Probability Of Events Research Articles

Association of dietary index for gut microbiota and all-cause and cardiovascular mortality in patients with diabetes or prediabetes

BACKGROUND The dietary index for gut microbiota (DI-GM) demonstrates associations with diabetes prevalence and related mortality outcomes, serving as a nutritional assessment tool for microbial community evaluation. AIM To investigate connections between DI-GM values and survival endpoints in populations with impaired glucose metabolism, incorporating both total mortality and cardiovascular-related fatal events. METHODS Cox proportional hazards modeling through survival analysis evaluated the relationship between DI-GM quartile classifications and fatal event probabilities. Restricted cubic spline modeling evaluated non-linear associations between continuous DI-GM values and mortality endpoints. Stratified analyses and robustness checks ensured the validity of the results. RESULTS Higher DI-GM values showed a statistically significant negative correlation with total mortality risk [hazard ratio (HR) = 0.96, 95%CI: 0.93-1.00] and cardiovascular-related fatal outcomes (HR = 0.93, 95%CI: 0.87-0.99). When comparing quartiles, analysis indicated that participants in the upper quartile (Q4) had 17% decreased likelihood of all-cause death (HR = 0.83, 95%CI: 0.69-0.99) and 25% lower probability of cardiovascular mortality (HR = 0.75, 95%CI: 0.54-1.00) relative to those in the lowest quartile (Q1). CONCLUSION These findings position DI-GM as a protective determinant against mortality in glucose metabolism disorders. Dietary pattern optimization targeting DI-GM enhancement could constitute a strategic intervention in diabetes care protocols.

A new statistical method of rapid event attribution for probability of extreme events: applications to heatwave events in Japan

Abstract We have developed a new statistical method of rapid event attribution (EA) that can immediately estimate the probability of a specific extreme event and attribute it to long-term climate change including anthropogenic global warming by using existing long-term large ensemble (LE) climate simulations with an atmospheric general circulation model and observational data. This paper describes the new EA method with the example of summer heat waves that have occurred in Japan. The probability distribution functions of the temperature over Japan are well approximated with the Gaussian (Gauss) distribution or the generalized extreme value distribution. We propose the new methodology to estimate parameters of a distribution function as a function of global sea surface temperature (SST) anomalies or equivalent atmospheric variables under factual climate conditions and counterfactual non-warming climate conditions using a regression model, which enables us to incorporate the influence of underlying oceanic natural variability into the EA system. The key points of our method are that (1) it is based on findings that the location, scale, and shape parameters of the probability density function of temperature in Japan are closely linked to the primary modes of internal variability in the atmosphere and ocean, (2) changes in these parameters induced by the anthropogenic climate change can also be reconstructed from the trend patterns of the SST and (3) considering atmospheric forced responses to SST variations in the statistical relationship improves the estimation accuracy. We confirmed that this approach can successfully estimate the probability of several past extreme temperature events in Japan under the factual and counterfactual climate conditions which is comparable to the estimation of conventional LE method. This new rapid EA method will be applicable to other types of extreme events and to events all over the world.

Abstract B012: Prompting Large Language Models to Predict Adverse Events during Cancer Treatment

