Probability Estimates Research Articles

Risk-based maintenance and inspection of riverine flood defence systems

The condition of flood defence revetments is influenced by many different degradation processes such as animal burrowing, rutting and growth of weeds. Many of these processes are shock-based rather than progressive continuous. As shocks can cause a drop in performance, this means that the condition of a revetment can suddenly decrease, meaning that revetments can have significant initial damage at the beginning of a storm. Combined with the limited detection probability of common visual inspections of flood defences, this can have a significant influence on the reliability of flood defence systems, something typically not considered in reliability analysis. In this paper we study the reliability of a flood defence system subject to shock-based degradation. Various maintenance concepts are compared for a case study of a riverine flood defence of 20 kilometres length. This demonstrates that the current maintenance concept is insufficient to satisfy the reliability requirements for failure of the revetment. Overall, the joint influence of degradation and the existing maintenance concept leads to a 20 times higher failure probability estimate compared to a typical assessment without these aspects. Next, we demonstrate that both additional inspections, and targeted interventions to reduce the impact of for instance animal burrowing, can significantly reduce total cost and improve robustness of the considered flood defence system.

Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection

Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: (1) their efficacy strongly depends on the appropriate selection of regularization parameters, (2) being univariate, they do not take advantage of the information present across the brain, and (3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce multivariate sparse paradigm free mapping (Mv-SPFM), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, Mv-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, we present a formulation tailored for multi-echo fMRI acquisitions (MvME-SPFM), which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with the echo time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation ΔR2∗) to the resulting deconvolved events. Remarkably, we demonstrate that Mv-SPFM achieves comparable performance even when using a single-echo formulation. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Furthermore, we show that by employing stability selection, the performance of the algorithm depends less on the selection of temporal and spatial regularization parameters λ and ρ. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of ΔR2∗, for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.

Fractional Brownian motion: Small increments and first exit time from one-sided barrier

Let {BH(t)}t≥0 be a fractional Brownian motion indexed by non-negative time with initial value BH(0)=0 almost surely and Hurst parameter H∈(0,1). By using a random wavelet series representation of {BH(t)}t≥0, we show that the fractional Brownian increment |BH(t+h)−BH(t)| is almost surely bounded from above by C|h|Hloglog|h|−1 when the time variation |h| is sufficiently small, where the random variable C>0 does not depend on t nor on h; and an upper bound of the p’th moment of C, E[Cp], is provided for each p>0. This result fills some gap in the law of iterated logarithm for fractional Brownian motion, by giving the moments’ control of the almost sure upper bound of fractional Brownian increments. With this enhanced upper bound and some new results on the distribution of the maximum of fractional Brownian motion maxt∈[0,T]BH(t), we obtain a new and refined asymptotic estimate of the upper-tail probability P(τb>T) as T→+∞, where τb is the waiting time for {BH(t)}t≥0 (with H<1/2) to first exit from a positive-valued barrier b.

Abstract 245: A Validated Score to Predict Poor Neurological Outcome After 72 Hours of Refractory VF/VT in Out of Hospital Cardiac Arrest Patients Requiring Extracorporeal Cardiopulmonary Resuscitation

