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In recent years, brain tumors have become one of the most common fatal diseases. Despite the existence of an important number of research studies on tumors, the proportion of research on predicting the growth of tumors remains insufficient due to the intricate nature of this research domain. Therefore, the presence of any application able to predict the growth of the tumor may have a role in eliminating the tumor by finding the appropriate treatment for it before it grows. This paper investigates tumor growth and presents a technique for tumor growth prediction based on the Discrete Time Markov Chain (DTMC) and K-Nearest Neighbor (KNN) algorithms. The design and development of this technique consists of a proposition of a stochastic model of tumor progression. This is followed by an extension of the mode to several cases that allow the derivation of new cases based on the study of predictive probabilities. The aim of this paper is to develop a model based on the KNN and DTMC algorithms that can classify tumors and predict the future state based on the current state of the tumor without the knowledge of the past state. In other words, all relevant information about the past and the present that would be useful in making predictions is available in the current state. In terms of performance evaluation metrics, the results show that the proposed method exceeds the existing methods with 97.65% accuracy, 71.65% specificity and 99.087% sensitivity. [ABSTRACT FROM AUTHOR]

A major hypothesis about conditionals is the Equation in which the probability of a conditional equals the corresponding conditional probability: p(if A then C) = p(C|A). Probabilistic theories often treat it as axiomatic, whereas it follows from the meanings of conditionals in the theory of mental models. In this theory, intuitive models (system 1) do not represent what is false, and so produce errors in estimates of p(if A then C), yielding instead p(A & C). Deliberative models (system 2) are normative, and yield the proportion of cases of A in which C holds, i.e., the Equation. Intuitive estimates of the probability of a conditional about unique events: If covid-19 disappears in the USA, then Biden will run for a second term, together with those of each of its clauses, are liable to yield joint probability distributions that sum to over 100%. The error, which is inconsistent with the probability calculus, is massive when participants estimate the joint probabilities of conditionals with each of the different possibilities to which they refer. This result and others under review corroborate the model theory. [ABSTRACT FROM AUTHOR]

The mental model theory postulates that the meanings of conditionals are based on possibilities. Indicative conditionals—such as "If he is injured tomorrow, then he will take some leave"—have a factual interpretation that can be paraphrased as It is possible, and remains so, that he is injured tomorrow, and in that case certain that he takes some leave. Subjunctive conditionals, such as, "If he were injured tomorrow, then he would take some leave," have a prefactual interpretation that has the same paraphrase. But when context makes clear that his injury will not occur, the subjunctive has a counterfactual paraphrase, with the first clause: It was once possible, but does not remain so, that he will be injured tomorrow. Three experiments corroborated these predictions for participants' selections of paraphrases in their native Spanish, for epistemic and deontic conditionals, for those referring to past and to future events, and for those with then clauses referring to what may or must happen. These results are contrary to normal modal logics. They are also contrary to theories based on probabilities, which are inapplicable to deontic conditionals, such as, "If you have a ticket, then you must enter the show." [ABSTRACT FROM AUTHOR]

We studied the effects of probabilistic cues, i.e., of information of limited certainty, in the context of an action task (GL: grip-lift) and of a perceptual task (WP: weight perception). Normal subjects (n = 22) saw four different probabilistic visual cues, each of which announced the likely weight of an object. In the GL task, the object was grasped and lifted with a pinch grip, and the peak force rates indicated that the grip and load forces were scaled predictively according to the probabilistic information. The WP task provided the expected heaviness related to each probabilistic cue; the participants gradually adjusted the object's weight until its heaviness matched the expected weight for a given cue. Subjects were randomly assigned to two groups: one started with the GL task and the other one with the WP task. The four different probabilistic cues influenced weight adjustments in the WP task and peak force rates in the GL task in a similar manner. The interpretation and utilization of the probabilistic information was critically influenced by the initial task. Participants who started with the WP task classified the four probabilistic cues into four distinct categories and applied these categories to the subsequent GL task. On the other side, participants who started with the GL task applied three distinct categories to the four cues and retained this classification in the following WP task. The initial strategy, once established, determined the way how the probabilistic information was interpreted and implemented. [ABSTRACT FROM AUTHOR]