Abstract Background: Large language models (LLMs) excel on standardized oncology exams; however, their broader clinical utility remains unclear. LLMs are easy to use through “prompting.” For example, a doctor or patient can provide a clinical note and ask about the probability of an adverse event (AE). Current AE prediction relies on machine learning using tabular data, which requires substantial engineering to adapt to specific tasks and settings, making them costly and less generalizable. We compared prompting LLMs against tabular ML models to predict AEs during systemic cancer therapy. Materials an. Methods: Patients with aerodigestive cancers at Princess Margaret Cancer Centre who received their first systemic therapy from 2008 to 2015 formed the development set, and from 2016 to 2018 formed the test set. We evaluated different prompting strategies with open-source LLMs using the de-identified consult and most recent pre-treatment note from each patient to predict the risk of clinical, symptom, and laboratory AEs. An ensemble of ML models was trained on tabular electronic health record data for comparison. We measured performance with the area under the receiver-operating characteristic curve (AUC). Using an established schema, an oncologist reviewed the text-based justifications from 20 random LLM predictions. Results The cohort included 6,381 patients. Notes had a median token length of 1,737 (range 137-7,795). The LLM Qwen 2.5 14B achieved the best AUC across 14 of 19 AEs in the development set. The larger 14B model outperformed the 7B model on all targets (p = 4e-5). Among prompting strategies, no benefit was observed with the oncologist versus AI model persona (p = 0.21), chain-of-thought reasoning (p = 0.23), or concatenating tabular data to notes (p = 0.42). In the test cohort, LLMs and tabular ML showed equivalent performance for some AEs, such as death within 30 days (LLM AUC: 0.73 [95% CI 0.66, 0.80], versus [v.] ML: 0.74 [0.67, 0.81], p = 0.89) and hyperbilirubinemia (0.79 [0.72, 0.86] v. 0.78 [0.70, 0.85], p = 0.77). For other AEs, performance was numerically similar, such as death in one year (0.72 [0.70, 0.74] v. 0.76 [0.73, 0.78], p = 0.02) and anemia (0.78 [0.75, 0.80] v. 0.82 [0.8, 0.84], p = 0.01). LLMs performed worse for symptom-related AEs, such as pain (0.48 [0.44, 0.53] v. 0.69 [0.65, 0.74], p = 1e-11) and tiredness (0.49 [0.45, 0.52] v. 0.69 [0.65, 0.72], p = 2e-14). The oncologist deemed LLM justifications satisfactory across all dimensions for at least 90% of predictions, except that 20% had factual consistency errors. Conclusion: Prompting LLMs performed similarly to engineered tabular ML models for predicting several AEs, despite using only raw text from notes. Better performance with larger models suggests the gap between LLMs and ML models may continue to narrow. This work lays the foundation for using LLMs as general-purpose clinical decision-support tools for cancer care. Citation Format: Wayne Isaac T. Uy, Galileo Arturo Gonzalez Conchas, Jiang Chen He, Muammar Kabir, Baijiang Yuan, Geoffrey Liu, Sharon Narine, Melanie Powis, Benjamin Grant, Mattea Welch, Tran Truong, Robert Grant. Prompting Large Language Models to Predict Adverse Events during Cancer Treatment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B012.

A novel robot-assisted subretinal injection system: preliminary validation in ex vivo porcine eyes.

To evaluate the application of a novel, partially automated, robot-assisted ophthalmic surgery for subretinal injection, a study was conducted comparing manual and robot-assisted injections performed by doctors of varying experience levels on fresh, isolated porcine eyes. This system was developed by a team from Harbin Institute of Technology. The core component is a specialized microneedle with adjustable puncture angle and integrated force-sensing capability. It enables flexible intraocular manipulation during subretinal injection procedures and performs autonomous retinal puncture guided by force feedback. Results showed that robot-assisted injections by all doctors achieved a 100% success rate without safety events such as retinal laceration or RPE penetration. While the senior doctor exhibited the highest manual injection success rate and lowest safety event probability, medical students showed the opposite. Compared to manual injection, robot-assisted injection demonstrated smaller needle force variation, more regular retinal "bubble" shape, and more stable subretinal fluid volume (p < 0.05). The surgical robot can accurately perform automatic subretinal injection with high success and good safety, potentially shortening the learning curve and replacing manual operation in the future.

Precision of predictive nomograms for lymph node metastasis of thyroid cancer from Chinese real-world study: a systematic review and meta-analysis

BackgroundCurrent guidelines lack nomograms to predict lymph node metastasis (LNM) in thyroid carcinoma (TC) in China. Nomograms are simple, accurate tools to estimate the probability of specific events and have been extensively developed to predict LNM in TC. However, few effective nomograms have been validated in clinical practice.MethodsThe recommendations of the Cochrane Prognosis Methods Group were implemented in this systematic review. We conducted searches in PubMed, Web of Science, and Scopus for published research. The nomogram was categorized based on outcomes. We summarized the key characteristics and effectiveness of the nomogram and assessed the overall risk of bias (ROB). We employed random-effects and bivariate mixed-effects models to estimate the efficacy of the nomogram group and its predictive reliability.ResultsThe systematic review identified 57 nomogram models from China, of which only 14 had external validation cohorts. While the applicability was acceptable, the heterogeneity among the included nomograms was substantial, leading to a high overall risk of bias (ROB). Ultrasound information was utilized in nearly all studies. Size, extrathyroidal extension (ETE), tumor consistency index (TCI), and multifocality are commonly employed independent risk factors. Both outcome models showed good to excellent predictive efficacy. However, the performance of models that integrate radiomics with clinical features was inferior to those using ultrasound alone.ConclusionsThe feature-combined model offers several potential outcomes and advantages for clinical practice in China. Additionally, the systematic review serves as a reference tool for physicians to select appropriate nomograms based on individual clinical needs. Future research should focus on external validation and evaluation to minimize limitations in clinical utility.

