTRACTMultiomic data from multilevel biological systems are becoming common and motivate integrative modelling approaches to decipher within‐ and cross‐platform dependencies. Mediation analysis aims to identify mediating mechanisms that regulate the effect of an exposure on an outcome. In multiomic contexts, identification of genomic mediators of disease outcomes provides a deeper understanding of mechanisms of disease and corresponding therapeutic targets. While there has been significant work on joint modelling of high‐dimensional potential mediators, approaches that can identify individual mediators in presence of high‐dimensional potential mediators are lacking. We posit that the multiomic data are interrelated following multilayered Gaussian graphical models that include undirected and directed acyclic graphs as special cases. We develop a Bayesian inferential framework for multilayered mediation analysis with continuous, binary, and ordinal outcomes using probit models. As opposed to existing approaches focusing on identifying joint mediation effects, we decompose the joint effect into effects attributable to individual mediators in the framework of interventional mediation analysis. Simulations demonstrate our method outperforms other existing approaches to identify mediators that have nonzero indirect effects to the outcome. We apply our method to multiomic analysis on drug sensitivity outcomes of palbociclib and agents for endocrine therapy, standard care for breast cancer.

Bayesian finite element model inversion of offshore wind turbine structures for joint parameter-load estimation

Operating in harsh and unsteady marine environment, offshore wind turbine (OWT) structures are exposed to unpredictable wind and wave loads. Identifying the structural loads and their effects on the OWTs allow for predicting the remaining fatigue life of these structures and improving the structural design procedure. In this paper, a finite element (FE) model inversion method is presented to estimate the unknown loads and model parameters of OWTs using sparse measurement data. A realistic FE model of an OWT structure with jacket substructure is created in the open-source simulation platform, OpenSees. A Bayesian inference framework is presented to integrate the measured data with the FE model to estimate unknown wind loads and mass of rotor-nacelle assembly. To evaluate the performance of this data assimilation framework, the effect of sensor type, number of sensors, and modeling errors on the estimation accuracy of wind loads and model parameters are investigated through different case studies where synthetic data are used as measurements. The results of this study are important to guide instrumentation of new OWT structures, and to understand the potential limitations and sources of error in the real-world application of this data assimilation framework for joint model parameter and input load estimation.

Bayesian Empirical Likelihood Regression for Semiparametric Estimation of Optimal Dynamic Treatment Regimes.

We propose a semiparametric approach to Bayesian modeling of dynamic treatment regimes that is built on a Bayesian likelihood-based regression estimation framework. Methods based on this framework exhibit a probabilistic coherence property that leads to accurate estimation of the optimal dynamic treatment regime. Unlike most Bayesian estimation methods, our proposed method avoids strong distributional assumptions for the intermediate and final outcomes by utilizing empirical likelihoods. Our proposed method allows for either linear, or more flexible forms of mean functions for the stagewise outcomes. A variational Bayes approximation is used for computation to avoid common pitfalls associated with Markov Chain Monte Carlo approaches coupled with empirical likelihood. Through simulations and analysis of the STAR*D sequential randomized trial data, our proposed method demonstrates superior accuracy over Q-learning and parametric Bayesian likelihood-based regression estimation, particularly when the parametric assumptions of regression error distributions may be potentially violated.

Assessing the robustness of the Galactic rotation curve inferred from the Jeans equations using Gaia DR3 and cosmological simulations