Single-value failure temperatures for fire loss of electrical cable functionality have been the norm for Fire Probabilistic Risk Assessments since the publication in 2005 of NUREG/CR-6850. If the calculated exposure temperature matches or exceeds the cable failure temperature, electrical failure is always assumed; if not, no failure is assumed. While this can be relaxed somewhat if a distribution for the exposure temperature is estimated, use of a distribution on the cable failure temperature itself more readily enables such relaxation and, therefore, a more realistic assessment. This paper develops probability distributions for different generic cable types (based on insulation) using data from the US Nuclear Regulatory Commission tests. Results indicate mean failure temperatures considerably higher than those used deterministically, 252°C, 421°C and 383°C, respectively for thermoplastic, thermoset and Kerite-FR. This suggests considerable relaxation from the conservatism inherent using the deterministic failure temperatures could be achieved. The paper then postulates two hypothetical distributions on the exposure temperature from applying a fire phenomenological model in a statistical way to estimate the possible relaxation using the distributed cable failure temperatures to enhance the realism of the assessment. Examples show that use of probabilistically-distributed cable failure temperatures (in conjunction with similar for exposure temperatures) can reduce the probability of electrical failure for a normally-distributed exposure temperature with a mean of 350°C and standard deviation of 58.3°C by factors of approximately three and eight for Kerite-FR and thermoset cables, respectively. The reduction would be less pronounced for thermoplastic cables, although larger reductions would be possible here as well for lower exposure temperatures (e.g., a factor of two). [ABSTRACT FROM AUTHOR]

According to all-luck egalitarianism, the differential distributive effects of both brute luck, which defines the outcome of risks which are not deliberately taken, and option luck, which defines the outcome of deliberate gambles, are unjust. Exactly how to correct the effects of option luck is, however, a complex issue. This article argues that (a) option luck should be neutralized not just by correcting luck among gamblers, but among the community as a whole, because it would be unfair for gamblers as a group to be disadvantaged relative to non-gamblers by bad option luck; (b) individuals should receive the warranted expected results of their gambles, except insofar as individuals blamelessly lacked the ability to ascertain which expectations were warranted; and (c) where societal resources are insufficient to deliver expected results to gamblers, gamblers should receive a lesser distributive share which is in proportion to the expected results. Where all-luck egalitarianism is understood in this way, it allows risk-takers to impose externalities on non-risk-takers, which seems counterintuitive. This may, however, be an advantage as it provides a luck egalitarian rationale for assisting 'negligent victims'. [ABSTRACT FROM AUTHOR]

The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so they are incomplete. Second, it will be argues that when we attempt to incorporate the knowledge of such causal connections into Bayesian decision theory, a substantial technical problem arises for which there is no currently available solution that does not suffer from some damning objection or other. From a broader perspective, this then throws into question the use of decision theory as a model of human or machine planning. [ABSTRACT FROM AUTHOR]

Recent functional neuroimaging and lesion studies demonstrate the involvement of the orbitofrontal/ventromedial prefrontal cortex as a key structure in decision making processes. This region seems to be particularly crucial when contingencies between options and consequences are unknown but have to be learned by the use of feedback following previous decisions (decision making under ambiguity). However, little is known about the neural correlates of decision making under risk conditions in which information about probabilities and potential outcomes is given. In the present study, we used functional magnetic resonance imaging to measure blood-oxygenation-level-dependent (BOLD) responses in 12 subjects during a decision making task. This task provided explicit information about probabilities and associated potential incentives. The responses were compared to BOLD signals in a control condition without information about incentives. In contrast to previous decision making studies, we completely removed the outcome phase following a decision to exclude the potential influence of feedback previously received on current decisions. The results indicate that the integration of information about probabilities and incentives leads to activations within the dorsolateral prefrontal cortex, the posterior parietal lobe, the anterior cingulate and the right lingual gyrus. We assume that this pattern of activation is due to the involvement of executive functions, conflict detection mechanisms and arithmetic operations during the deliberation phase of decisional processes that are based on explicit information. [ABSTRACT FROM AUTHOR]

In his paper, “Should the Numbers Count?" John Taurek imagines that we are in a position such that we can either save a group of five people, or we can save one individual, David. We cannot save David and the five. This is because they each require a life-saving drug. However, David needs all of the drug if he is to survive, while the other five need only a fifth each. Typically, people have argued as if there was a choice to be made: either numbers matter, in which case we should save the greater number, or numbers don't matter, but rather there is moral value in giving each person an equal chance of survival, and therefore we should toss a coin. My claim is that we do not have to make a choice in this way. Rather, numbers do matter, but it doesn't follow that we should always save the greater number. And likewise, there is moral value in giving each person an equal chance of survival, but it doesn't follow that we should always toss a coin. In addition, I argue that a similar approach can be applied to situations in which we can save one person or another, but the chances of success are different. [ABSTRACT FROM AUTHOR]

Personalized medicine promises to represent a transformation in clinical care that will be ushered in by the unprecedented growth and development in the field of human genetics. Further examination of the scientific foundations of this new hope reveals a great number of challenges that lie ahead. While basic science research feverishly races to produce solutions, we continue to wait for the translation of deliverables. Products that have and will come to market may leave our clinical communities and systems exposed and unprepared. At each step, from basic science research to infrastructure development, a great deal of creativity and investment are required before the arsenal of more personalized tools can be assimilated into our current models of health care. This commentary seeks to share perspectives on the current status of personalized medicine from the vantage point of several potential investors, and integrate them into a common set of goals and understanding. We conclude that the stylized model of personalized medicine is more akin to a marketing tool than a literal prediction of the future. [ABSTRACT FROM AUTHOR]