Mean-field limits for Consensus-Based Optimization and Sampling

For algorithms based on interacting particle systems that admit a mean-field description, convergence analysis is often more accessible at the mean-field level. In order to transfer convergence results obtained at the mean-field level to the finite ensemble size setting, it is desirable to show that the particle dynamics converge in an appropriate sense to the corresponding mean-field dynamics. In this paper, we prove quantitative mean-field limit results for two related interacting particle systems: Consensus-Based Optimization and Consensus-Based Sampling. Our approach requires a generalization of Sznitman’s classical argument: in order to circumvent issues related to the lack of global Lipschitz continuity of the coefficients, we discard an event of small probability, the contribution of which is controlled using moment estimates for the particle systems. In addition, we present new results on the well-posedness of the particle systems and their mean-field limit, and provide novel stability estimates for the weighted mean and the weighted covariance.

The neurophysiological features of anticipation in schizophrenia: a cross-sectional study of event-related potentials

BACKGROUND: It is known that disorders of mental activity in schizophrenia patients may be caused by an impairment in the actualization of past experience during anticipation (prediction), which leads to impairment in constructing predictions, comparing incoming sensory information with the predictions, and updating the predictions. Previous studies have shown that the probability of an expected event affects the components of event-related potentials in mentally healthy individuals. However, it has not yet been studied how changes in the probability of an expected stimulus influence the behavior of individuals with schizophrenia and their event-related potential measures. AIM: To compare the influence of event probability on the characteristics of brain potentials in patients with schizophrenia and healthy individuals. METHODS: The study included mentally healthy individuals and male schizophrenia patients. Electroencephalograms were recorded while participants performed a saccadic task within the Central Cue Posner’s Paradigm under conditions of varying probability (50% and 80%) of target stimulus presentation. Pre-stimulus (Contingent Negative Variation) and post-stimulus (Mismatch Negativity and P3) components of event-related potentials were analyzed upon the presentation of two types of target stimuli: standard (presented on the same side as the cue stimulus) and deviant (presented on the opposite side), under conditions of 50% and 80% stimulus congruence probability. RESULTS: The study involved 20 mentally healthy individuals and 18 schizophrenia patients. In healthy subjects, the amplitude of the contingent negative variation increased with high stimulus congruence probability, while the amplitude of the Mismatch Negativity (MMN) and P3 component was higher for deviant stimuli under conditions of high (80%) probability. In schizophrenia patients, changes in probability demonstrated no impact on the amplitude of the contingent negative wave, MMN, or P3. CONCLUSION: The characteristics of event-related potentials in patients with schizophrenia indicate impaired anticipation processes.

Possible influence of human activity on the daily maximum precipitation events over the Three-River Headwaters region of the Tibetan Plateau

The Three-River Headwater (TRH) region, located in the hinterland of the Tibetan Plateau (TP), has experienced significant wetting since the early 2000s. However, the potential contribution of external forcings to the growing frequency of extreme precipitation events in this area has been rarely examined. Here, we conduct an analysis on the change in daily maximum precipitation (Rx1day) in the TRH region, with a focus on the anomalous event in 2023, and an investigation into the influence of external forcings on the occurrence probability of the Rx1day events. The results indicate that extreme precipitation has increased in the TRH region, particularly after the early 2000s, with a record anomalous Rx1day event in 2023. Further analysis suggests that anthropogenic forcings may have reduced the probability of Rx1day events in general, though greenhouse gases have increased atmospheric water vapor, offsetting the effect of anthropogenic aerosols. And aerosol forcing inhibits Rx1day events mainly through dynamic processes. However, the occurrence probability of a 2023-like anomalous Rx1day event may have been primarily related to anthropogenic factors, with greenhouse gases playing a key role by enhancing atmospheric moisture. This study emphasizes the crucial role of anthropogenic forcings in precipitation extreme in the TRH region.

Large deviation theory approach to fluctuation theorems and Landauer’s principle through heat redefinition

Large deviation theory (LDT) provides a mathematical framework to quantify the probabilities of rare events in stochastic systems. In this study, we applied LDT to model a chemical reaction system and demonstrated that the fluctuation theorem for nonequilibrium reaction systems can be derived from the symmetry of the cumulant generating function defined through the rate function. Notably, this derivation does not depend on the assumption of local detailed balance. Furthermore, we redefined heat using this rate function based on information theory and evaluated Landauer's principle, which addresses the minimum energy cost associated with information erasure. These findings show the utility of LDT as a comprehensive framework for analyzing a wide range of nonequilibrium systems.

