Probabilistic Case Research Articles

Estimation of HIV prevalence at the ZIP code-level in Atlanta, Georgia: Bayesian prediction modeling using passive surveillance data and social determinants of disease spreading

ObjectiveThis study aims to predict the number of undiagnosed HIV cases at the ZIP Code-level in Atlanta, Georgia, based on publicly available information. Study designStatistical modeling. MethodsWe fitted a Bayesian hierarchical Binomial model to county-level estimates of the passive-surveillance-system. The denominator was the true total HIV cases arising from a Negative Binomial distribution. The trial probability, known as ascertainment probability, depended on socio-economic determinants of HIV, retained via feature-selection algorithms. Data were obtained from CDC's HIV report for End of the HIV Epidemic and the American Community Survey. The prediction model was assessed out-of-sample in Georgia counties. We combined socio-economic data with the posterior predictive distribution of the coefficients to predict the mean ascertainment probability and total HIV cases at the ZIP Code-level. These estimates were spatially smoothed and aggregated at the county-level for secondary validations. ResultsThe county-level model showed good mixing properties and predictive accuracy. The mean ascertainment probability calibrated to the ZIP Code-level varied from 78.4% (95% credible interval: 24.4%–99.3%) to 93.8% (95%CI: 80.6%–99.8%). Further, the predicted undiagnosed HIV cases ranged between 12 (95%CI: 6–19; ZIP Code 30322) to 1603 (95%CI 1209–1968; ZIP Code 30318). ConclusionsOur findings provide a more complete picture of the relative burden of HIV across ZIP codes. Such information can be used by Local Health Departments to identify underserved areas and allocate resources accordingly. Furthermore, our methodological approach can be applied to complement the information obtained from passive surveillance, especially when more resource-intensive approaches are not available or are unfeasible to employ.

Optimality Conditions in Control Problems with Random State Constraints in Probabilistic or Almost Sure Form

In this paper, we discuss optimality conditions for optimization problems involving random state constraints, which are modeled in probabilistic or almost sure form. Although the latter can be understood as the limiting case of the former, the derivation of optimality conditions requires substantially different approaches. We apply them to a linear elliptic partial differential equation with random inputs. In the probabilistic case, we rely on the spherical-radial decomposition of Gaussian random vectors in order to formulate fully explicit optimality conditions involving a spherical integral. In the almost sure case, we derive optimality conditions and compare them with a model based on robust constraints with respect to the (compact) support of the given distribution. Funding: The authors thank the Deutsche Forschungsgemeinschaft [Projects B02 and B04 in the “Sonderforschungsbereich/Transregio 154 Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks”] for support. C. Geiersbach acknowledges support from the Deutsche Forschungsgemeinschaft [Germany’s Excellence Strategy–the Berlin Mathematics Research Center MATH+ Grant EXC-2046/1, Project 390685689]. R. Henrion acknowledges support from the Fondation Mathématique Jacques Hadamard [Program Gaspard Monge in Optimization and Operations Research, including support to this program by Electricité de France].

The impact of health inequity on spatial variation of COVID-19 transmission in England.

Considerable spatial heterogeneity has been observed in COVID-19 transmission across administrative areas of England throughout the pandemic. This study investigates what drives these differences. We constructed a probabilistic case count model for 306 administrative areas of England across 95 weeks, fit using a Bayesian evidence synthesis framework. We incorporate the impact of acquired immunity, of spatial exportation of cases, and 16 spatially-varying socio-economic, socio-demographic, health, and mobility variables. Model comparison assesses the relative contributions of these respective mechanisms. We find that spatially-varying and time-varying differences in week-to-week transmission were definitively associated with differences in: time spent at home, variant-of-concern proportion, and adult social care funding. However, model comparison demonstrates that the impact of these terms is negligible compared to the role of spatial exportation between administrative areas. While these results confirm the impact of some, but not all, static measures of spatially-varying inequity in England, our work corroborates the finding that observed differences in disease transmission during the pandemic were predominantly driven by underlying epidemiological factors rather than aggregated metrics of demography and health inequity between areas. Further work is required to assess how health inequity more broadly contributes to these epidemiological factors.

A Review of Fluid Bolus in Critically Ill Patients After Initial Volume Expansion: Bayesian Probability Analysis and Case Studies.

