The concept of resilience and some evaluation bias may have been deleterious for the development of the PTG concept in the French-speaking world. There is a need to consolidate data to understand the pathway leading to PTG, noticeably to identify factors contributing to PTG that can help to promote the growth as a new therapy for trauma.The concept of resilience and some evaluation bias may have been deleterious for the development of the PTG concept in the French-speaking world. There is a need to consolidate data to understand the pathway leading to PTG, noticeably to identify factors contributing to PTG that can help to promote the growth as a new therapy for trauma.Computational modeling builds mathematical models of cognitive phenomena to simulate patterns of perception, decision-making, and belief updating. These models mathematically represent the information processing by combining an anterior probability distribution, a likelihood function and a set of parameters and hyperparameters. Their use popularized the conception of a nervous system functioning as a predictive machine, or "bayesian brain". Applied to psychiatry, these models seek to explain how psychiatric dysfunction may emerge mechanistically. Despite the significance of emotions for cognitive phenomena and for psychiatric disorders, few computational models offer mathematical representations of emotion or incorporate emotional factors into their modeling parameters. We present here some computational hypotheses for the modeling of affective parameters, and we suggest that computational psychiatry would benefit from these modeling parameters. Atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) are very common cardiac arrhythmias and are driven by localized sources that can be ablation targets. Peficitinib clinical trial Non-invasive body surface potential mapping (BSPM) can be useful for early diagnosis and ablation planning. We aimed to characterize and differentiate the arrhythmic mechanisms behind AT, AFL and AF from the BSPM perspective using basic features reflecting their electrophysiology. 19 simulations of 567-lead BSPMs were used to obtain dominant frequency (DF) maps and estimate the atrial driving frequencies using the highest DF (HDF). Regions with |DF-HDF|≤1Hz were segmented and characterized (size, area); the spatial distribution of the differences |DF-atrialHDFestimate| was qualitatively analyzed. Phase singularity points (SPs) were detected on maps generated with Hilbert transform after band-pass filtering around the HDF (±1Hz). Connected SPs along time (filaments) and their histogram (heatmaps) were used for rotational activity characterization the arrhythmias and their mechanisms' location achieved balanced accuracy of 72.0% and 73.9%, respectively. Non-invasive characterization of AT, AFL and AF based on realistic models highlights intrinsic differences between the arrhythmias, enhancing the BSPM utility as an auxiliary clinical tool.Non-invasive characterization of AT, AFL and AF based on realistic models highlights intrinsic differences between the arrhythmias, enhancing the BSPM utility as an auxiliary clinical tool.The state-dependent Riccati equation (SDRE) method is an efficient approach to solve nonlinear optimal control problems (OCPs), but nonlinear necessary conditions for the first-order optimality are seldom met in the SDRE approach. In this paper, a state-dependent indirect pseudospectral (SDIP) technique is developed to design nonlinear optimal controllers. To preserve the nonlinearity of the system and reduce the computational cost as well, the state-dependent coefficient (SDC) parameterization technique is employed. Then the optimality conditions are derived under input and state constraints, and spectral methods are used to discretize the optimality conditions into a series of mixed linear complementarity problems (MLCPs). The developed SDIP method is able to handle the finite and infinite-horizon nonlinear OCPs in a unified framework. Numerical comparisons also verify the performance of the developed SDIP method.Uncertainties in the plant model parameters and perturbations in the controller gains imposed by implementation errors represent a challenge to ensure robust stability and controller non-fragility simultaneously. Optimal design of robust non-fragile proportional-integral-derivative (PID) controller is presented for an automatic voltage regulator (AVR). The PID design relies basically on Kharitonov theorem and optimization by future search algorithm (FSA). The proposed algorithm has low computational complexity and fast convergence rate because it utilizes both local and global search methods. Further, FSA can improve the exploration characteristic and prevent trapping in local optima by updating its random initial. The PID controller is optimized by FSA to cope with expected parametric uncertainties of the plant model and tolerate its gain perturbations such that robust stability and controller non-fragility are simultaneously met. An interval plant model is suggested to account for model uncertainties where only eight extreme plants derived by Kharitonov theorem are considered in design. FSA-based PID optimization is constrained by the stability conditions of Kharitonov's plants derived using Routh-Hurwitz. A new figure-of-demerit (FoD) based performance index is suggested to enforce simultaneous minimization of the time domain specifications. The suggested objective function is represented by a weighted sum of FoD of nominal response and the sum of reciprocals of the perturbation radii of PID gains. The output results of the recommended design are compared to that of artificial bee colony (ABC) algorithm and teaching-learning based optimization (TLBO) algorithm, multi-objective extremal optimization (MOEO), and non-dominated sorting genetic algorithm II (NSGA II). The results can confirm better response of the suggested technique measured up to other techniques where robustness and non-fragility are simultaneously ensured. Timely surgery has been shown to impact outcome in endometrial cancer patients. Social determinants of health (SDH) are associated with adverse cancer outcomes. We sought to evaluate the association of SDH with surgical treatment indicators in endometrial cancer patients in a public healthcare system. Endometrial cancer patients in Ontario, Canada, diagnosed between 2009 and 2017 were identified, and clinical, social and demographic variables were extracted from administrative databases. Validated community marginalization scores that include material deprivation, residential instability and ethnic concentration were used for stratification. Surgical treatment features were compared across marginalization quintiles using chi-square, Fischer exact or Wilcoxon rank sum tests as appropriate. Predictors of timely surgical treatment were evaluated with logistic regression. 20228 patients were identified of whom 14,423 had primary hysterectomy for a preoperative diagnosis of endometrial cancer. Fewer patients in marginalized communities received surgery (89% vs.