Spatiotemporal patterns of wet–dry encounters between water source and receiving areas in the South-to-North water transfer project

As global water scarcity becomes more acute, cross-basin water diversion projects, such as the South-to-North Water Diversion Project in China, need scientific risk assessment methods to ensure sustainable water allocation. This study proposes a coupling analysis framework based on Copula-Bayesian, which fills the gap in the systematic risk research of large-scale water transfer projects. By constructing a joint annual runoff distribution model for the Yangtze River and Yellow River, combined with mutation point detection, periodicity analysis (Morlet wavelet) and frequency statistics of boom-bust encounter, the characteristics of hydrological variability under climate change were systematically evaluated. The risk of water diversion under different scenarios is quantified by using Bayesian network for probabilistic reasoning. The results showed that: (1) During 1959–2023, three abrupt runoff points were detected in both the Yangtze River basin (YTB) and the Yellow River basin (YRB), with periodicism of 27/17/11 years and 12/27/29 years, respectively. The runoff of the lower Yellow River showed a significant downward trend (− 1.2%/ decade, p < 0.05). (2) The occurrence probability of extreme flood and ebb synchronous events is less than 5%, while the overall distribution of asynchronous and synchronous events is balanced (45–55%), indicating that the risk of water transfer is generally controllable; (3) The four-level water diversion risk scenario (optimal to most unfavorable) is identified through probability simulation, showing that the low-risk operation probability of the project under current conditions is 68%. This study innovatively combines statistical hydrological models with risk simulation to provide decision support for inter-basin collaborative water resources management in the context of climate change.

Application and Verification of Convective Scale Ensemble Forecast for a Heavy Precipitation Event That Occurred in Eastern Southwest China

ABSTRACTThis study aims to provide forecasters with valuable and practical insights into the effective application of convective‐scale ensemble forecasts for precipitation prediction. Statistical verification and subjective analyses were conducted on the forecast performance during a heavy precipitation event in eastern Southwest China. The results indicate that different postprocessed deterministic forecast products each have distinct advantages and limitations that forecasters should consider. The ensemble mean forecast (EMF) has shown strengths in forecasting small magnitude precipitation (i.e., light rain, moderate rain, and heavy rain events), but it tends to smooth out information regarding extreme precipitation. The probability‐matched EMF (PMEMF) outperforms the EMF for extreme precipitation predictions. In general, optimal ensemble quantile forecasts outperform the corresponding EMFs and PMEMFs, as well as most individual ensemble members, but notably, the optimal quantiles vary significantly across different cases. The ensemble forecast system is capable of predicting certain probabilities of heavy rainstorms and extraordinary rainstorm events as early as 4 days in advance. Based on the verification results, it is recommended that forecasters should remain cautious even when only a single or few ensemble members predict extremely heavy precipitation (or whether a certain probability of extreme precipitation exists, even if it is relatively low), thus helping to reduce decision‐making errors. Furthermore, probabilistic forecasting should be more comprehensively and effectively applied in China.

The Mental Health Bill (2025) for England and Wales: professional and carer consensus statement summarising concerns and unintended consequences from proposed changes to autism and learning disability.

The Mental Health Bill, 2025, proposes to remove autism and learning disability from the scope of Section 3 of the Mental Health Act, 1983 (MHA). The present article represents a professional and carer consensus statement that raises concerns and identifies probable unintended consequences if this proposal becomes law. Our concerns relate to the lack of clear mandate for such proposals, conceptual inconsistency when considering other conditions that might give rise to a need for detention and the inconsistency in applying such changes to Part II of the MHA but not Part III. If the proposed changes become law, we anticipate that detentions would instead occur under the less safeguarded Deprivation of Liberty Safeguards framework, and that unmanaged risks will eventuate in behavioural consequences that will lead to more autistic people or those with a learning disability being sent to prison. Additionally, there is a concern that the proposed definitional breadth of autism and learning disability gives rise to a risk that people with other conditions may unintentionally be unable to be detained. We strongly urge the UK Parliament to amend this portion of the Bill prior to it becoming law.

