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Purpose: This study aimed to cross-validate a recently proposed equation for the prediction of maximal oxygen uptake ( V ˙ O 2 max) in cycling exercise by using the average power output normalized by the body mass from a 5-minute time trial (RPO5-min) as the independent variable. Further, the study aimed to update the predictive equation using Bayesian informative prior distributions and meta-analysis. Methods: On different days, 49 male cyclists performed an incremental graded exercise test until exhaustion and a 5-minute time trial on a stationary cycle ergometer. We compared the actual V ˙ O 2 max with the predicted value obtained from the RPO5-min, using a modified Bayesian Bland–Altman agreement analysis. In addition, this study updated the data on the linear regression between V ˙ O 2 max and RPO5-min, by incorporating information from a previous study as a Bayesian informative prior distribution or via meta-analysis. Results: On average, the predicted V ˙ O 2 max using RPO5-min underestimated the actual V ˙ O 2 max by −6.6 mL·kg–1·min–1 (95% credible interval, −8.6 to −4.7 mL·kg–1·min–1). The lower and upper 95% limits of agreement were −17.2 (−22.7 to −12.3) and 3.8 (−1.0 to 9.5) mL·kg–1·min–1, respectively. When the current study's data were analyzed using the previously published data as a Bayesian informative prior distribution, the accuracy of predicting sample means was found to be better when compared with the data combined via meta-analyses. Conclusions: The proposed equation presented systematic bias in our sample, in which the prediction underestimated the actual V ˙ O 2 max. We provide an updated equation using the previous one as the prior distribution, which could be generalized to a greater audience of cyclists. [ABSTRACT FROM AUTHOR]

Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging. In this paper, we introduce TinyNS, the first platform-aware neurosymbolic architecture search framework for joint optimization of symbolic and neural operators. TinyNS provides recipes and parsers to automatically write microcontroller code for five types of neurosymbolic models, combining the context awareness and integrity of symbolic techniques with the robustness and performance of machine learning models. TinyNS uses a fast, gradient-free, black-box Bayesian optimizer over discontinuous, conditional, numeric, and categorical search spaces to find the best synergy of symbolic code and neural networks within the hardware resource budget. To guarantee deployability, TinyNS talks to the target hardware during the optimization process. We showcase the utility of TinyNS by deploying microcontroller-class neurosymbolic models through several case studies. In all use cases, TinyNS outperforms purely neural or purely symbolic approaches while guaranteeing execution on real hardware.

To succeed in the global market, firms must prioritize quality over individual goals and preferences. Acceptance sampling is one of the two primary approaches for ensuring quality, which is used in statistical process control for attributes inspection of the product. In acceptance sampling based on predetermined acceptance criteria for inspection the lot is either accepted or not accepted. This research introduces a Bayesian double group sampling plan (BDGSP) for estimating average number of nonconforming products. Based on acceptance criteria the Poisson distribution is used to construct the likelihood function for both nonconforming and conforming products. To calculate the average probability of acceptance, the gamma distribution is used as a suitable prior distribution of the Poisson distribution. For various suggested producer's and consumer's risk levels four distinct quality regions are calculated. The producer's risk is related to acceptable quality level and consumer's risk is related to and limiting quality level. Operating characteristic curves are utilized to track the effects of changes in the values of the specified parameters. The applicability of the proposed plan for current industrial strategies is demonstrated using a real dataset. [ABSTRACT FROM AUTHOR]

