Papers

The article identifies the desired properties of the reliability risk metric for the Australian Energy Sector. It proposes a comprehensive set of metrics designed to measure various aspects of the reliability risk when a large portion of power generation is composed of variable renewable energy (VRE). The suggested methods aim to balance the tradeoff between economic benefit and risk management, effectively address the tail risk, and interpret the severity of the outage in a direct way. Properties of the considered reliability metrics are investigated by using simulation studies that use time series of expected unserved energy values. Sample distributions, dependency structures and other statistical properties of the metrics are studied. The results suggest new tools and approaches that can be used to increase the efficiency of the Australian Energy Sector.

Hospice quality should be interpreted in relation to both provider organization and the local conditions under which care is delivered. This study develops a provider-county performance assessment framework by linking national Centers for Medicare & Medicaid Services (CMS) hospice data and Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey outcomes with county measures of rurality, social vulnerability, health burden, and workforce and health-resource context. The adjusted analysis includes 2,928 providers in 1,078 counties and combines geographic mapping, blockwise regression, six secondary CAHPS outcomes, and eight sensitivity analyses. Adding county context increased adjusted R-squared from 0.077 to 0.202. After full adjustment, for-profit hospices had overall caregiver ratings 3.832 percentage points lower than nonprofit hospices. This negative association appeared across all six secondary CAHPS domains and remained significant in every sensitivity specification. Higher county social vulnerability was also associated with poorer caregiver experience, although its magnitude depended partly on the specification of community health burden. These findings show that county context materially improves hospice performance assessment but does not eliminate the ownership difference. The framework supports context-aware monitoring, peer comparison, and targeted quality improvement.

Accurate prediction of post-fire snow conditions does not necessarily establish how wildfire changed those conditions. We developed an explainable geospatial artificial intelligence framework combining multisource Earth observations, meteorological information, and matched before-after comparisons to examine seasonal snow persistence following the 2020 Creek Fire in California's Sierra Nevada. Harmonized Landsat Sentinel-2 observations were used to estimate the fraction of clear-sky observations containing snow during October-July for eleven water years, 2016-2026, on a 500 m analysis grid. We matched 3,778 snow-zone cells inside the fire perimeter to comparable unburned controls using terrain and pre-fire snow conditions; absolute standardized mean differences after matching were no greater than 0.047. HLS persistence agreed closely with MODIS, with a mean annual spatial correlation of 0.947. The landscape-average before-after control-impact contrast was +0.0017, with a year-level 95% confidence interval of -0.023 to +0.027. A stronger response emerged in the highest burn-severity class, where observed persistence increased by 0.026 relative to matched controls, equivalent to 2.6 percentage points. Under 5 km spatial cross-validation, XGBoost predicted raw post-fire persistence with an out-of-fold coefficient of determination of 0.811, whereas predictive skill for the fire-adjusted anomaly reached 0.046. Elevation and temperature together accounted for 65.7% of mean absolute model attribution. These findings distinguish a severity-associated optical snow response from the terrain-climate relationships that dominate predictive skill. Combining matched comparisons with explainable GeoAI provides a practical framework for forest monitoring that separates accurate environmental mapping from inference about disturbance effects.

We propose a Bayesian regression tree model fit within a multilevel structure and apply it to historic wildfire data in the western United States. Sharing of information between related groups (ecoregions) combined with highly interpretable regression trees allows for better predictions and understanding of climate and land cover variables predictive of wildfires. By doing a simulation study with a range of performance metrics, we demonstrate our method produces tree posteriors most structurally similar to assumed true trees, while simultaneously achieving good out-of-sample predictive performance. Applied to a large wildfire data set, we explore variable splits within regression trees corresponding to each ecoregion in detail, taking into account known features of each location. Shared hyperparameters between trees provide highly useful understanding of both variable and split value importance in predicting wildfires among all ecoregions, with no direct parallel in comparable models. Namely, we highlight potential evaporation, temperature, and evergreen forest land cover as variables most associated with historic wildfires, with some observable patterns in split values most commonly chosen across the groups. We propose a new algorithm based on parallel tempering, conditioning on shared hyperparameters at the true posterior temperature, improving Markov chain mixing, a known bottleneck in Bayesian CART models.