Introduction: The challenge with neuroprognostication is finding a multimodal prediction model that yields the lowest false positive rate for a poor outcome. Objectives: To develop and validate a score that accurately predicts poor neurological outcome after 72 hours of ECPR initiation. Methods: Single-center, retrospective cohort study included 361 OHCA patients who underwent ECPR at the University of Minnesota between December 2015-January 2023. In a 50% random modeling sample, we obtained a derivation (n=180) and validation sample (n=181). The primary outcome was poor neurological outcome (defined as CPC 3-5) at hospital discharge. Neurological predictors were neuro specific enolase (NSE)&gt;3ULN on day 1, myoclonus status epilepticus on day 1-3, neurological pupillary index (NPI)&lt;2 on day 3-6, Glasgow motor=3 on day 3-6, highly malignant pattern on EEG (hmEEG) on day 3-6, CT with anoxic brain injury on day 3, and absent of brainstem reflexes on day 3-6. Univariate and multivariate analysis were performed on each predictor. A simple prediction score tool was constructed and evaluated through ROC analysis. A youden index was used for optimal cut-off. Results: Of 361 patients, 170 (47%) survived to hospital discharge. Of these, 85% (145/170) survived with favorable neurological outcome. The final multivariate logistic regression model in the derivation sample retained eight predictors: Absence of witnessed status and bystander CPR; downtime≥ 7min, lactate≥ 9mmol, low flow≥ 55min, NSE&gt;3ULN, hmEEG, CT with anoxic brain injury and absence of brainstem reflexes, which were combined to develop an 8-point risk scoring model Minnesota OHCA using ECPR (MECPR). Model discrimination had an AUC of 0.87 [95%CI 0.82-0.92] in the derivation sample and 0.86 [95% CI 0.85-0.92] in the validation sample, and calibration (goodness of fit p=0.04). MECPR≥6 predicted poor neurological outcome with a sensitivity ~21%, specificity 100% and a FPR 0%. A MECPR ≥4 and ≥6 had an 89% and 97% probability of poor neurological outcome, respectively. Conclusions: A simple, accurate prediction scoring tool (MECPR) based on a multimodal approach after 72hrs of refractory cardiac arrest provides estimates of probability of poor neurological outcome among patients treated with ECPR.

Reasoning from background knowledge: Evaluating and explaining behaviour in Finnish rape judgments

According to explanationist theories of evidence, fact-finders reason by evaluating the ‘goodness’ of different narratives that explain the evidence. One standard is external coherence: does the narrative fit with what the fact-finder knows or assumes about the world? This study uses qualitative content analysis to examine how District Court judges draw inferences directly from ‘background knowledge’ in 119 Finnish rape cases and how these inferences are contested by dissenting and appellate judges. The results show that especially the complainant's behaviour was frequently evaluated against behavioural scripts and other background beliefs. Outspoken reliance on rape myths was relatively rare, and myths were explicitly resisted as often as they were relied upon. Where judges used stereotypical behaviour to support the rape complainant's testimony, this reasoning could be explained by the search for optimum (explanatory) coherence or the Finnish legal principle that a rape complainant's testimony requires supporting evidence. Judges also used estimates of prior probabilities to inform their reasoning; few behaviours are impossible, but some actions are ‘more coherent’ with rape than with non-rape, or vice versa. Accruing the type of knowledge necessary (and usually unavailable) for probabilistic reasoning is therefore necessary also for explanationist reasoning.

Dynamic occupancy modeling of a cryptic ground-dwelling pheasant species in the Upper Blue Nile Basin in Ethiopia

Modeling the occupancy of species in the context of habitat components is a crucial step to deliver an appropriate conservation strategy. Accounting for imperfect detection in occupancy models helps to conclude on true species distribution and occupancy. We used dynamic occupancy modeling to investigate the influence of habitat covariates on occupancy dynamics of the Near Threatened Harwood’s Francolin (Pternistis harwoodi) in the Upper Blue Nile Basin in Ethiopia. We used direct observation and playback technique to collect presence/absence data both during a wet and a dry season in 2019 and 2020. By accounting for species’ imperfect detection, the model averaged estimates of occupancy probabilities (mean ± SE) across respective seasons were 0.81 ± 0.08 and 0.79 ± 0.07 and detection probabilities were 0.47 ± 0.08 and 0.62 ± 0.06. The colonization and local extinction probability estimates between seasons were 0.59 ± 0.20 and 0.12 ± 0.07, respectively. We demonstrate that occupancy probability significantly decreased with increasing both Normalized Difference Vegetation Index (NDVI: ꞵmean ± SE = −1.83 ± 0.66; 95% CI: −3.12, −0.54) and quadratic term of slope (SL2 = −1.51 ± 0.62; 95% CI: −2.73, −0.29) in the study area. Furthermore, human disturbance index (HDI: = −1.06 ± 0.54; 95% CI: −2.12, −0.004) significantly negatively influenced the occupancy of the species. As we hypothesized, the detection probability increased significantly as a function of average temperature (0.37 ± 0.13; 95% CI: 0.12, 0.63). There were no statistically significant associations among covariates and the dynamic parameters, yet important covariates such as NDVI slightly negatively influenced colonization, whereas HDI positively influenced local extinction. The aversion of the species towards human disturbance and its persistence at lower NDVI and lower slopes has important implications for conservation strategy in the area. The current study demonstrates empirical evidence of dynamic occupancy modeling for a cryptic ground-dwelling pheasant species in the Upper Blue Nile Basin. Further study is recommended to understand spatiotemporal species-habitat association at fine and landscape scales.

