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Previous studies have developed plenty of mathematical models for assessing indoor environment quality. Literature reviews that most of them presented a mean value of perception of a population and ignored the interaction effects of multiple environmental factors on occupants' perceptions. In this regard, the aim of our paper is to develop a comprehensive indoor physical environment quality (IPEQ) index which could present a whole picture of perception of a population by taking the combined effects (i.e., main, crossed and interaction effects) into consideration. The hypothesis and methodology of quantitation were firstly proposed according to Probability Theory. Our quantitation was performed based on the subjective perception votes that collected from 216 experimental conditions in a controlled laboratory. Three 3-input 7-output Artificial Neural Network models were subsequently developed for predicting probability mass functions (PMFs) of 7-point sensation votes under the combined effects of temperature, illuminance, and sound level. Thereafter, the PMFs of 3-point satisfaction votes were expressed and calculated by using Law of Total Probability. Finally, a comprehensive IPEQ index-Predicted Probability Overall Dissatisfied (PPOD) index, which provides an estimate of how many occupants in a space would feel dissatisfied with the overall physical environment, was developed. The predicted expected value of overall satisfaction distribution (average overall satisfaction) was validated to be roughly in accordance with previous studies and more reasonable when at least one kind of environmental factor was far away from expectation. The new quantitative measure also indicated that the minimum PPOD is 17.55% (air temperature = 24.3°C, illuminance = 910Lx, sound level = 35 dB) within common ranges of environmental parameters. It was also discussed that our new quantitative measure could provide quick and direct suggestions for controlling or designing the indoor environment., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions. To address this problem, models based on quantum probability theory, such as the quantum-like Bayesian networks, have been proposed. However, this model makes use of a Bayes normalisation factor during probabilistic inference to convert the likelihoods that result from quantum interference effects into probability values. The interpretation of this operation is not clear and leads to extremely skewed intensity waves that make the task of prediction of these irrational decisions challenging. This article proposes the law of balance, a novel mathematical formalism for probabilistic inferences in quantum-like Bayesian networks, based on the notion of balanced intensity waves. The general idea is to balance the intensity waves resulting from quantum interference in such a way that, during Bayes normalisation, they cancel each other. With this representation, we also propose the law of maximum uncertainty, which is a method to predict these paradoxes by selecting the amplitudes of the wave with the highest entropy. Empirical results show that the law of balance together with the law of maximum uncertainty were able to accurately predict different experiments from cognitive psychology showing paradoxical or irrational decisions, namely in the Prisoner's Dilemma game and the Two-Stage Gambling Game.

Purpose: To compare acute kidney injury (AKI) incidence between nephron sparing surgery (NSS) and microwave ablation (MWA) for T1a RCC patients, reveal the effect of AKI on survival prognosis, construct AKI nomogram and use Law of Total Probability for survival probability (SP) prediction. Materials and methods: Patients were studied retrospectively after NSS ( n  = 1267) or MWA ( n  = 210) from January 1, 2011 to June 30, 2017. Using one to one Propensity Score Matching (PSM), 158 pairs of patients were identified for the cohort study. AKI incidence, risk factors and impact on survival outcomes were analyzed using Chi-square test, logistic and cox regression analysis. AKI risk and SP were predicted by nomogram and Law of Total Probability. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical usefulness. Results: AKI occurred more commonly in NSS (27.85%) cohort, when compared to MWA (17.72%) cohort ( p  = 0.032), but treatment modality was not independently predictive of AKI occurrence (odds ratio [OR]: 0.598; 95% confidence interval [CI]: 0.282-1.265; p  = 0.178). The 5-yr overall survival (OS) was lower in AKI patients (73.5%) compared with non-AKI patients (94.8%; p  < 0.001). AKI was an independent risk factor for all-cause mortality in RCC patients (hazard ratio [HR]: 2.820; 95% confidence interval [CI]: 1.110-7.165; p  = 0.029). Predictors for both NSS- and MWA-related AKI included tumor diameter, baseline eGFR and CCI score. RENAL score and tumor blood supply can predict AKI after NSS and MWA, respectively. The AKI normograms demonstrated good discrimination, with AUCs >0.86, excellent calibration and net benefits at the decision curve analysis with probabilities ≥5%. SP predicted by Law of Total Probability was comparable to actual OS. Conclusion: AKI was an early indicator for poor overall survival in RCC patients. It can be predicted by several oncological parameters. Nomogram and Law of Total Probability can accurately predict AKI risk and SP.

Many decision making tasks in life involve a categorization process, but the effects of categorization on subsequent decision making has rarely been studied. This issue was explored in three experiments (N=721), in which participants were shown a face stimulus on each trial and performed variations of categorization-decision tasks. On C-D trials, they categorized the stimulus and then made an action decision; on X-D trials, they were told the category and then made an action decision; on D-alone trials, they only made an action decision. An interference effect emerged in some of the conditions, such that the probability of an action on the D-alone trials (i.e., when there was no explicit categorization before the decision) differed from the total probability of the same action on the C-D or X-D trials (i.e., when there was explicit categorization before the decision). Interference effects are important because they indicate a violation of the classical law of total probability, which is assumed by many cognitive models. Across all three experiments, a complex pattern of interference effects systematically occurred for different types of stimuli and for different types of categorization-decision tasks. These interference effects present a challenge for traditional cognitive models, such as Markov and signal detection models, but a quantum cognition model, called the belief-action entanglement (BAE) model, predicted that these results could occur. The BAE model employs the quantum principles of superposition and entanglement to explain the psychological mechanisms underlying the puzzling interference effects. The model can be applied to many important and practical categorization-decision situations in life., (Copyright © 2016 Elsevier B.V. All rights reserved.)