Crash counts on urban road networks exhibit spatial dependence driven by road connectivity rather than Euclidean proximity alone. We propose a Bayesian negative binomial model that represents spatial variation through a parsimonious combination of spectral components defined directly on road segments. The model combines the normalized random edge neighborhood Gaussian (RENeGe) operator with a continuous spike-and-slab prior, with mixture indicators marginalized out. Eigenvalue-dependent slab variances link spectral regularization to network-supported smoothness, while posterior slab-membership probabilities quantify support for individual components and uncertainty in effective spectral complexity. Segment length is used as an exposure offset, so fitted rates describe crash frequency per unit road length rather than traffic-adjusted risk. In a simulation study with 1,200 datasets and 4,800 Bayesian fits, the proposed model achieved the lowest mean spatial-field recovery error and highest mean test log predictive density under sparse RENeGe truth. Under alternative generating mechanisms, its predictive performance remained close to dense spectral models and exceeded that of a nonspatial model. Applications to Barcelona and central Bogota showed distinct posterior spectral patterns. Exploratory leave-one-segment-out comparisons found similar predictive accuracy for the proposed model and a low-rank edge CAR model, both outperforming a nonspatial negative binomial model, while a spectral BYM2 model achieved the highest exploratory predictive score. These results support probabilistic summaries of spectral complexity without a detectable predictive penalty relative to the corresponding CAR model.

Social media increasingly shapes everyday life, serving as both a central venue for discussion of major events and a space where online collective behavior can spill over into real-world activity, while artificial intelligence (AI) is likewise becoming increasingly influential across society. Understanding how different online communities respond to AI-related events, and disentangling the mechanisms underlying the development of such engagement, are therefore increasingly important for studying information spreading and the societal reception of AI. Our work addresses the limited connection between empirical studies of AI-related online engagement and mathematical modeling of group-level spreading dynamics. We develop a framework to collect and organize empirical Bluesky activity into distinct user groups, then use a network-based dynamics model to investigate the mechanisms underlying their engagement. We use the release of DeepSeek R1 as a case study under two complementary grouping schemes: AI-related communities and academic disciplines. For each group, we fit a network-based Susceptible-Infected-Recovered (SIR)-type model augmented with an exogenous engagement term, allowing us to quantify the relative strengths of endogenous network-driven spreading and direct external response. Across groups, we find that strong direct responses to the event do not necessarily coincide with strong network-driven propagation, revealing distinct engagement patterns that may be obscured by aggregate activity alone.

Response-adaptive randomization (RAR) modifies treatment allocation probabilities during a clinical trial as response/outcome data accumulate, with the aim of improving patient benefit, statistical efficiency, or both. Despite substantial methodological development, adoption of RAR in clinical practice has remained limited, and the software available to support its design and implementation has not previously been reviewed. We identified 16 publicly available, open-source software packages implementing RAR methods, spanning urn-based, target-allocation, Bayesian, Markov decision process (MDP)-based, and dose-finding approaches, and evaluated them with respect to their methodological and practical characteristics. We found that while several software packages exist, most are method-specific, and only a small number provide broader, general-purpose adaptive-trial design and analysis capabilities. Explicit support for practically relevant features, including delayed and missing outcome data, temporal trends in response rates, flexible operating-characteristic evaluation, and platform or multi-arm multi-stage trial designs, is rare or absent across the identified software. These findings suggest that while a diverse set of tools exists for exploring RAR designs, gaps remain between the methodological literature and the software available to implement it in practice. Continued development of flexible, practically oriented, and validated software is important for the wider adoption of RAR in clinical research, and we highlight interesting areas of further work.

Objectives: There has been increased appreciation of the differences between marginal and conditional estimates and different types of effect measures regarding their applicability to different target populations. This issue of transportability is of concern in model-based cost-effectiveness analysis (CEA) when treatment effects from (international) trials are applied to (country-specific) baseline risk estimates. The objective of this paper is to create awareness regarding the issues that arise when using different types of treatment effect and baseline risk estimates in a model-based CEA to inform health technology assessment (HTA). Methods: We clarify collapsibility, marginal versus conditional estimation, and transportability; derive the ideal modeling approach implied by a marginal cost-effectiveness estimand; and characterize the issues of common modeling approaches, illustrated with a fictitious state-transition model. Results: An individual-level simulation that predicts outcomes from conditional inputs and averages them over the target population targets the marginal cost-effectiveness estimand. Cohort-model approaches that marginalize inputs early, use conditional inputs at mean covariate values, or combine a conditional effect with a marginal baseline (or vice versa) can misstate cost-effectiveness results even with correct-population inputs; inputs from the wrong population add further error. Conclusions: The most rigorous approach is an individual-level simulation that carries conditional inputs (baseline risk, treatment effect, prognostic effects and effect modifiers) and marginalizes late. Cohort approaches instead marginalize early, relying on aggregated inputs, and do not necessarily target the marginal cost-effectiveness estimand of interest for HTA. Model developers should document, for each input, whether it is marginal or conditional and its population.