Stochastic management of electric vehicles in an intelligent parking lot in the presence of hydrogen storage system and renewable resources

The paper emphasizes the significance of sustainable energy solutions centered around electric vehicles (EVs). This involves Electric Intelligent Parking Lots (IPLs) that are interconnected with Renewable Energy Sources (RES) and Hydrogen Storage Systems (HSS) to achieve both technical and environmental goals. The study introduces a probabilistic approach to managing HSS-based energy in IPLs, employing the Probabilistic Two-Point Estimate Method (TPEM) to consider variables like sun radiation, temperature, wind speed, and IPL load. The Honey Badger Algorithm (HBA) is employed for optimization, striving to minimize operational costs related to energy distribution while maximizing profits by exploring revenue opportunities within the IPL system. This strategy enhances energy management and intelligent EV charging in IPLs, improving efficiency and economic viability. The approach is tested in a standard IPL environment integrated with RES and load demand, showcasing its successful technical and financial outcomes. Results reveal a 7.8 % profit increase from renewable resources and an additional 4.89 % when uncertainties are factored in. Ultimately, the proposed energy management framework utilizing HSS and RES integration proves effective in achieving objectives, with the potential for greater profitability through renewable resource incorporation and uncertainty consideration.

Evaluating temporal turnover in avian species richness in a Mediterranean semiarid region: Different responses to elevation and forest cover

AbstractAimWhen studying the effects of global change on biodiversity, it is far more common for the effects of climate change and land‐use changes to be assessed separately rather than jointly. However, the effects of land‐use changes in recent decades on species richness in areas affected by climate change have been less studied. We assess the temporal turnover in species richness of an avian community between a historical period and a modern one as a consequence of global change.LocationSemiarid Mediterranean ecosystem (southeastern Spain).MethodWe fitted a hierarchical multispecies occupancy model for each period (1991–1992, and 2012–2017), obtaining avian species‐specific estimates of occupancy probability in relation to environmental covariates (elevation and forest cover). We analyse the relationships between changes in the bird community and environmental variables, analysing the temporal turnover of the species richness and the richness‐based species‐exchange ratio.ResultsThe estimated species richness accounting for detectability was higher than observed species richness, and decreased in the more recent period. Following our hypotheses, we observed a dual pattern of species richness increase associated with different elevations, showing different species turnover rates due to the joint effects of climate change and land‐use change. There is a trend towards greater species richness with higher elevations that is associated with climate change, where the species turnover rate is low. Also, species richness increased towards lower elevations, but with a high turnover rate. The latter can be due to species expansions throughout new habitat configurations in bordering forest systems associated with anthropic land‐use changes.ConclusionsOur study is of great interest to understand the temporal turnover of avian species richness associated with areas experiencing both climate and land‐use change.

On the probability of down-crossing and up-crossing rogue waves

By means of the direct numerical simulation of directional waves on the surface of deep water, it is shown that extreme waves can exhibit such asymmetry that the occurrence of deeper troughs is several times more likely on the wave rear slopes. This effect becomes most pronounced in the case of steep short-crested waves. It is not related to the Benjamin–Feir instability but is a result of complex contribution from nonlinear combination harmonics, mainly cubic in nonlinearity. The discovered asymmetry can lead to remarkably different estimates of the rogue wave probability based on either down- or up-zero-crossing methods for individual wave selection, commonly used in the oceanography.