Several authors have recently applied Jeans modelling to -based datasets to infer the circular velocity curve for the Milky Way. These works have consistently found evidence for a continuous decline in the rotation curve beyond $ kpc $, which may indicate the existence of a light dark matter (DM) halo. Using a large sample of Gaia DR3 data, we aim to derive the rotation curve of the Milky Way using the Jeans equations, and to quantify the role of systematic effects, both in the data and those inherent to the Jeans methodology under the assumptions of axisymmetry and time independence. We used data from the Gaia DR3 radial velocity spectrometer sample, supplemented with distances inferred through Bayesian frameworks, to determine the radial variation of the second moments of the velocity distribution for stars close to the Galactic plane. We used these profiles to determine the rotation curve using the Jeans equations under the assumption of axisymmetry and explored how they vary with azimuth and position above and below the plane of the Galactic disc. We applied the same methodology to an N-body simulation of a Milky Way-like galaxy impacted by a satellite akin the Sagittarius dwarf, and to the Auriga suite of cosmological simulations. The circular velocity curve we infer for the Milky Way is consistent with previous findings out to $ 15$ kpc, where our statistics are robust. Due to the larger number of stars in our sample, we are able to reveal evidence of disequilibrium and deviations from axisymmetry closer in. For example, we find that the second moment of $V_R$ flattens out at $R kpc, and that the second moment of $V_ is different above and below the plane for $R kpc. Our exploration of the simulations indicates that these features are typical of galaxies that have been perturbed by external satellites. From the simulations, we also estimate that the difference between the true circular velocity curve and that inferred from Jeans equations can be as high as $15<!PCT!>$, but that it is likely of the order of $10<!PCT!>$ for the Milky Way. This is higher than the systematic uncertainties associated with the observations or those linked to most modelling assumptions when using the Jeans equations. However, if the density of the tracer population were truncated at large radii instead of being exponential as often assumed, this could lead to the erroneous conclusion of a steeply declining rotation curve. We find that steady-state axisymmetric Jeans modelling becomes less robust at large radii, indicating that particular caution must be exercised when interpreting the rotation curve inferred in those regions. A more careful and sophisticated approach may be necessary for precision measurements of the DM content of our Galaxy.

A nonlinear inversion method for Young’s modulus and shear modulus based on the exact Zoeppritz equations

IntroductionYoung’s modulus and shear modulus are essential mechanical parameters for evaluating subsurface rocks, playing a pivotal role in the exploration and development of unconventional resources. Young’s modulus indicates the brittleness of the reservoir, while shear modulus determines the ease of fracturing rock layers.MethodsTraditional methods estimate these moduli through indirect calculations and approximate expressions, which are prone to cumulative errors and rely on multiple assumptions, reducing inversion accuracy. This paper presents a direct inversion method for acquiring of Young’s modulus and shear modulus using the exact Zoeppritz equations, integrated within a Bayesian framework for pre-stack inversion. The quantum particle swarm optimization (QPSO) algorithm is introduced to achieve a nonlinear solution to the objective function.ResultsTests on synthetic and actual field data demonstrate the feasibility and effectiveness of the proposed method, yielding more accurate inversion results compared to traditional methods.DiscussionThese findings provide valuable insights for predicting reservoir brittleness and characterizing reservoirs in unconventional shale gas exploration and development.

Genomic epidemiology and evolutionary dynamics of the Omicron variant of SARS-CoV-2 during the fifth wave of COVID-19 in Pakistan

IntroductionThe coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed extraordinary challenges to global health systems and economies. The virus’s rapid evolution has resulted in several variants of concern (VOCs), including the highly transmissible Omicron variant, characterized by extensive mutations. In this study, we investigated the genetic diversity, population differentiation, and evolutionary dynamics of the Omicron VOC during the fifth wave of COVID-19 in Pakistan.MethodsA total of 954 Omicron genomes sequenced during the fifth wave of COVID-19 in Pakistan were analyzed. A Bayesian framework was employed for phylogenetic reconstructions, molecular dating, and population dynamics analysis.ResultsUsing a population genomics approach, we analyzed Pakistani Omicron samples, revealing low within-population genetic diversity and significant structural variation in the spike (S) protein. Phylogenetic analysis showed that the Omicron variant in Pakistan originated from two distinct lineages, BA.1 and BA.2, which were introduced from South Africa, Thailand, Spain, and Belgium. Omicron-specific mutations, including those in the receptor-binding domain, were identified. The estimated molecular evolutionary rate was 2.562E-3 mutations per site per year (95% HPD interval: 8.8067E-4 to 4.1462E-3). Bayesian skyline plot analysis indicated a significant population expansion at the end of 2021, coinciding with the global Omicron outbreak. Comparative analysis with other VOCs showed Omicron as a highly divergent, monophyletic group, suggesting a unique evolutionary pathway.ConclusionsThis study provides a comprehensive overview of Omicron’s genetic diversity, genomic epidemiology, and evolutionary dynamics in Pakistan, emphasizing the need for global collaboration in monitoring variants and enhancing pandemic preparedness.