Robustness evaluation must examine diverse visual perturbations, while benchmarks cover only some real-world conditions and physical testing is costly. Paired evaluations link clean and perturbed predictions for the same image, capturing changes in correctness, confidence, and acceptance beyond aggregate accuracy. We investigate how this image correspondence supports two needs in robustness evaluation: interpreting paired evaluation results and prioritizing samples for physical testing. To interpret paired evaluation results, we fix both sets of prediction records and vary their correspondence within each class. We prove that classwise correct-correct counts give the same sharp bounds on lost acceptance and mean true-class probability decrease among retained-correct inputs as any feasible five-state refinement. Distinguishing persistent from changed wrong answers can further constrain accepted-error transitions, while shared correspondence can establish policy orderings left unresolved by separate cost intervals. To prioritize samples for physical testing, we retain each image's synthetic responses and rank clean-correct images by their mean true-class probability under corruption. Across 44 classifiers, testing the highest-risk 20% finds 67% and 45% of failures under mild screen and print recaptures, versus 58% and 36% for clean confidence and 60% and 37% for an equal-size natural-transformation average. With both probability averaging and an A3Rank scoring adaptation, the tested corruption set yields higher mean failure recall than the natural-transform set; differences between scores depend on the source and budget. Together, these findings show that the value of correspondence depends on the evaluation objective: classwise counts suffice for specified reliability bounds, while image-specific synthetic responses improve the allocation of physical tests within the evaluated pool.

Refugee integration is a central policy challenge for host countries, and where governments initially place refugees shapes their integration trajectories. Yet placement officers often have limited information about where each case is most likely to succeed. Algorithmic refugee matching uses administrative data, machine learning, and constrained optimization to recommend employment-optimized placements in real time as cases arrive, with human placement officers retaining final authority. Between January 2020 and June 2023, the Swiss State Secretariat for Migration randomly assigned about 2,000 refugee cases to receive a canton recommendation either algorithmically optimized for employment or drawn to approximate existing procedures, with placement officers and refugees blinded to assignment. The two arms used identical but separate canton and origin-group quotas, so gains reflect better refugee-canton matching rather than reallocation toward stronger labor markets. The trial began just before the COVID-19 pandemic shifted labor-market conditions. For the pre-registered primary outcome – the share of months employed during the first three years – the pooled intention-to-treat (ITT) estimate across the 2020-2023 placement cohorts was +2.2 percentage points (about 10% of the 22.3% control mean; 95% CI [+0.05, +4.33]), rising to +3.9 pp (about 17%; [+1.11, +6.68]) for the post-COVID 2022-2023 cohorts. Effects grew over time: at 36 months, the pooled ITT on the employment rate was +5.2 pp (about 11%; 95% CI [+1.10, +9.25]) – comparable to the gains from hundreds of hours of intensive language training. Overall, the results provide rare field evidence that AI-based decision support can improve high-stakes public-sector allocation, offering a scalable, low-cost way to raise refugee employment.

Power laws are often claimed for Bitcoin quantities from the slope of a log-log plot. We test one such claim with a formal protocol: whether the upper tail of the values of live unspent transaction outputs (UTXOs) follows a power law. We reconstruct the exact UTXO set at eight block heights from 2012 to 2025, validate it against an independent Bitcoin Core snapshot, and apply the Clauset-Shalizi-Newman protocol with three adaptations the data require: exact discrete support, several near-tied cutoffs retained and reselected in every bootstrap replicate, and resampling by transaction, because the outputs of one transaction are not independent. The protocol was fixed before inference and every later change is recorded. The pure power law is rejected at every height, with observed Kolmogorov-Smirnov distances five to forty times those the fitted law produces, and where the exponent is below 2 its extrapolation predicts, in expectation, several outputs each larger than the total coin supply. A lognormal, a stretched exponential and a power law with an exponential cutoff near a thousand bitcoin fit better in sample at every height, as families that contain the power law must, by margins that exceed those seen on simulated power-law tails at six of the eight heights, and they predict held-out transactions better at nearly every height. They rank differently from height to height, and the lognormal and the cutoff power law differ clearly at only two. The tail's shape and the share of supply it holds drifted slowly while the number of outputs grew seventy-fold. The rejection survives a null that reproduces the dependence measured within transactions, which halves the one exceptional effect size in the panel and changes no verdict. Bitcoin UTXO values are heavy-tailed but not power-law distributed.