Probabilistic Estimates of Variations in Applied Indicators of the Thermal Regime for the Adaptation to Climate Change in Russia

Probabilistic Estimates of Variations in Applied Indicators of the Thermal Regime for the Adaptation to Climate Change in Russia

Occupancy of the Ethiopian endemic Moorland Francolin in pristine and degraded Afroalpine biome using a camera trap approach.

Occupancy modeling is an essential tool for understanding species-habitat associations, thereby helping to plan the conservation of rare and threatened wildlife species. The conservation status and ecology of several avian species, particularly ground-dwelling birds, are poorly known in Ethiopia. We used camera trap-based occupancy modeling to investigate habitat covariate influence on occupancy (Ψ) and detection probability (ρ) estimates of Moorland Francolins Scleroptila psilolaema from spatially replicated surveys across both relatively pristine and disturbed landscapes in the Afroalpine biome of Ethiopia. Model-averaged estimate of across all sites was 0.76 (SD = 0.28) and was 0.77 (SD = 0.13) in the pristine landscape. The of the species in the disturbed landscape was 0.56 (SD = 0.19) and was 0.48 (SD = 0.06). As hypothesized, based on our model-averaged beta coefficient estimates (βmean ± SE), predators significantly negatively influenced the occupancy of Moorland Francolins in pristine habitat. We also found a significant positive association of occupancy with herb species richness. Contrary to our prediction, distance to road significantly negatively influence the occupancy of the species, suggesting that occupancy probability was highest in proximity to roadsides and trails in the pristine habitat. There was no significant influence of habitat covariates on the occupancy of the species in the disturbed habitat. The most important covariates that significantly influence the detectability of the species in pristine habitat included sampling occasion and precipitation. The greater occupancy and detectability of this endemic species in the pristine habitat could be linked with the particular conservation status and management of this biodiversity hotspot in the central highlands of Ethiopia. Our results suggest that strict legal enforcement is required to sustainably preserve Moorland Francolins and the ecological integrity of the entire Afroalpine biome. We recommend using camera traps in order to develop realistic and effective conservation and management strategies for rare, sensitive, cryptic, and ground-dwelling animals in the region.

Probabilistic finite element-based reliability of corroded pipelines with interacting corrosion cluster defects

The random distribution of pitting corrosion defects in pipelines normally leads to interacting cluster defects that behave noticeably different from single metal loss defects. Generally, the probabilistic approaches employ explicit burst pressure limit states for corroded pipelines subjected to only internal pressure. However, a significant level of conservatism is typically associated with the probabilistic assessments of corroded pipelines using the closed-form explicit limit state functions, which presents considerable challenges in maintenance planning and risk management. Therefore, this paper proposes a pathway for developing efficient performance functions for the assessment of interacting pipeline corrosion clustering defects using probabilistic finite element-based reliability method. This seven-stage framework combines multiple uncertainty representation schemes to evaluate the probability of failure. The impact of the critical design variables such as the elastic and plastic material properties, corrosion features, and interacting cluster defect characterisations are identified to guide the burst pressure design, operations and maintenance optimisation. The employed surrogate-based active learning reliability approach yielded an efficient probability estimate at a lesser computational cost than the simulation-based reliability methods. The proposed framework reduces the conservatism and computational cost related with explicit burst pressure limit state functions for corroded pipelines and offers informed decision-making on risk and maintenance management.