Seismic Discrimination Using Regional Distance Coda-Wave Ratios

Abstract This study utilizes regional seismic coda-wave amplitude ratios to distinguish earthquakes and declared nuclear explosions in the Democratic People’s Republic of Korea (DPRK). We compute the coda spectral ratios at common stations to isolate source information from propagation and site effects. The resulting frequency-dependent amplitude ratios help identify differences between source types at DPRK. We use parametric models to validate observations, linking them to seismic source parameters. Our method effectively distinguishes the spectral signatures of explosions, nearby earthquakes, and a post-explosion collapse event. The measurements show clear differences between source-type combinations and agree with estimated seismic source models. Notably, the coda spectral ratios between explosions and earthquakes indicate that explosive sources exhibit relatively lower high-frequency S-wave energy and corner frequency compared to P waves. We leverage these spectral differences to develop a Bayesian inference framework for seismic source discrimination at the DPRK test site. This study demonstrates that spectral differences between source types can be exploited in coda spectral ratios to provide critical information for seismic discrimination.

Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong

Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.

Differential responses to weather and land‐cover conditions explain spatial variation in winter abundance trends in a migratory bird of conservation concern

Abstract Effective conservation strategies for animal populations require knowledge of relationships between population dynamics and their environmental drivers. However, these processes often vary within animal populations, requiring site‐specific conservation planning. Given limited financial resources, identifying groups of sites with similar population dynamics can help practitioners efficiently implement conservation programs to larger areas. We evaluated spatial patterns and environmental drivers of wintering site trends in a migratory bird of conservation concern, the Greenland white‐fronted goose (Anser albifrons flavirostris). We used latent class analysis to identify trend patterns in 35 years of abundance data among 59 geographically discrete Greenland white‐fronted goose wintering sites. We developed a state‐space abundance model in a Bayesian framework to quantify the effects of weather and land‐cover conditions experienced throughout spring migration, summer breeding, autumn migration and wintering periods on variation in wintering site abundance. We identified two main patterns in Greenland white‐fronted goose abundance trends: northeastern wintering sites declined on average by 3% per year, while southwestern wintering sites declined on average by 14% per year. Differential responses to weather and habitat conditions likely explained variation among groups, as geese at southwestern wintering sites were more negatively affected by harsh weather conditions (e.g. low temperatures and high precipitation on breeding areas) and poor habitat conditions (i.e. low‐quality grasslands and croplands) on wintering areas. Future conservation efforts to improve the suitability and nutritional quality of agricultural areas, especially cereal croplands in autumn and early winter and grasslands in late winter and early spring, could potentially improve local habitat conditions, especially in the southwestern wintering sites where abundance declines were steepest. Synthesis and applications. We demonstrate the potential to delineate animal populations based on spatial patterns in population dynamics using long‐term abundance monitoring data, which are commonly the only available data for conservation practitioners. By grouping sites based on spatial patterns in local abundance trends, we can further test hypotheses about how these groups are differentially affected by changing environmental conditions. This information is important for informing efficient conservation planning over large areas when financial resources are limited.

Heat impacts on an aging society: a spatio-temporal analysis of heatstroke deaths in Japan

Abstract Climate change, especially through heatwaves, significantly affects human health and is a growing global concern. Concurrently, population aging is occurring worldwide, with many countries experiencing an increase in their elderly populations. As the elderly are particularly susceptible to extreme temperatures and unevenly distributed due to internal migration of younger populations, a spatio-temporal analysis integrating temperature changes and demographic data is essential. This study focuses on Japan, a super-aged society where over 25% of the total population is 65 years or older. We examined the effects of climate and the proportion of the elderly population on heatstroke deaths through spatio-temporal analysis within a Bayesian framework. We estimated the annual relative risk of heatstroke-related deaths at the prefecture level from 2008 to 2019. The results indicate a strong spatial autocorrelation in heatstroke deaths across Japan. The spatio-temporal interaction model was the best-performing, showing that regional and temporal variations significantly impact heatstroke mortality. In this model, a one-degree increase in temperature anomaly was linked to a 0.35 (95% CI 0.25 to 0.46) times higher odds of heatstroke deaths, while a 1% increase in the population aged 65 years or older was associated with 4.85 (95% CI 0.92 to 8.65) times higher odds. We found that not only metropolitan areas but also rural areas, such as the Tohoku and Shikoku regions, face a high risk of heatstroke, emphasizing the need to address the challenges in rural communities. Our study highlights the necessity of integrating temperature changes and demographic data in a spatio-temporal context for heatstroke risk assessment. It demonstrates the profound effects of temperature anomalies and the proportion of the elderly population on heatstroke mortality. This research framework could be applicable to other countries experiencing aging and heatwave issues, aiding in the development of targeted public health interventions.