AI reliability concerns whether an AI system performs its intended function dependably over a stated period and under stated operating conditions, with stated evidence. As these systems become more autonomous, that function includes more than a correct output. Retrieval, memory, tool use, permissions, human oversight, and interactions among systems must operate consistently and safely, and, for generative systems, so must the reasoning process that produces the output. Average benchmark accuracy measures capability; it does not quantify this broader reliability claim. This paper adapts established reliability engineering methods, from failure definitions and operational envelopes to FMEA, accelerated testing, field monitoring, and reliability growth, to AI systems. A four-level diagnostic framework classifies failures as component, operational-loop, agentic-conduct, or network and governance failures. Test, evaluation, verification, and validation (TEVV), sequential monitoring, and FRACAS create and refresh evidence. SMART provides statistical guidance for measurement, analysis, assessment, and test planning; the NIST AI Risk Management Framework provides organizational guidance for governance, evaluation, monitoring, and mitigation. Three cases illustrate the program: adversarial testing of a convolutional neural network, perception-error propagation, and autonomous-vehicle disengagements. Established reliability engineering provides a usable foundation; new measurements and safety guardrails are still needed as these systems are self-evolving.

Price stability remains a pillar in monetary policy practices and carries a special importance within monetary unions. Mainstream economics tried to leverage price stability using price indices and several metrics to shed light on specific dynamics and optimal macroeconomic levels. The wide availability of data led researchers to consider the study of systems using Random Matrix Theory, based on inner correlation patterns. This aims to enhance the multivariate analysis by removing noisy patterns from the signal and improve data quality for further inferences. This work considers the collection of monthly inflation indices in the Eurozone as a system of prices to analyze its eigenvalues' statistical and asymptotic properties and uncover inner country-level insights. Results confirm the system cannot assumed to be randomly generated, and the data exhibit noise-dominated patterns, due to small and persistent variations at the country-level. The latter make the inter-country correlations more dynamic and the separation of the signal from the noise quiet difficult. Findings identified two countries as distorting inflation dynamics besides three other distinct, regional-based groups of countries. Variability sources might stem from economic episodes fueling inflation spikes in some countries, as well as methodological aspects used to ensure data quality and representativeness in the European Union. Despite being complex, the system demonstrates a certain stability, in terms of self-organization; while large monthly fluctuations cannot be considered as rare events, but part of the data-generating process.

Circulatory shock is one of the leading causes of mortality in intensive care units (ICUs), and its early detection is critical to enable timely treatment and improve clinical outcomes. This study aimed to develop and evaluate a two-stage cascade machine learning framework for the early detection and etiological classification of circulatory shock in critically ill patients. Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, four patient groups were defined: septic shock, cardiogenic shock, hypovolemic shock, and a non?shock control group, comprising a total of 32,907 patients. Vital signs and laboratory data were collected during the first six hours after ICU admission. After data cleaning and missing-value imputation, the mean value of each variable was used for model development. Several machine learning algorithms were compared, including logistic regression, Random Forest, XGBoost, and multilayer perceptron (MLP) networks. Random Forest and XGBoost achieved the highest overall performance, with an AUROC of approximately 0.82-0.83 for shock detection, and a macro-averaged sensitivity of approximately 0.61 and precision of approximately 0.58 across all four classes. Classification performance was highest for the non?shock group, followed by septic and cardiogenic shock, while hypovolemic shock showed the lowest performance. These results indicate that machine learning models can identify early signs of hemodynamic deterioration associated with circulatory shock and may support clinical decision-making in the ICU. However, further improvements are needed for the classification of specific shock subtypes, particularly hypovolemic and cardiogenic shock, as well as for real-time clinical implementation.