Biological sex estimation with femoral dimensions: a study of an adult sample from the osteological collection of Granada (Spain)

ABSTRACT Estimating the biological sex is one of the key objectives in a forensic investigation involving unidentified human skeletal remains. The femur is sexually dimorphic and has been extensively employed for the assessment of sex in different populations. The purposes of this article include a detailed macroanatomical characterization of the femur in a modern reference sample of the Spanish population (N = 226; Cemetery of San José in Granada, housed in the Physical Anthropology Laboratory of the University of Granada), based on 15 measurements of the proximal, distal and diaphyseal regions, and the development of univariable and multivariable models that enable the prediction of sex from complete or fragmentary femora. Univariable models were developed with C4.5, a decision-tree algorithm. Furthermore, univariable and multivariable models were generated through logistic regression (LR) with stepwise selection procedures. The models were able to correctly estimate sex in up to 91.2% of the individuals, providing high classification accuracy and low bias under cross-validation. Logistic regression models provide a probabilistic estimate of sex, that acknowledges both the Daubert standard and the fact that biological sex is not strictly dichotomous.

The positional probability and true host star identification of TESS exoplanet candidates

Abstract We present a method for deriving a probabilistic estimate of the true source of a detected TESS transiting event. Our method relies on comparing the observed photometric centroid offset for the target star with models of the offset that would occur if the event was either on the target or any of the Gaia identified nearby sources. The comparison is done probabilistically, allowing us to incorporate the uncertainties of the observed and modelled offsets in our result. The method was developed for TESS Full Frame Image lightcurves produced from the SPOC pipeline, but could be easily adapted to lightcurves from other sources. We applied the method on 3226 TESS Objects of Interest (TOIs), with a released lightcurve from SPOC. The method correctly identified 96.5% of 655 known exoplanet hosts as the most likely source of the eclipse. For 142 confirmed Nearby Eclipsing Binaries (NEBs) and Nearby Planet Candidates (NPCs), a nearby source was found to be the most likely in 96.5% of the cases. For 40 NEBs and NPCs where the true source is known, it was correctly designated as the most likely in 38 of those. Finally, for 2365 active planet candidates, the method suggests that 2072 are most likely on-target and 293 on a nearby source. The method forms a part of an in-development vetting and validation pipeline, called RAVEN, and is released as a standalone tool.

Study on the Transition to True North in Air Navigation

The paper is an introductory study on the possible transition from Magnetic North reference to True North reference in air navigation, as envisaged by the International Association of Institutes of Navigation’s AHRTAG Group. The use of the Magnetic Field of the Earth as a direction reference in aviation is explained briefly. Magnetic North is an unstable and irregular directional reference that aviation manages well, but with significant costs. The unpredictability and uncertainties of the Magnetic Field of the Earth might be critical in the future, especially in the case of reversal of the magnetic poles, or incipient reversal. The paper puts forward the case for calculating the probability of such a catastrophic event, with a view to engaging further expert research in the geomagnetic phenomena. The purpose of such a probability estimate would be for the aviation decision makers to determine whether contingency planning might be required or not. Furthermore, the paper analyses the adoption of True North in maritime navigation as a possible model.

Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data

We release a database of cloze probability values, predictability ratings, and computational estimates for a sample of 205 English sentences (1726 words), aligned with previously released word-by-word reading time data (both self-paced reading and eye-movement records; Frank et al., Behavior Research Methods, 45(4), 1182–1190. 2013) and EEG responses (Frank et al., Brain and Language, 140, 1–11. 2015). Our analyses show that predictability ratings are the best predictors of the EEG signal (N400, P600, LAN) self-paced reading times, and eye movement patterns, when spillover effects are taken into account. The computational estimates are particularly effective at explaining variance in the eye-tracking data without spillover. Cloze probability estimates have decent overall psychometric accuracy and are the best predictors of early fixation patterns (first fixation duration). Our results indicate that the choice of the best measurement of word predictability in context critically depends on the processing index being considered.