The role of host sea turtles in the dispersal of amphipod epibionts

Abstract Epibiosis is an interspecific association whereby one organism lives on the surface of another, called the host or basibiont. One of the many advantages of epibiosis is that the interaction with the basibiont may increase the epibiont’s dispersal capacity, especially when the basibiont are highly migratory, such as sea turtles. Such hosts are known to harbour a broad diversity of epibiont communities, and crustacean amphipods are one of the most abundant. The increase in dispersal capacity due to the epibiont habit may be a strategic adaptation for amphipods, which are direct-developmental organisms. To understand the role of sea turtles on the dispersal of their epibionts, we combined mitochondrial (Cytb) and nuclear (ITS-1) markers to assess population genetic structure and evolutionary history of the amphipod species (Podocerus chelonophilus, Hyachelia tortugae and Hyachelia lowryi) known as obligate commensals of sea turtles. We obtained amphipod specimens from 33 sea turtles of three distinct species: loggerhead (Caretta caretta), green (Chelonia mydas), and hawksbill (Eretmochelys imbricata) sampled in six localities along the Brazilian coast. Our results show high genetic diversity, low genetic structure, and high gene flow among the different turtle species and localities for all epibiont species. Our findings suggest an overlap between the life histories of the hosts and their epibionts and a probable colonization event of a small number of mitochondrial DNA lineages, leading to high gene flow over long distances promoted by historical and present interaction with the host sea turtles.

Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides

Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 − ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p < 0.05). Applying a 5% MFS event probability threshold stratifies patients into low- and high-risk groups. After dichotomization, combining RlapsRisk BC with clinico-pathological factors increases cumulative sensitivity (0.69 vs 0.63) and dynamic specificity (0.80 vs 0.76) compared to clinical factors alone. Expert analysis of high-impact regions identified by the model highlights well-established morphological features, supporting its interpretability and biological relevance.

Making Sense of Uncertainties: Ask the Right Question

Abstract Earth observation data should inform decision making, but good decisions can only be made if the uncertainties in the data are taken into account. Making sense of uncertainty information can be difficult, because uncertainties represent the statistical spread in the observations (e.g., expressed as $$x \pm y$$ x ± y ), which does not relate directly to one specific use case of the data. Here, we propose a Bayesian framework to transform Earth observation product uncertainties into actionable information, i.e., estimates of how confident one can be in the occurrence of specific events of interest given the data and their uncertainty. We demonstrate this framework using two case examples: (i) monitoring drought severity based on soil moisture and (ii) estimating coral bleaching risk based on sea surface temperature. In both cases, we show that ignoring uncertainties can easily lead to misinterpretation of the data, making any decisions based on these data unlikely to be the best course of action. The proposed framework is general and can, in principle, be applied to a wide range of applications. Doing so requires a careful dialogue between data users, to formulate meaningful use cases and decision criteria, and data producers, to provide a rigorous description of their data and its uncertainties. The next step would then be to confront the uncertainty-informed estimates of event probabilities (created by the framework proposed here) with the costs and benefits of possible courses of action in order to make the best possible decisions that maximize socioeconomic merit.

Predictive Modeling of Flood Frequency Utilizing an Analysis of the Casimcea River in Romania

Flooding is a recurrent natural hazard in Romania, causing significant socio-economic impacts. Historical data highlights the severity of floods, particularly the unprecedented flood of 1926. Between 1960 and 2010, Romania experienced over 400 major floods, which significantly impacted its infrastructure and population. Particularly, the floods in 2005 and 2006 affected over 1.5 million people, resulting in 93 deaths and causing damages exceeding EUR 2 billion. In compliance with the Floods Directive, EU member states must assess and map flood hazards and risks. This study aims to develop a frequency analysis to determine discharges as a predictive indicator for different hazard levels: frequent events (10-year return period), medium probability events (100-year return period), and extreme events. The Casimcea catchment in central Dobrogea, drained by the Casimcea River into Lake Tasaul, serves as the study area. The annual maximum discharge data analysis, conducted through frequency analysis and the ELECTRE method, indicates that EV3-Min-Weibull, L-moments, and GEV-Min (L-moments) are the most effective probability density functions (PDFs). To conclude, although a single PDF model cannot be determined for the Casimcea River and its tributaries, it contributes to predictive modeling efforts.