We propose a Bayesian nonparametric factor model that infers the number of factors, induces row-wise sparsity, and merges redundant dictionary elements via exact clustering. A Dirichlet process prior is placed on the columns of an overcomplete loading matrix and fully marginalized to an exact Pólya urn, avoiding stick-breaking and auxiliary variables. A spike-and-slab base measure allows entire factors to be exactly zero. The model uniquely combines exact zeros, exchangeability over columns, and exact merging within a single marginalized Dirichlet process, unlike CUSP, MGP, or the beta process. An exact Gibbs sampler with canonical relabeling and parallel C/MPI implementation is developed. We prove posterior contraction at rate $\sqrt{M s_0 \log n / n}$ for the covariance matrix, and in the fixed-dictionary setting obtain the minimax optimal rate $\sqrt{s_0 \log n / n}$ plus underfitting consistency; the overfitting direction is an open conjecture. The spike-and-slab is essential: without it the effective dimension scales as $pM$, yielding a slower rate. Simulations show the method is the only fully adaptive approach to recover the true rank, achieving the smallest covariance, loading, and signal-reconstruction errors, beating an oracle baseline. On van 't Veer breast cancer data ($n=97$, $p=1213$), the posterior concentrates on eight interpretable programmes; seven pass coherence and two pass Bonferroni-corrected Hallmark enrichment.

Linear mixed-effect panel data models are widely used in longitudinal biomedical, environmental, and social studies, but are often sensitive to data contamination and numerous covariates. To address these challenges, we propose a robust variable selection approach based on a least squares approximation (LSA) of the density power divergence (DPD) objective function combined with the Adaptive LASSO penalty. The LSA converts the nonlinear DPD objective into a computationally efficient quadratic approximation while preserving the robustness of DPD estimation. Under suitable regularity conditions, the proposed DPD Adaptive LASSO-LSA estimator is shown to possess oracle properties, including selection consistency and asymptotic normality. Simulation studies demonstrate improved robustness, better sparsity recovery, and significantly enhanced computational efficiency than penalized likelihood methods, while maintaining performance comparable to existing DPD-based approaches. An application to the SWAN bone mineral density dataset illustrates the practical relevance of the proposed methodology for robust estimation and reliable identification of important covariates.

We test whether a multivariate vector X conforms to a specified distribution F, a problem in copula modelling and density forecasting. The Rosenblatt transform reduces it to a test of uniformity, but depends on an arbitrary coordinate ordering that strongly affects power under dependence. We study order randomization: applying the transform under many random orderings and merging the evidence with dependence-robust rules. Reordering conserves the total Mahalanobis signal energy and merely redistributes it, so one ordering is a lucky or unlucky draw. In simulations we observe significant gains in calibrated power over both the expected single random ordering and order-invariant references. Two ingredients are essential: a two-sided base statistic, and a re-estimating parametric bootstrap that restores level under an estimated null and unlocks the gain. The calibrated pooled tests are robust to the departure's shape; no order-invariant reference we compare is: the symmetric-root test collapses on diffuse departures, while the shape-flat chi-squared test trails on concentrated ones. We apply it to a Gaussian foreign-exchange risk model over a decade of daily data on nine currencies, where it detects episodes such as Brexit and COVID. Though we focus on Gaussian nulls, the procedure extends to any null whose conditional distributions can be computed and simulated from.

This paper conducts an empirical mediation analysis to examine how U.S. stock-market sectors responded to the 2025 tariff-policy episode. Using S&P 500 stock data along with hundreds of firm-level financial variables, we study whether sector membership was associated with differences in stock returns and whether these differences were statistically related to firm financial characteristics. For each of five economically distinct tariff-policy windows, we use high-dimensional linear causal mediation models to estimate the direct sector effects. We use a linear regression model to estimate the total sector effects, and then estimate the indrect sector effects by using the difference between the total sector effects and the direct sector effects. To accommodate a large number of candidate mediators, we use a partially penalized least-squares procedure that regularizes financial-variable coefficients while unpenalizes coefficients of all sector indicators. Wald and F-type tests are used to examine whether indirect and direct effects are significant or not, respectively. The results show that tariff news did not produce one uniform market response. Sector effects vary across five economically distinct tariff-policy windows. The clearest findings are in the direct sector effects. Technology was the sector most negatively affected during the initial decline and escalation collapse, but it also rebounded most strongly during the April 9 relief rally. The results indicate that in addition to Technology, Energy and other trade-exposed sectors were particularly affected during the initial decline and escalation phases. The selected mediators provide evidence that growth, investment, liquidity, profitability, and cash-flow characteristics helped characterize firms associated with sector-level return differences.

The SLGP package provides nonparametric estimation of fields of probability densities indexed by covariates, from unevenly sized samples with few or no replicates. A Spatial Logistic Gaussian Process applies a logistic density transformation to a finite-rank Gaussian process over a joint index-response domain, yielding a prior over conditional densities that adapts in location, shape, and modality. The contribution of this paper is the implementation: a finite-rank Random-Fourier-Feature representation that keeps the transformed process numerically tame, a log-concave likelihood that makes the fit a convex problem, and a family of quadrature shortcuts for the normalising integrals that turn a per-observation cost into a per-grid cost. Three estimation modes are available: MAP, Laplace, and full MCMC. We demonstrate the package throughout on the classical Fiji-Tonga earthquake catalogue, estimating how the distribution of hypocentre depths deforms across the region, and on a discrete-response variant.