Introduction Fluid resuscitation is a crucial intervention for the management of critically ill patients. However, after initial volume expansion, the advantages of fluid bolus administration remain controversial. Our aim was to investigate the probabilistic reasoning against fluid bolus administration in critically ill patients after initial volume expansion. We then applied this reasoning to two hypothetical case studies that evaluated the benefits and risks associated with a fluid bolus for each patient. Methods We analyzed data from 12 previously published studies, totaling 334 patients, on fluid responsiveness in critically ill patients.Owing to differences in these studies, we used a Monte Carlo simulation based on their parameters to improve our Bayesian prior, generate strong estimates, and address uncertainty. Using the established Bayesian prior for volume responsiveness, we scrutinized two hypothetical case studies employing Bayesian mathematical notation to assess the pre-test probability, posterior probability, and likelihood ratios in patients with septic shock. Results The Monte Carlo simulation yielded a mean response rate of 0.54 (SD = 0.026), suggesting that only approximately 54% of patients were responsive to fluid bolus administration. These results had an effective sample size of 17,204 and an R-hat value of 1, demonstrating the reliability of our results.In our Bayesian case studies, we demonstrate the low probabilities of volume and VO2 responsiveness over time using common bedside testing. Conclusion Our analysis shows that the pretest and posttest probabilities for volume responsiveness following initial fluid resuscitation are low. Additional bedside testing should be pursued before administering additional volume. This approach emphasizes the importance of evidence-based decision-making in the management of critically ill patients to optimize patient outcomes and minimize potential risks.

Efficiency of Stochastic Coordinate Proximal Gradient Methods on Nonseparable Composite Optimization

This paper deals with composite optimization problems having the objective function formed as the sum of two terms; one has a Lipschitz continuous gradient along random subspaces and may be nonconvex, and the second term is simple and differentiable but possibly nonconvex and nonseparable. Under these settings, we design a stochastic coordinate proximal gradient method that takes into account the nonseparable composite form of the objective function. This algorithm achieves scalability by constructing at each iteration a local approximation model of the whole nonseparable objective function along a random subspace with user-determined dimension. We outline efficient techniques for selecting the random subspace, yielding an implementation that has low cost per iteration, also achieving fast convergence rates. We present a probabilistic worst case complexity analysis for our stochastic coordinate proximal gradient method in convex and nonconvex settings; in particular, we prove high-probability bounds on the number of iterations before a given optimality is achieved. Extensive numerical results also confirm the efficiency of our algorithm. Funding: This work was supported by Norway Grants 2014-2021 [Grant ELO-Hyp 24/2020]; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii [Grants PN-III-P4-PCE-2021-0720, L2O-MOC, nr 70/2022]; and the ITN-ETN project TraDE-OPT funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement [Grant 861137].

How general is the natural frequency effect? The case of joint probabilities.

Natural frequencies are known to improve performance in Bayesian reasoning. However, their impact in situations with two binary events has not yet been completely examined, as most researchers in the last 30 years focused only on conditional probabilities. Nevertheless, situations with two binary events consist of 16 elementary probabilities and so we widen the scope and focus on joint probabilities. In this article, we theoretically elaborate on the importance of joint probabilities, for example, in situations like the Linda problem. Furthermore, we implemented a study in a 2×5×2 design with the factors information format (probabilities vs. natural frequencies), visualization type ("Bayesian text" vs. tree diagram vs. double tree diagram vs. net diagram vs. 2×2 table), and context (mammography vs. economics problem). Additionally, all four "joint questions" (i.e., were asked for. The main factor of interest was whether there is a format effect in the five visualization types named above. Surprisingly, the advantage of natural frequencies was not found for joint probabilities and, most strikingly, the format interacted with the visualization type. Specifically, while people's understanding of joint probabilities in a double tree seems to be worse than the understanding of the corresponding natural frequencies (and, thus, the frequency effect holds true), the opposite seems to be true in the 2 × 2 table. Hence, the advantage of natural frequencies compared to probabilities in typical Bayesian tasks cannot be found in the same way when joint probability or frequency tasks are asked.