Introduction: Precise understanding of proliferative activity in breast cancer holds significant value in the monitoring of neoadjuvant treatment, while current immunostaining of Ki-67 from biopsy or resected tumour suffers from partial sampling error. Multi-compartment model of transverse relaxation time has been proposed to differentiate intra- and extra-cellular space and biochemical environment but susceptible to noise, with recent development of Bayesian algorithm suggested to improve robustness. We hence hypothesise that intra- and extra-cellular transverse relaxation times using Bayesian algorithm might be sensitive to proliferative activity. Materials and methods: Twenty whole tumour specimens freshly excised from patients with invasive ductal carcinoma were scanned on a 3 T clinical scanner. The overall transverse relaxation time was computed using a single-compartment model with the non-linear least squares algorithm, while intra- and extra-cellular transverse relaxation times were computed using a multi-compartment model with the Bayesian algorithm. Immunostaining of Ki-67 was conducted, yielding 9 and 11 cases with high and low proliferating activities respectively. Results: For single-compartment model, there was a significant higher overall transverse relaxation time (p = 0.031) in high (83.55 ± 7.38 ms) against low (73.30 ± 11.30 ms) proliferating tumours. For multi-compartment model, there was a significant higher intra-cellular transverse relaxation time (p = 0.047) in high (73.52 ± 10.92 ms) against low (61.30 ± 14.01 ms) proliferating tumours. There was no significant difference in extra-cellular transverse relaxation time (p = 0.203) between high and low proliferating tumours. Conclusions: Overall and Bayesian intra-cellular transverse relaxation times are associated with proliferative activities in breast tumours, potentially serving as a non-invasive imaging marker for neoadjuvant treatment monitoring. [ABSTRACT FROM AUTHOR]

Spatiotemporal point process models have a rich history of effectively modeling event data in space and time. However, they are sometimes neglected due to the difficulty of implementing them. There is a lack of packages with the ability to perform inference for these models, particularly in python. Thus we present BSTPP a python package for Bayesian inference on spatiotemporal point processes. It offers three different kinds of models: space-time separable Log Gaussian Cox, Hawkes, and Cox Hawkes. Users may employ the predefined trigger parameterizations for the Hawkes models, or they may implement their own trigger functions with the extendable Trigger module. For the Cox models, posterior inference on the Gaussian processes is sped up with a pre-trained Variational Auto Encoder (VAE). The package includes a new flexible pre-trained VAE. We validate the model through simulation studies and then explore it by applying it to shooting data in Chicago. [ABSTRACT FROM AUTHOR]

Background: An estimated 75.8 under-five deaths per 1000 live births occurred in sub-Saharan Africa. This study aimed to investigate the prevalence and factors associated with under-five mortality in Sub-Saharan Africa. Methods: This study was based on secondary data sources from 33 Sub-Saharan countries' recent Demography and Health surveys from 2010 to 2020. A weighted sample of 360,397 under-five children was included in the study. Bayesian multilevel binary logistic regression was fitted using the brms R package. Besides, leave one out information criteria was used for model comparison. The adjusted odds ratio (AOR) and its 95% credible interval (CrI) were reported for significant factors associated with under-five mortality. Clinical trial number: not applicable. Results: The prevalence of under-five mortality in sub Saharan Africa was 62 per 1000 live births (95%CI (56.29, 68.29). In sub region of SSA, it was 65 in central, 52 in eastern, 50 in southern and 73 in western region per 1000 live births. Multiple birth (AOR = 5.27; 95%CrI: 4.72, 5.87), number of under-five children 3 to 5 (AOR = 3.31; 95%CrI: 3.01, 3.60), caesarean section delivery (AOR = 1.64; 95%CrI: 1.47, 1.83), being unmarried (AOR = 1.16; 95%CrI 1.08, 1.26), using unimproved toilet (AOR = 1.08;95%CrI: 1.02, 1.16), birth order of 4th to 6th (AOR = 1.18; 95%CrI:1.1, 1.25),were risk factors of under-five mortality. Whereas, being female (AOR = 0.86; 95%CrI: 0.82, 0.91), preceding birth interval of 24–35 months (AOR = 0.61; 95%CrI: 0.57, 0.65) and above 36 months (AOR = 0.48; 95%CrI: 0.43, 0.49), ANC visit (AOR = 0.80; 95%CrI: 0.74 0.86), contraceptive use (AOR = 0.57; 95%CrI: 0.53, 0.61), were preventive factors of under-five mortality. Conclusion: Under-five mortality remains the highest in sub-Saharan Africa. Most of the risk factors of under-five mortality were found to be preventable. Policymakers and other stakeholders should enhance maternal education, lengthen birth interval, ANC visit, improved toilet facilities and, giving special attention to small size child and cesarean section delivery to reduce under-five mortality. [ABSTRACT FROM AUTHOR]