NBA athlete performance tends to increase through early career as athletes develop and acclimate to the league, followed by decline due to age-related deterioration in athleticism. While this general pattern persists, the precise shape of this trajectory varies by athlete and across different measures of performance. To model performance increase and decline, we introduce the concave process prior, a novel nonparametric prior over concave functions. We then use a latent variable model to characterize dependence in aging profiles across player-metrics, embedding each player in a shared low-dimensional latent space so that players with similar profiles learn similar trajectory shapes, peak ages, and peak values. Posterior analysis of the learned embedding supports latent-space nearest-neighbor retrieval of career-comparable players and informed projections of young players. We apply our model to data across over a dozen performance metrics for over two thousand players in seasons ranging from 1997 to 2026. Our results show that jointly modeling all metrics improves held-out predictive performance over single-metric alternatives, and that the concavity constraint itself improves prediction. We find that athleticism-driven metrics such as blocks and offensive rebounds peak in a player's early twenties, while skill-based shooting metrics peak in the mid-twenties or later.

Groups differing in social and economic circumstances often differ markedly in depressive symptom levels, yet their estimated symptom networks show few clear differences. Such null results are often read as evidence that the groups' symptom systems are the same, but they only show that no difference was detected in how symptoms interact. How readily symptoms become present, and how this depends on the contextual conditions under which they are observed, is rarely compared. Building on a slow-fast perspective, we treat depressive symptoms as a relatively fast-changing system embedded in more persistent social, economic, psychosocial, health, and lifestyle conditions that shape symptom activation. Using a cohort of 23,689 adults from Amsterdam, we illustrate this with PHQ-9 depressive symptoms and a composite index of persistent socioeconomic, psychosocial, and health-related conditions, which we call Slow Risk Load (SRL). Participants with high SRL had substantially higher symptom levels than those with low SRL. A standard Network Comparison Test detected no overall difference in network structure, although global strength was modestly higher in the high-SRL group. Ising models, which separate how symptoms co-occur from how readily each becomes present, indicate higher activation under high SRL for eight of nine symptoms, and model comparison consistently favored group-specific activation over group-specific interactions. The slow-fast perspective reframes group network comparisons as a multiple-timescale problem in which network structure, symptom activation, and context should be considered jointly. It places symptom-focused and context-focused interventions at different layers of the same coupled system rather than in competition.

Historically, statistical analysis of offensive lineman has been hindered by the lack of easily measurable quantities. More recently, with the introduction of player tracking data new methodological advances are now possible. Using high-dimensional spatio-temporal data, we adapt the defensive-matchup hidden Markov model of \cite{franks2015characterizing} from basketball to football pass protection, producing frame-by-frame probabilistic assignments of each pass blocker to the rushers. We show how this probabilistic assignment is a usable modeling artifact that augments existing player-evaluation frameworks. We directly quantify the attention a rusher commands, upgrade adjusted plus-minus \citep{Macdonald+2012} from all-or-nothing stints to partial, continuous blocking credit in continuous time, yield block-shedding survival metrics, and measure the space a rusher generates for his teammates. Fit to the first eight weeks of the 2021 NFL season, the resulting metrics recover widely-recognized elite rushers and pass protectors and align with independent charting.

Critical slowing down (CSD), the slowing of recovery from perturbations as a system approaches a critical transition, is widely used for early warning, but whether such signals appear in environmental transitions subject to rapidly changing external forcing has rarely been tested with observations. Here we show, using 2,427 icing-onset events in 1-min observations from 121 South Korean road weather stations, that such signals do not consistently precede road icing. Icing began from different prior surface states, but conditions at onset varied continuously with weak separation among pathways. Cold dose, deposition dose and freeze-thaw accumulation at onset exceeded those at non-icing control times, with cold-dose discrimination highest at decay timescales of 30 min or less. However, the increases in variance and lag-1 autocorrelation expected under CSD did not distinguish icing from control times; the same procedure recovered these increases in synthetic series with a declining recovery rate, and the null result was replicated for 3,091 events at 127 independent stations. Predictive information was nevertheless present: adding recent changes and cumulative antecedent conditions to current conditions improved discrimination, and surrounding meteorological fields increased the area under the ROC curve by 0.030-0.032 for forecast windows of 1 to 12 h and reduced false alarms by 62-67% at 3 and 6 h. These results suggest that road icing is more consistent with recent meteorological forcing moving the road surface into icing-favourable conditions than with a progressive decline in internal stability, and they indicate where early warning should look when a transition is externally forced.