Standing and Probabilistic Injury

Standing to sue often turns on questions of probability. For example, public law plaintiffs must show that they are likely to be affected by allegedly unlawful government surveillance or environmental policies, and consumers may wish to sue private defendants over false credit reporting or data breaches that may or may not cause them financial or reputational harm in the future. This Article offers a framework for resolving a wide range of these “probabilistic standing” issues. Our core claim is that courts and commentators ask too much of standing doctrine in probabilistic cases. First, scholars sometimes seek a unified theory of probabilistic standing to cover categories of cases that ask distinct questions, such as cases involving who is subject to a challenged action, on the one hand, and those involving whether a person subject to such an action is sufficiently likely to be harmed, on the other. Second, courts should not ask how probable elements of a plaintiff’s case must be in order to support standing, but rather who should decide whether a given probability is sufficient. Judges and parties struggle in litigation to assess the actual probability of occurrences, and Article III of the Constitution provides no standard for how probable an injury must be to support a lawsuit. Third, any doctrinal probability threshold for standing would encounter a related problem, which is that the probability of an injury depends significantly on how that injury is framed. Which harms “count” for standing is thus a vital question in assessing the probability of injury, but Article III is an unlikely place to look for answers. Within certain constraints, courts should look instead to the underlying substantive law to define the relevant injuries for standing purposes. Finally, we contend that many of the concerns associated with probabilistic claims are better addressed through the law of remedies and prudential elements of the timing doctrines (mootness and ripeness) than through the constitutional law of standing.

Statistical Inference Methods for Clinical Medical Data with Missing and Truncated Data

Abstract In clinical medicine, due to some accidents will inevitably produce the situation of missing data, this study for its with missing and truncated data, the use of mathematical statistics methods for inference supplement. After classifying the types of incomplete data, the article utilizes the great likelihood and empirical likelihood to form a linear statistical model to infer such data. It verifies it through simulation experiments and example analysis. In the simulation experiment, for the case of the same missing probability, as the number of samples increases from 150 to 300, the bias, variance, and mean square error of this paper’s algorithm in parameter β 1 are reduced to 0.0122, 0.1435, and 0.1441, respectively. In the actual statistical inference analysis of cardiac disease and heart transplantation, the standard error of this paper’s method reduces by 0.0576 compared with that of CAA, and the inference The results are by the reality. In clinical medicine, this study proposes a practical statistical extrapolation method and a realization path for objective interpretation when incomplete data is present.

Anomalous diffusion limit for a kinetic equation with a thermostatted interface

We consider the limit of solutions of scaled linear kinetic equations with a reflection-transmission-killing condition at the interface. Both the coefficient describing the probability of killing and the scattering kernel degenerate. We prove that the long-time, large-space limit is the unique solution of a version of the fractional in space heat equation that corresponds to the Kolmogorov equation for a symmetric stable process, which is reflected, or transmitted while crossing the interface and is killed upon the first hitting of the interface. The results of the paper are related to the work in Komorowski et al. (Ann Prob 48:2290–2322, 2020), where the case of a non-degenerate probability of killing has been considered.

Nonlinear approximation of functions by sets of finite pseudo-dimension in the probabilistic and average case settings

This paper aims to explore the optimal nonlinear approximation on the class of functions in Sobolev space in the probabilistic and average case settings. Specifically, we investigate the optimal approximation through the utilization of a set with finite pseudo-dimension, measured by the Kolmogorov probabilistic nonlinear [Formula: see text]-width (or say, the Kolmogorov probabilistic pseudo-[Formula: see text]-width). Furthermore, we provide an estimation of the exact asymptotic order of the Kolmogorov probabilistic nonlinear [Formula: see text]-width and [Formula: see text]-average nonlinear [Formula: see text]-width on the class of functions in Sobolev space.

A seminatural approach towards restoration, classification, and pattern extraction from heritage Hindi poetry: A probabilistic case study with Dohā chanda

Abstract Indian literary heritage is vast and is of great importance; to explore it, one has to devote them in studying dialects (Khariboli, Haryanavi, Brajbhasha, Awadhi, Bhojpuri, Marwari, etc.) especially when old Hindi is under the lens of observation. Chanda are poetic compositions that have well-defined structures. Dohā is a kind of chanda which, in our work, has been explored using Kabir’s compositions as a case study. Kabir represents the cult of poets who relied on oral means for the propagation and consumption of poetry. The poems were communicated to the later generation through simple acts of recitation and hearing, leading to obvious mutation and multiple versions of the same compositions when documented later and hence needs restoration (at least metrically). Using the knowledge from the state-of-art models metadata generator, Text2Mātrā, and RPaGen and extending beyond them, this article is first of its kind to present Kabir’s dohā within the scope of restoration, metrical computation, and statistics. Starting with the restoration process, the proposed algorithms generate data that are subjected to suitable statistical models. These models highlight the trends in the dataset, which is helpful in realizing abstract patterns inside the textual data.