Objective: To demonstrate the application of Bayes' theorem to diagnostic testing in clinical settings, especially with respect to rare diseases, enhancing an understanding of pre-test probability and its implications. Conclusion: Bayes' theorem enables the revision of the conditional probabilities of an event occurring when new information is acquired. It demonstrates that when the prevalence of a disease is very low, there are a high number of false positives, thereby reducing the clinical utility and cost benefit profile of the diagnostic test, even in the presence of relatively high sensitivities and specificities of the chosen test. [ABSTRACT FROM AUTHOR]

Nonprofit organizations are vital to the implementation of social policy and provide myriad benefits to those nearby, yet few studies consider founding patterns in small areas. Conceptualizing ecological processes at the local level and in the context of developmental regimes, this article investigates nonprofit founding events among neighborhoods with a unique data set aggregated by census tract from 2010 to 2016 in one northeast Ohio county (United States) using a hierarchical Bayesian model. The results broadly support the density dependence hypothesis, however, suggest high density is required to reduce founding rates in small areas. The results indicate nonprofit founding rates are lower in tracts with higher levels of economic disadvantage and higher shares of Latino residents. The paper calls for further research into nonprofit population dynamics among small areas and for policymakers to closely consider nonprofit founding events, as founding determines the future of a region's nonprofit infrastructure. [ABSTRACT FROM AUTHOR]

Devil rays (Mobula spp.) are caught in fisheries across the Indian Ocean, with reports of significant recent declines in catch and sightings. Globally, the few populations studied have extremely low population growth rates due to low fecundity and long reproductive cycles, making them highly vulnerable to overfishing. To allow for assessment of the current sustainability of devil ray catch in the Indian Ocean, we provide estimates of age using the caudal vertebrae; somatic growth using a Bayesian, multi-model approach; maximum intrinsic rate of population increase (rmax); and fishing mortality for Endangered Spinetail Devil Ray (Mobula mobular) and Bentfin Devil Ray (M. thurstoni) sampled from small-scale fisheries catch in Indonesia, Kenya, and Pakistan. The oldest individuals of Spinetail Devil Ray (n = 79) and Bentfin Devil Ray (n = 59) were 17.5 and six years, respectively. Both species had relatively low growth coefficients (k = 0.05 and g = 0.19 year−1, respectively), with the von Bertalanffy and logistic models providing the best fitting growth models, and low rmax (0.109 and 0.107 year−1, respectively) indicating that they are highly sensitive to overexploitation. Fishing mortality F estimates (0.16 and 0.18 year−1, respectively) were higher than rmax and exploitation ratio E (0.77 and 0.80, respectively) were higher than an optimum value of 0.5 for biological sustainability for both species, suggesting that the fisheries catches of the species are unsustainable. We demonstrate an approach to assess data-poor species and apply this to two Indian Ocean devil ray species. The results highlight the urgent need for better management actions to reduce the catch of all devil rays to prevent species extinction and aid in population recovery. [ABSTRACT FROM AUTHOR]

Subnational estimation is an important and challenging problem in the context of the National Crime Victimization Survey (NCVS). Direct estimates for subnational domains are often unreliable due to small sample sizes. Model-based procedures have potential to improve upon direct estimators. However, model-based estimation presents several new challenges. One must identify suitable covariates, and an appropriate model form must be specified. This article discusses issues associated with the formulation of a small area model for production of state-level estimates of crime victimization rates. An analysis of direct estimates and covariates motivates the development of a Bayesian multivariate model. A model in the original scale is compared to a model in the log scale. Efficiency gains from the model relative to the direct estimators are examined. One challenge is that direct estimates can equal zero. Two ways of handling zero direct estimates are discussed. [ABSTRACT FROM AUTHOR]