Background. Magnetic resonance imaging (MRI) is central to Alzheimer's disease research and clinical evaluation, yet timing of first MRI after cohort entry varies substantially. In aging cohorts, many participants die before imaging, and treating death as noninformative censoring can distort inference on neuroimaging access and conceal disparities. Objective. To quantify individual-level determinants and center-level heterogeneity in timing of first neuroimaging while accounting for the competing risk of death. Methods. We analyzed National Alzheimer's Coordinating Center Uniform Data Set v3 linked to MRI records from 2015 through March 2025. The study included 20,867 participants aged 50-95 years without dementia at baseline and with known APOE {\epsilon}4 status. Time from baseline to first post-baseline MRI was the primary event, with death before MRI treated as a competing event. We fit Bayesian multilevel cause-specific accelerated failure time models with Alzheimer's Disease Center random intercepts, using Weibull and log-logistic models for MRI timing and death, respectively. Frequentist accelerated failure time and Cox models were used for sensitivity analyses. Results. APOE {\epsilon}4 was not credibly associated with timing of first MRI. Older age, lower educational attainment, and non-White race were associated with delayed MRI acquisition. Center-level variation in MRI timing was substantial. Conversely, APOE {\epsilon}4 homozygosity was associated with earlier death prior to MRI, alongside strong effects of age and cognitive impairment. Conclusions. Timing of first MRI in Alzheimer's research cohorts is driven primarily by demographic, social, and center-level factors rather than genetic risk. Accounting for competing mortality and site heterogeneity is necessary to avoid biased neuroimaging samples and support equitable Alzheimer's research.

Structural health monitoring of deteriorating structures requires the latent deterioration process to be inferred indirectly from measured structural responses and propagated to quantities of engineering interest. This paper presents a probabilistic framework that formalizes this process by distinguishing Bayesian inference of uncertain deterioration and model parameters from diagnosis of the current deterioration state, prognosis of its future evolution, and prediction of structural reliability. The deterioration model thereby provides the common probabilistic link between monitoring-based inference and these downstream tasks. To enable sequential inference as monitoring information accumulates, Bayesian Updating with Structural Reliability Methods (BUS) is extended through a nested formulation of the BUS events associated with successively growing monitoring datasets. The resulting Sequential BUS method allows Subset Simulation to continue from the augmented conditional sample population obtained at the preceding monitoring stage rather than restarting from the prior distribution. The posterior population can then be propagated to monitoring-informed reliability analysis. The framework is demonstrated for fatigue deterioration of a redundant jacket-type structure, where modal properties obtained from simulated vibration monitoring are used to infer a high-dimensional deterioration model of welded connections. The results show how successive monitoring observations constrain plausible deterioration histories, provide probabilistic system-level diagnosis, and update crack-growth prognosis and structural reliability while retaining ambiguity between deterioration scenarios that produce similar structural responses. The proposed framework thus provides a coherent probabilistic route from response-based monitoring data to deterioration diagnosis, prognosis, and reliability prediction.

The wide-ranging benefits of physical activity and sports participation among children and adolescents have been closely examined and are reported to include improvements in physical and mental health, cognitive functioning, and social connectedness. However, it remains largely unknown how these activities may affect flourishing and academic engagement, which are closely tied to long-term success, health, and well-being, and how these patterns evolved before, during, and after the COVID-19 pandemic. In this article, we provide the protocol for an observational study using data from the National Survey of Children's Health to examine these relationships among American adolescents. To strengthen our findings, we will conduct this investigation across three different time periods, allowing us to assess the replicability of our conclusions. We introduce a novel statistical design, called data turnover, to carry out this analysis. Data turnover allows a single group of statisticians and domain experts to work together to assess the strength of evidence gathered across multiple data splits while incorporating both qualitative and quantitative findings from data exploration. We delineate our analysis plan using this new method and conclude with a brief discussion of additional considerations for our study.