Do Mathematics Teachers' Preferences on Teaching Methods and Materials Change in Online Education? Case of Probability

This study examines changes in middle school mathematics teachers’ teaching methods and materials during the transition from face-to-face to online education and difficulties in terms of teaching methods and materials used in this process. 35 middle school mathematics teachers’ lesson plans for both face-to-face and online classrooms were analyzed according to their content, and the reason for the changes in teaching practices of the teachers was evaluated through interviews. While the study revealed that the use of lecture-based and demo & practice methods increased with the transition from face-to-face education to online education, teachers stated that they did not use different teaching methods in the classroom. It has been determined that the situations that limit the use of different teaching methods are the low motivation of the students and the difficulties of the teachers in the classroom management process. In addition, the use of concrete manipulatives has been replaced by videos and other digital teaching tools in online education. Other reflections of compulsory transitions to online education on teaching practice were discussed in terms of teaching the subject of probability and within the framework of in-service teachers' training.

Chest X-ray Findings in Community-acquired Pneumonia

Abstract Background: Community acquired pneumonia (CAP) is an important subject in pulmonary medicine and chest imaging plays a major role in the diagnosis and chest x ray (CXR) historically are the image of choice in the workup. Aims and objectives: to provide the role of chest X-ray (CXR) in diagnosis of pneumonia. Materials and Methods: This study which involves 185 cases of high clinical probability of CAP investigated by CXR and several patterns of infiltrates has been detected mainly lobar pneumonia followed by interstitial infiltrates then lobular and bronchopneumonic pattern. Chest x ray is not only a tool for diagnosis but also can be utilized in the follow up for resolution or development of complications. Results: CXR is a good modality for pneumonia diagnosis in which lobar pattern of infiltrate was the most common type 82 patient (44.3%), followed by interstitial pattern which was 57 patients (30.8%), then bronchopneumonia pattern 21 patient (11.3%) and lobular pattern 20 patient (10.8%). Conclusions and Recommendations: Chest x ray plays a major role in diagnosis of pneumonia, it can provide information about any structural pulmonary diseases , exclude alternative diagnoses and even giving an idea about the possible underlying pathogen and hence the antibiotic that preferred to be use; so we recommend to use CXR as an initial modality of choice in cases of suspected pneumonia.

Pre and Post Earthquake Slope Stability Analysis Using Equivalent Linear Method – a Case Study of Jure Landslide

Slope stability analysis is one of the most important tools to determine the stability of both natural as well as man-made slopes. The main objective of this study is to compare safety factor of the Jure landslide before and after an earthquake occurrence using Geostudio QUAKE/W module. Three different positions of water table (top, middle and bottom of debris) were considered for the study. The safety factors of both deterministic and probabilistic case are determined. The Gorkha Earthquake's time history data were taken into consideration. Both the bedrock and debris are modeled using the equivalent linear approach for dynamic analysis. The findings indicate that the slope was on the verge of failing prior to the earthquake and is predicted to fail following the earthquake event that was taken into account.

#4140 DAPRODUSTAT VERSUS RECOMBINANT HUMAN ERYTHROPOIETIN FOR TREATING ANAEMIA OF CHRONIC KIDNEY DISEASE: COST EFFECTIVENESS MODEL METHODOLOGY AND FINDINGS