Micro-milling is a crucial machining technology used for complex and precise 3D parts and plays a crucial role in modern manufacturing. In this work, to comprehensively evaluate the safety, economic feasibility, and environmental friendliness of the micro-milling processing process, a sustainable optimization framework including reliability assessment is designed. Firstly, modeling and experimental measurements are conducted on sustainability evaluation standards such as tool life, machining process, machining surface quality, material removal rate, machining cost, and carbon emissions. Subsequently, the influence of physical information such as observation/measurement data and processing errors is analyzed, and a Bayesian update method is proposed and integrated with neural networks to precisely inversion the probability distribution of parameters within the optimization cycle under conditions of uncertainty. Finally, combined with the overall sustainability indicators, the multi-objective optimization process is refined through penalty functions and weighting methods, aiming to achieve the best balance between processing performance and sustainability. The simulation and experimental results have verified the superiority of the developed framework, and the present method provides valuable guidance for sustainable micromanufacturing. [ABSTRACT FROM AUTHOR]

The projection predictive variable selection is a decision-theoretically justified Bayesian variable selection approach achieving an outstanding trade-off between predictive performance and sparsity. Its projection problem is not easy to solve in general because it is based on the Kullback–Leibler divergence from a restricted posterior predictive distribution of the so-called reference model to the parameter-conditional predictive distribution of a candidate model. Previous work showed how this projection problem can be solved for response families employed in generalized linear models and how an approximate latent-space approach can be used for many other response families. Here, we present an exact projection method for all response families with discrete and finite support, called the augmented-data projection. A simulation study for an ordinal response family shows that the proposed method performs better than or similarly to the previously proposed approximate latent-space projection. The cost of the slightly better performance of the augmented-data projection is a substantial increase in runtime. Thus, if the augmented-data projection's runtime is too high, we recommend the latent projection in the early phase of the model-building workflow and the augmented-data projection for final results. The ordinal response family from our simulation study is supported by both projection methods, but we also include a real-world cancer subtyping example with a nominal response family, a case that is not supported by the latent projection. [ABSTRACT FROM AUTHOR]

An influential argument is that mental processes can be explained at three different levels of analysis: the functional, algorithmic, and implementation level. Identity attribution (the process whereby an identity is attributed to another individual or to the self) has been rarely explored at the functional level. To address this, here I propose a theory of identity attribution grounded on Bayesian inference, being the latter a well-established functional perspective in cognitive science. The theory posits that an identity is inferred based on observations about a target's features, about the context, and about motivational factors. This inference can be made based upon multiple sources of observations, with prior beliefs becoming more prominent when observations are fewer in number. The theory offers an interpretation of key processes driving identity attribution, potentially providing a platform for integrating different perspectives on identity in psychology and sociology. [ABSTRACT FROM AUTHOR]

Brake systems are subjected to various factors such as wear and fatigue over a long period of time. They bring a great challenge to the reliability analysis of the braking system. In this paper, the hyperellipsoid model is used and the hyperellipsoid dynamic Bayesian network model is constructed. The model synthesizes the strengths of the dynamic Bayesian network with the precise constraint capability of the superellipsoid model, offering a novel methodology for the reliability analysis of the braking system. In order to verify the validity of the super ellipsoidal dynamic Bayesian network model, this paper applies it to the reliability analysis of the braking system and compares it with the traditional interval Bayesian network. The results show that the hyperellipsoidal dynamic Bayesian network model enhances the range accuracy of the probability intervals by 54.06%. This solves the conservatism problem of interval Bayesian networks. This suggests that the method improves the accuracy of brake system reliability analysis. [ABSTRACT FROM AUTHOR]

This paper aims to discuss Prof. Efthymios (Mike) Tsionas' work on asset pricing and highlight his original contributions, as well as the necessary foundation in mathematics, econometrics, economics, statistics, and programming. [ABSTRACT FROM AUTHOR]

This study proposes a novel Bayesian damage identification method that uses an Improved Elemental Modal Strain Energy Ratio (IEMSER) to guide a sparse prior distribution. Measured frequencies and mode shapes develop the IEMSER indicator for preliminary damage assessment, forming the basis for a sparse prior distribution. Using the sparse prior and initial damage estimates, Markov Chain Monte Carlo (MCMC) sampling computes the posterior Probability Density Functions (PDFs) of damage parameters to determine the Maximum A Posteriori (MAP) estimates. The proposed method better utilizes the advantages of prior information in the Bayesian method, making the identified damage more accurate. A numerical case of a steel truss bridge shows that IEMSER's preliminary damage estimates closely match actual damage, yielding a reliable sparse prior and significantly improving identification accuracy. The method's effectiveness is further validated using modal test data from an 18-story frame structure, confirming its applicability to real structures. [ABSTRACT FROM AUTHOR]