Nonlinear dynamics arise whenever multifarious entities of a system cooperate, compete, or interfere. For example, cardiovascular system involves a great level of complexity. Multi-lead ECG signals are generated through orchestrated depolarization and repolarization of cells and manifest significant nonlinear dynamics. Nonlinear dynamical systems defy understanding based on the traditional reductionist's approach, in which one attempts to understand a system's behavior by combining all constituent parts that have been analyzed separately. In order to cope with system complexity, modern healthcare systems are investing in advanced physiological sensing and patient monitoring, thereby giving rise to big data. Realizing the full potential of big data for healthcare intelligence requires fundamentally new methodologies to harness and exploit complexity. However, available nonlinear dynamics techniques are either not concerned with healthcare analytical objectives or fail to effectively analyze big data to extract useful information for improving healthcare services. There is an urgent need to develop analytical methodologies that fully exploit the underlying nonlinear dynamics in physiological systems for advancing healthcare services with exceptional features such as personalization, responsiveness, and superior quality. This chapter presents some theoretical developments and tools to advance the applications of nonlinear dynamics principles in health care. Specifically, we focus on sensor-based characterization and modeling of nonlinear dynamics (i.e., multifractal analysis and multiscale recurrence quantification). Then, current developments and applications of these methodologies are examined for characterizing and exploiting heart rate variability and space-time ECG signals.

The cryptocurrency market's volatility and complex price dynamics challenge portfolio diversification and risk management. This study examines price similarity across 347 cryptocurrencies from March 2020 to April 2024 using daily OHLCV data from the Binance API. Daily dynamics are summarized via a signed, capped intraday price-range metric, and pair similarity is assessed using the Pearson cross-correlation coefficient, capturing linear co-movement at a given lag, and Dynamic Time Warping (DTW) distance, capturing shape similarity independent of lag. Data are segmented into three market phases: an uptrend, a decline, and a subsequent uptrend. For each period, the 10 pairs with highest cross-correlation and lowest DTW distance are identified. BNB-CAKE, XLM-XRP, and MANA-SAND show the strongest cross correlations across the three periods, with coefficients of 0.78 or higher and maximum correlations at lag 0, indicating synchronized movements. Three to four of the ten lowest-DTW pairs coincide with the cross-correlation top 10 per period, showing the measures are related but not interchangeable. Two same-platform fan tokens rank among the most DTW-similar pairs despite weak cross-correlation, while a euro pegged asset and gold-backed token show the lowest DTW distance, driven by low volatility rather than shared dynamics. Results demonstrate substantial, measure dependent interconnectedness with implications for diversification strategies.

Predicting cellular responses to genetic perturbations helps prioritize experiments in single-cell genomics, where exhaustive measurement is infeasible. While computational models increasingly predict these responses, three evaluation deficiencies obscure what their scores demonstrate. First, absolute metrics cannot separate target-specific predictions from a shared background response. Second, common metrics remain high under gene shuffling, so gene-level accuracy is never verified. Third, a score at one training size says nothing about coverage, which depends on representation-space proximity and response-constraining power. We propose AmbiModBench, a specificity-aware, gene-resolved and coverage-aware benchmark. It pairs every score with a training-mean reference fitted on the same split, screens each readout by gene-coordinate permutation, and links embedding distance to response variation. Across K562, RPE1 and Norman, strong absolute scores largely reflect shared background rather than target-specific learning. Widely used readouts track response magnitude distributions rather than the affected genes. Detectable gain follows representation-space coverage rather than training-set size. Nonetheless, on RPE1 the protocol yields a reproducible target-specific gain across five additional splits and three gene selections, which absolute scores alone cannot distinguish from shared background.

Climate change may materially affect long-term life insurance liabilities by altering both the level and seasonal pattern of mortality. This article develops a multi-population mortality framework that combines a Hermite spline model with a distributed lag non-linear model to capture age-, region-, and temperature-specific mortality effects. We apply the framework to mortality and temperature data from 15 Spanish NUTS-2 regions and project future mortality under three shared socioeconomic pathway (SSP) scenarios. We then assess the implications for a hypothetical whole life insurance portfolio through expected death-benefit payments and portfolio profit and loss. The results reveal an important seasonal offset: warmer conditions reduce expected payoffs during winter periods but increase them during summer periods, with these effects becoming more pronounced under more severe climate scenarios and for policies issued in later years. Over longer horizons, adverse summer mortality effects become increasingly important. The portfolio analysis further shows that climate-related mortality risk can materially increase the dispersion and downside risk of portfolio outcomes, particularly under SSP5-8.5. These findings highlight the importance of incorporating temperature-related mortality effects into long-term life insurance liability projections and risk assessment.