Effectiveness of perioperative chemotherapy and radical cystectomy in treating bladder cancer

BackgroundDespite abundant evidence supporting the use of perioperative chemotherapy from clinical trials, no study to date has comprehensively evaluated its use in the treatment of muscle-invasive bladder cancer (MIBC) in the real-world setting. Little is known regarding the impact of pretreatment disease stage and real-world factors such as patient comorbidities preventing timely completion of therapy on its effectiveness. This study aims to assess the usage of perioperative chemotherapy and examines its impact on pathologic downstaging rates and recurrence free survival in patients undergoing radical cystectomy. MethodsA retrospective review was conducted in 805 patients with muscle invasive bladder cancer undergoing radical cystectomy with no perioperative chemotherapy, 761 with presurgical chemotherapy followed by radical cystectomy, and 134 radical cystectomy followed by adjuvant chemotherapy. Relevant clinicopathologic features were reviewed. Recurrence-free survival and Overall Survival probability estimates were calculated using the Kaplan–Meier method and compared using the Log-rank or Gehan-Breslow tests. The prognostic effects of presurgical chemotherapy and adjuvant chemotherapy regimens were evaluated by estimating hazard ratio and 95% confidence interval from an adjusted Cox proportional hazards model. Statistical tests were 2-sided, and significance was defined as P-value < 0.05. ResultsIn this contemporary, real-world cohort, 5-yr RFS was found to be 65.6% in pT0, 59.1%in <pT2, 35.4% in pT2, 15.3% in >pT2, and 10.8% in pN+ patients. Presurgical chemotherapy increased pathologic downstaging rates from 27.5% to 41.1% in patients with ≥cT2 BCa. Stratified by clinical T-stage, only cT2 patients derived recurrence-free survival (Median 45.3 months vs. 29.0 months, P < 0.01) and overall survival (Median 62.3 months vs. 41.9 months, P < 0.001) benefits. In patients with adverse pathologic features (≥pT3 or pN+), adjuvant chemotherapy improved recurrence-free survival (Median 22.8 months vs. 10.0 months, P < 0.0001) and overall survival (Median OS 32.4 months vs. 16.3 months, P < 0.0001). ConclusionsWe report real-world outcomes from a large cohort of muscle-invasive bladder cancer patients undergoing surgical treatment with/out perioperative chemotherapy. Pathologic response rates to pre-surgical chemotherapy were modest and led to clinical benefit only in cT2 patients. Adjuvant chemotherapy provided survival benefit for pathologically advanced MIBC patients irrespective of pT/N staging.

Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA

Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as &gt; 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

Multiscale Coupling and the Maximum of {mathcal {P}}(phi )_2 Models on the Torus

We establish a coupling between the {mathcal {P}}(phi )_2 measure and the Gaussian free field on the two-dimensional unit torus at all spatial scales, quantified by probabilistic regularity estimates on the difference field. Our result includes the well-studied phi ^4_2 measure. The proof uses an exact correspondence between the Polchinski renormalisation group approach, which is used to define the coupling, and the Boué–Dupuis stochastic control representation for {mathcal {P}}(phi )_2. More precisely, we show that the difference field is obtained from a specific minimiser of the variational problem. This allows to transfer regularity estimates for the small-scales of minimisers, obtained using discrete harmonic analysis tools, to the difference field.As an application of the coupling, we prove that the maximum of the {mathcal {P}}(phi )_2 field on the discretised torus with mesh-size epsilon > 0 converges in distribution to a randomly shifted Gumbel distribution as epsilon rightarrow 0.

On the Implications of Ignoring Competing Risk in Survival Analysis: The Case of the Product-Limit Estimator

Although the Kaplan-Meier (KM) is a single event model, it is frequently used in literature with datasets that are assumed to be cause-specific without any proper verification. It is crucial to evaluate the implication of this on the probability estimates. This study compares the estimates of the cumulative incidence functions to the complement of the product-limit estimator (1-KM). The KM was found to inflate probability estimates when the dataset is unverified for competing risk. Estimates with lower standard errors and a larger area under the Receiver Operation Characteristic (ROC) curve were related to datasets verified for competing events, while estimates with datasets unverified for competing risk had lower area under the ROC curve and higher standard errors. The results support the idea that since the product-limit estimator is a cause-specific model, it naturally performs better for a single event model than in the case of several events; hence, it is necessary to verify the dataset for competing events. The findings of this study clearly suggest that before choosing a modelling strategy, one should confirm competing risks in the survival dataset.