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffer from imperfect detection, including false positives (eg, travel-related cases reported outside of endemic area) and false negatives (eg, misdiagnosis or underreporting). We proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides , accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting. This article is part of a Special Collection on Environmental Epidemiology. [ABSTRACT FROM AUTHOR]

Background: Childhood nutrition plays an important role in the promotion of long-term health. Introducing solid foods in alignment with the Mediterranean Diet during weaning fosters a preference for healthy foods early in life. However, access to nutritious diets remains a challenge in underserved communities. Scampia, a socioeconomically disadvantaged district in Naples, Italy, exemplifies a community where barriers to healthy eating persist. This research reports a trial protocol that plans for a study to evaluate the impact of the Mediterranean Diet on child health and to establish preventive strategies for chronic diseases. Methods: The PEMED (PrEventive effect of MEditerranean Diet in Children) trial is a Bayesian Sequential Pragmatic Cluster Randomized Clinical Trial. Family Pediatricians (FPs) are randomized to deliver either Mediterranean Diet-based dietary guidance starting at weaning or standard dietary practices using typical baby foods. Children will be followed up for six years, with regular assessments of growth, microbiome composition, and adherence to the Mediterranean Diet, using validated tools. Interim analyses will be conducted at three-year intervals to evaluate the efficacy and monitor adverse events. Saliva and stool samples will be collected for genetic and microbiome analyses, and adherence will be monitored through quarterly dietary recalls and biomarkers. Results: This trial will consider Italy's established FP network for implementing innovative dietary intervention in a real-world setting. Conclusions: This study will address nutritional disparities in the underserved Scampia community and provide a scalable model for early dietary interventions. The results will shed light on the role of the Mediterranean Diet in improving childhood health and informing public health strategies globally. [ABSTRACT FROM AUTHOR]

Micro-milling technology is widely employed in manufacturing micro-precision parts due to its flexible processing and high accuracy. Compared to the macro-milling process, micro-milling has a smaller machining size, which leads to tool wear, tool runout, and other factors being more sensitive to the impact of machining quality. This work proposes a reliability evaluation framework for machining systems based on the micro-milling mechanical model and its surrogate method. Establish a cutting force model under shear and plow conditions with periodic variations in instantaneous uncut thickness. Subsequently, the probability distribution of the cutting parameters is obtained based on Bayesian updates. An improved buffer failure probability method is proposed to quickly get the micro-milling system failure probability. Developing new ensemble and adaptive sampling methods and introducing stochastic configuration network (SCN), an adaptive stochastic configuration network ensemble (ASCNE) model is established to alleviate the time-consuming problem of repeated calculations of mechanical models in reliability analysis. Experimental results indicate that the mechanical model can achieve good predictive performance, and the constructed ASCNE model can achieve high-precision surrogate effects. Additionally, the reliability analysis results of the system can guide tool replacement during the machining process, ensuring safe and efficient execution of the micro-milling. [ABSTRACT FROM AUTHOR]

This study develops a three-state Markov framework to estimate the transition rates between normal, preclinical screen-detectable phase (PCDP), and clinical breast cancer using simulated data. Two exponential models are explored: a five-mode transition model and a six-mode transition model, the latter incorporating exact cancer case timings. Each model is analyzed both with and without covariates to evaluate their influence on breast cancer progression. Parameters are estimated utilizing maximum likelihood estimation and Bayesian models with Gibbs sampling to ensure robustness and methodological rigor. Additionally, a nonhomogeneous model based on the Weibull distribution is introduced to account for time-varying transition rates, providing a more dynamic perspective on disease progression. While the analysis is conducted with simulated data, the framework is adaptable to real-world datasets, offering valuable insights for refining screening policies and optimizing inter-screening intervals. [ABSTRACT FROM AUTHOR]