Abstract Background and Aims Anaemia is a common manifestation in patients with chronic kidney disease (CKD).1,2 Daprodustat, a hypoxia-inducible factor prolyl hydroxylase inhibitor, offers an alternative to conventional treatment with recombinant human erythropoietins (rhEPOs). This study reports the development of a cost-effectiveness analysis to support reimbursement discussions for daprodustat in Canada. Method A Markov model was developed to conduct a cost-utility analysis to compare expected costs and outcomes associated with daprodustat, vs two rhEPO treatment comparators (darbepoetin alfa [DA] or epoetin alfa [EA]), which represent standard of care. The model used a lifetime time horizon, and a publicly funded Canadian healthcare payer perspective for adults with anaemia of CKD (on/not on dialysis). Clinical inputs for the model were obtained from two Phase 3 clinical trials: ASCEND-D1 (daprodustat vs DA or EA in 2964 dialysis patients) and ASCEND-ND2 (daprodustat vs DA in 3872 non-dialysis patients). Efficacy and adverse event profiles of both rhEPOs were assumed equal. Patients entering the model (Figure) receive daprodustat, DA or EA. The model includes five health states informed by ASCEND-D and ASCEND-ND: non-dialysis, dialysis, kidney transplant, post-transplantation and terminal care. While in the dialysis or non-dialysis health state, costs and utilities are assigned based on three haemoglobin (Hb) levels: <10g/dL, 10–11.5g/dL or >11.5g/dL. A monthly cycle length is utilised and aligned with the ASCEND study visits and observations up to Week 52. Hb concentrations from ASCEND-D and ASCEND-ND are used to estimate proportions of patients in the target Hb range of 10–11.5g/dL, and non-target Hb levels. They were assumed to have different utility values. Patients could transition from non-dialysis to dialysis; in the latter state, patients could die or receive a kidney transplant, and transition to a post-transplantation state. Transition probabilities were derived from ASCEND-D and ASCEND-ND data and the literature. The median follow-up durations in ASCEND-D and ASCEND-ND were 2.5 and 1.9 years, respectively, necessitating extrapolation to the model time horizon; increases over time in the transition probabilities were applied. It is assumed that patients cannot transition back to non-dialysis from the dialysis state. Patients may transition to death from any health state, with mortality risks informed by the ASCEND trials and Canadian all-cause mortality data. Costs in the model included treatment acquisition and administration, cold-chain storage, dialysis, kidney transplant, transfusions of red blood cells and iron, and adverse events (AEs). All costs were sourced from fee schedules (2022 $CAD) or the literature. Utilities for health states and Hb levels were obtained from the ASCEND trials; AE-associated utility decrements could not be estimated from ASCEND data due to low event rates – hence, disutilities were sourced from the literature. Results In the probabilistic reference case analysis, daprodustat was less costly (comparison with DA: −$8763; EA:−$13,864) and produced more quality-adjusted life years (DA: +0.012; EA: +0.018; Table). The incremental savings with daprodustat versus DA and EA were −$860 and −$1033, respectively, in the non-dialysis state, and −$7904 and −$12,831 in the dialysis state. Contributors to the cost savings with daprodustat include an absence of cold-chain storage costs and reduced administration costs. Conclusion This study indicates that daprodustat is less costly and originates more QALYs than DA and EA in the treatment of anaemia due to CKD. In this setting, daprodustat may provide cost savings from a Canadian public payer perspective. The model described here could be adapted to other perspectives, including European countries.

Bayesian decision making under soft probabilities

Bayesian decision models use probability theory as a commonly technique to handling uncertainty and arise in a variety of important practical applications for estimation and prediction as well as offering decision support. But the deficiencies mainly manifest in the two aspects: First, it is often difficult to avoid subjective judgment in the process of quantization of priori probabilities. Second, applying point-valued probabilities in Bayesian decision making is insufficient to capture non-stochastically stable information. Soft set theory as an emerging mathematical tool for dealing with uncertainty has yielded fruitful results. One of the key concepts involved in the theory named soft probability which is as an immediate measurement over a statistical base can be capable of dealing with various types of stochastic phenomena including not stochastically stable phenomena, has been recently introduced to represent statistical characteristics of a given sample in a more natural and direct manner. Motivated by the work, this paper proposes a hybrid methodology that integrates soft probability and Bayesian decision theory to provide decision support when stochastically stable samples and exact values of probabilities are not available. According to the fact that soft probability is as a special case of interval probability which is mathematically proved in the paper, thus the proposed methodology is thereby consistent with Bayesian decision model with interval probability. In order to demonstrate the proof of concept, the proposed methodology has been applied to a numerical case study regarding medical diagnosis.

Winning a CHSH Game without Entangled Particles in a Finite Number of Biased Rounds: How Much Luck Is Needed?

Quantum games, such as the CHSH game, are used to illustrate the puzzle and power of entanglement. These games are played over many rounds and in each round, the participants, Alice and Bob, each receive a question bit to which they each have to give an answer bit, without being able to communicate during the game. When all possible classical answering strategies are analyzed, it is found that Alice and Bob cannot win more than 75% of the rounds. A higher percentage of wins arguably requires an exploitable bias in the random generation of the question bits or access to "non-local" resources, such as entangled pairs of particles. However, in an actual game, the number of rounds has to be finite and question regimes may come up with unequal likelihood, so there is always a possibility that Alice and Bob win by pure luck. This statistical possibility has to be transparently analyzed for practical applications such as the detection of eavesdropping in quantum communication. Similarly, when Bell tests are used in macroscopic situations to investigate the connection strength between system components and the validity of proposed causal models, the available data are limited and the possible combinations of question bits (measurement settings) may not be controlled to occur with equal likelihood. In the present work, we give a fully self-contained proof for a bound on the probability to win a CHSH game by pure luck without making the usual assumption of only small biases in the random number generators. We also show bounds for the case of unequal probabilities based on results from McDiarmid and Combes and numerically illustrate certain exploitable biases.