Trans-DeepMCM: A transformer-based deep dynamic mixture cure model

Risk prediction models in survival analysis estimate event probabilities and risk rates. Recent dynamic survival models utilize longitudinal data with joint models, landmark models, and machine learning methods. This article introduces Trans-DeepMCM, a novel deep dynamic mixture cure model integrating a transformer-based encoder with a non linear cure structure. Specifically, a transformer encoder is used to extract temporal patterns from irregularly observed longitudinal data into a dynamic context vector. This dynamic context vector is then used as input information for the DeepMCM network, in which the context vector is passed to the cure rate network to predict the cure probability π and simultaneously forwarded to the DeepSurv network to estimate the survival risk for uncured individuals. To improve interpretability, a two-stage SHAP (Shapley additive explanation) analysis is performed: the first stage attributes importance to input features contributing to the context vector; the second stage evaluates how each context vector component contributes to risk prediction. Experiments on PBC2 and AIDS datasets demonstrate superior performance in C-index and IBS compared to existing methods when the cure model is included. The proposed model consistently shows competitive and robust performance across different settings, providing an interpretable, dynamic, and personalized approach to survival prediction.

Prediction and mechanism analysis of mine rockburst based on regional seismic activity

Abstract To analyze the characteristics of mine rockburst under regional seismic activity, this study used seismic data from the southern mining area of Hegang City, Heilongjiang Province over the past 20 years to explore the correlation between mine rockburst and regional seismic activity through horizontal-to-vertical spectral ratio method and waveform inversion method. In the study, the surface to base spectral ratio was used to modify the horizontal vertical spectral ratio method to improve the accuracy of seismic site effect analysis. The analysis results showed that the frequency of earthquakes was relatively high between 2004 and 2017, reaching a peak of 715 in 2005, while the frequency significantly decreased after 2018. The release of strain energy reached its peak in 2016 and 2023, with magnitudes of 2.7 and 3.1, respectively. The depth of the epicenter was mainly concentrated between 12 and 15 kilometers, indicating a complex geological structure in the region. Through the analysis of the horizontal-to-vertical spectral ratio method, it was found that most areas belong to hard sites, with some areas having relatively hard sites. Microseismic activity was more frequent in 2012, 2014, 2018, and 2019, which was related to the increased coal mining activity. In addition, high-energy microseismic events were significantly correlated with earthquakes in time and space, with a higher probability of high-energy microseismic events occurring within a distance of less than 20 kilometers from the epicenter. The above results indicate that mine impacts are significantly affected by natural seismic activity. The study provides important data support and theoretical basis for the prediction and prevention of mine rockburst by analyzing the characteristics of seismic activity, seismic site effects, and the correlation between natural earthquakes and rockburst.

Statistical learning dynamically shapes auditory perception

Humans implicitly pick up on probabilities of stimuli and events, yet it remains unclear how statistical learning builds expectations that affect perception. Across 29 experiments, we examine the influence of task-irrelevant distributions—defined across acoustic frequency—on both tone detection in noise and tone duration judgments. The shape and range of the frequency distributions impact suppression and enhancement effects, as does a given tone's position within the range. Perception adapts quickly to changing distributions, but past distributions influence future judgments. Massed exposure to a single frequency impacts perception along a range of subsequently encountered frequencies. A novel bias emerges as well: lower frequencies are perceived as longer and higher ones as shorter. Probability-driven learning dynamically shapes perception, driven by interacting influences of sensory processing, distributional learning, and selective attention that sculpt a gain function involving modest enhancement of more-likely stimuli, and robust suppression of less-likely stimuli.

STATISTICAL ANALYSIS OF THE SITUATION IN GEOSTATIONARY ORBITS

Statistical analyses of near-miss events involving geostationary zone objects from 2011 to 2024, derived from orbital propagation based on geostationary zone object orbital element catalogs and observational data of events, are presented. The data reveal a general increase in the number of near-miss events, attributable to the newly launched satellites and enhanced detection capabilities for space debris fragments. Notably, the average daily distribution of near-miss events exhibits longitudinal asymmetry, with a higher number of such events at 75°E longitude compared to 255°E. Conversely, potentially hazardous mutual approaches with distances as low as 10 km are more prevalent at 255°E. An investigation into the relationship between the average daily number of such events and the minimum object separation as a function of the catalog epoch indicates an increase in daily dangerous approaches within 1 km from 0.24 events/day in 2011 to 0.39 events/day in 2024. When considering a 20-meter collision threshold, the 2024 data suggest a potential collision event probability in geostationary orbit of one event per eight years. Projections based on the current trajectory of orbit occupancy suggest that, if prevailing trends persist, collision events in GEO could occur every six years by 2030 and every four years by 2040. Keywords: Space debris, Near-Earth Space, Geostationary orbit, Near-Miss Events, Space situational awareness.