Temporal processing can be divided into explicit timing and implicit timing. Explicit timing tasks require participants to attend to the temporal aspects of the task, whereas in implicit timing tasks, temporal information affects performance without explicit instruction to process time. Compared to younger adults, older adults have been shown to exhibit greater variability in explicit timing, while in implicit timing, they have been shown to rely more on temporal predictions formed by the hazard function. However, the relationship between explicit and implicit timing and its age-related changes have yet to be explored. To address this issue, we collected data in which younger and older adults performed a time bisection task (i.e., explicit timing task) and a foreperiod task (i.e., implicit timing task) in a within-subjects design. Based on a Bayesian optimization framework, we hypothesized that individuals with higher variability in explicit timing would show a stronger foreperiod effect, which is an index of the degree of reliance on temporal predictions. Results showed a different relation between explicit and implicit timing in younger and older adults. In older adults, results were consistent with the hypothesis that increased variability in explicit timing was associated with a stronger foreperiod effect. In contrast, bias in the temporal representation but not variability was associated with the foreperiod effect in younger adults. Implications for the age-related difference in the relation between explicit and implicit timing are discussed. [ABSTRACT FROM AUTHOR]

Background: Over the last few years, many efforts have been made to leverage historical information in clinical trials. Incorporating historical data into current trials allows for a more efficient design, smaller studies, or shorter duration and may potentially increase the relative amount of information on efficacy and safety. Despite these advantages, it is crucial to select external data sources appropriately to avoid introducing potential bias into the new study. This is where borrowing methods become useful. We illustrate and compare the latest methods of borrowing historical data in a single-arm phase II clinical trial setting, examining their impact on statistical power and type I error. Methods: We implemented static and dynamic versions of the power prior method, incorporating overlapping coefficient and loss functions and meta-analytic predictive priors. These methods were compared with standard and pooling approaches, in which none or all historical data are used. Results: Dynamic borrowing methods achieve lower type I error inflation than pooling. The power prior approach, integrated with overlapping coefficient, allowed for measuring the similarity of the subjects considering their baseline characteristics, thus the likelihood of the data contains information about both confounders and outcome. Using a discounting function to estimate the power parameter guarantees the similarity of historical information and current trial data. Conclusion: We provided a comprehensive overview of borrowing methods, encompassing frequentist and Bayesian approaches as well as static and dynamic technique, to guide researchers in selecting the most appropriate strategy. [ABSTRACT FROM AUTHOR]

In this article, we consider experimental designs for gauge R & R studies used to assess measurement systems. We present standard full factorial designs with long forgotten block designs and compare them via a simulation study for various run sizes. The simulation study shows that the block designs which accommodate more parts and especially more operators provide better inference even though their run sizes are the same as the full factorial designs. [ABSTRACT FROM AUTHOR]

In linear regression, the coefficients are simple to estimate using the least squares method with a known design matrix for the observed measurements. However, real-world applications may encounter complications such as an unknown design matrix and complex-valued parameters. The design matrix can be estimated from prior information but can potentially cause an inverse problem when multiplying by the transpose as it is generally ill-conditioned. This can be combat by adding regularizers to the model but does not always mitigate the issues. Here, we propose our Bayesian approach to a complex-valued latent variable linear model with an application to functional magnetic resonance imaging (fMRI) image reconstruction. The complex-valued linear model and our Bayesian model are evaluated through extensive simulations and applied to experimental fMRI data. [ABSTRACT FROM AUTHOR]

We propose a Bayesian, noisy-input, spatial–temporal generalized additive model to examine regional relative sea-level (RSL) changes over time. The model provides probabilistic estimates of component drivers of regional RSL change via the combination of a univariate spline capturing a common regional signal over time, random slopes and intercepts capturing site-specific (local), long-term linear trends and a spatial–temporal spline capturing residual, non-linear, local variations. Proxy and instrumental records of RSL and corresponding measurement errors inform the model and a noisy-input method accounts for proxy temporal uncertainties. Results highlight the decomposition of regional RSL changes over 3,000 years along North America's Atlantic coast. The physical process glacial isostatic adjustment prevailed before 1800 CE, with anthropogenic forcing dominating after 1900 CE. [ABSTRACT FROM AUTHOR]