An Automated Framework for Detecting Change in the Source Code and Test Case Change Recommendation

Improvements and acceleration in software development have contributed towards high-quality services in all domains and all fields of industry, causing increasing demands for high-quality software developments. The industry is adopting human resources with high skills, advanced methodologies, and technologies to match the high-quality software development demands to accelerate the development life cycle. In the software development life cycle, one of the biggest challenges is the change management between the version of the source codes. Various reasons, such as changing the requirements or adapting available updates or technological upgrades, can cause the source code's version. The change management affects the correctness of the software service's release and the number of test cases. It is often observed that the development life cycle is delayed due to a lack of proper version control and due to repetitive testing iterations. Hence the demand for better version control-driven test case reduction methods cannot be ignored. The parallel research attempts propose several version control mechanisms. Nevertheless, most version controls are criticized for not contributing toward the test case generation of reduction. Henceforth, this work proposes a novel probabilistic rule-based test case reduction method to simplify the software development's testing and version control mechanism. Software developers highly adopt the refactoring process for making efficient changes such as code structure and functionality or applying changes in the requirements. This work demonstrates very high accuracy for change detection and management. This results in higher accuracy for test case reductions. The outcome of this work is to reduce the development time for the software to make the software development industry a better and more efficient world.

Identifying and testing the probability distribution of earthfill dam breach parameters for probabilistic dam breach modeling

AbstractThe main goal of this study is to identify the probability distribution of earthfill dam breach parameters using a dam failures database (3861 observations). This work looks to fill the lack of studies observed, using an extensive database instead of empirical models, plausible values, or small databases. It is, expected that this will contribute to the development of probabilistic dam breach inundation maps instead of deterministic mapping. Four parameters were selected: (i) side slope, (ii) height, (iii) width, and (iv) formation time. To identify the best‐fit distributions, a correlation analysis was performed and a comparative analysis was undertaken by groups related to dam and failure properties such as dam type, material erodibility, magnitude, failure mode, and others. These steps defined samples with 33–311 observations. The results demonstrate that dam breach parameters might be considered independent of each other and are affected by factors such as side slope by failure mode and bottom width by dam shape. The probabilistic case study indicated that deterministic dam breach models predominantly produce conservative results of breach peak outflow.

The Epimetheus Project: Searching for new authentic ‘contactees’ and the scientific information received from Extraterrestrial Intelligences

The recent American congressional hearing was carried out after the Pentagon admitted the authenticity of encounters of UFOs (Unidentified Flying Objects) with jets and released an unclassified version of a report investigating several cases. Previously in the Final Contact Project (FCP) investigating the probability of the authenticity of a series of alleged ‘contactees’ in Close Encounters of the Fifth Kind (CE5thK) and using an original approach, we found evidence to support that in some CE5thK advanced contact with Extraterrestrial Intelligences (ETI) with human beings is probably happening. In this study, the Epimetheus Project (EP), with a new team of researchers and using the same twelve-criteria method as the prior FCP, investigated a new series of 66 alleged CE5thK from the world, compared the results to the previous FCP, and present original variables from both studies. In the EP, we found 52 cases to have a low probability of authenticity, one case to be inconclusive evidence, and 13 cases having a high probability of being an authentic phenomenon. In comparison to FCP, the EP study had a significantly lower rate of cases of high probability of being authentic (19.7% vs. 34.755 respectively, p=0.048). In addition, we discuss the meaning of these results, and the information received in these CE5thK cases of high probability of being authentic, also including original information from both studies, such as historical characters cited, existence of intraterrestrial life, parallel worlds, and time travel. Our study confirmed that advanced contact from ETI with human beings on the Earth is occurring. The richness of new technical and scientific information received by authentic ‘contactees’ could represent a means of ‘technology’ transfer, with revolutionary potential for our society to develop and apply these benefits to the critical problems that risk mankind’s extinction.