Transition Probabilities Research Articles

Cost-effectiveness and budget impact analysis of screening and preventive interventions for cardiovascular disease in Myanmar: an economic modelling study

Cardiovascular diseases (CVD) remains a leading cause of mortality in Myanmar. Despite the burden, CVD preventive services receive low government and donor budgets, which has led to poor CVD outcomes. We conducted a cost-effective analysis and a budget impact analysis on CVD prevention strategies recommended by the WHO. A Markov model was used to analyse the cost and quality-adjusted life year (QALY) from healthcare provider and societal perspectives. We calculated transition probabilities from WHO CVD risk data and obtained treatment effects and costs from secondary sources. Subgroup analysis was performed on different sex and age groups. We framed the budget impact analysis from a healthcare provider perspective to assess the affordability of providing CVD preventive care. The most cost-effective strategy from the healthcare provider perspective varied. The combination of screening, primary prevention, and secondary prevention (Sc-PP-SP) (incremental cost-effectiveness ratio [ICER]: US$1574/QALY) is most cost-effective at the three times gross domestic product (GDP) per capita threshold, while at one time the GDP per capita threshold, secondary prevention is the most cost-effective strategy (ICER: US$160/QALY). Sc-PP-SP is the most cost-effective strategy from the societal perspective (ICER: US$647/QALY). Among age groups, intervention at age 45 years appeared to be the most cost-effective option for both men and women. The budget impact revealed the Sc-PP-SP would avert 55,000 acute CVD events and 28,000 CVD-related deaths with a cost of US$157 million for the first year of CVD preventive care. A combination of screening, primary prevention, and secondary prevention is cost-effective to reduce CVD-related deaths in Myanmar. This study provides evidence for the government and development partners to increase investment in and support for CVD prevention. These findings not only provide a basis for efficient resource allocation but also underscore the importance of adopting a total cardiovascular risk approach to CVD prevention, in alignment with global health goals. Pilot grant from Duke Global Health Institute, USA.

Quantum simulation of the pseudo-Hermitian Landau-Zener-Stückelberg-Majorana effect

While the Hamiltonians used in standard quantum mechanics are Hermitian, it is also possible to extend the theory to non-Hermitian Hamiltonians. Particularly interesting are non-Hermitian Hamiltonians satisfying parity–time (PT) symmetry, or more generally pseudo-Hermiticity, since such non-Hermitian Hamiltonians can still exhibit real eigenvalues. In this article, we present a quantum simulation of the time-dependent non-Hermitian non-PT-symmetric Hamiltonian used in a pseudo-Hermitian extension of the Landau–Zener–Stückelberg–Majorana (LZSM) model. The simulation is implemented on a superconducting processor by using Naimark dilation to transform a non-Hermitian Hamiltonian for one qubit into a Hermitian Hamiltonian for a qubit and an ancilla; postselection on the ancilla state ensures that the qubit undergoes nonunitary time evolution corresponding to the original non-Hermitian Hamiltonian. We observe properties such as the dependence of transition probabilities on time and the replacement of conservation of total probability by other dynamical invariants in agreement with predictions based on a theoretical treatment of the pseudo-Hermitian LZSM system. Published by the American Physical Society 2024

Sideband Vibro-Acoustics Suppression and Numerical Prediction of Permanent Magnet Synchronous Motor Based on Markov Chain Random Carrier Frequency Modulation

This paper presents a Markov chain random carrier frequency modulation (MRCFM) technique for suppressing sideband vibro-acoustic responses caused by discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motors (PMSMs) for new energy vehicles. Firstly, the spectral and order distributions of the sideband current harmonics and radial electromagnetic forces introduced by DPWM are characterized and identified. Then, the principle and implementation method of three-state Markov chain random number generation are proposed, and particle swarm optimization (PSO) algorithm is chosen to quickly find the key parameters of transition probability and random gain. A Simulink and JMAG multi-physics field co-simulation model is built to simulate and predict the suppression effect of the MRCFM method on the sideband vibro-acoustic response. Finally, a 12-slot-10-pole PMSM test platform is built for experimental testing. The results show that the sideband current harmonics and vibro-acoustic response are effectively suppressed after the optimization of Markov chain algorithm. The constructed multi-physics field co-simulation model can accurately predict the amplitude characteristics of the sideband current harmonics and vibro-acoustic response.

Transition probabilities and suitable coupling scheme for 6p→6s transition array of neutral xenon using hollow cathode discharge lamp technique

In the present work, analyses have been performed to determine spectroscopic parameters, including transition probabilities, and relative line strengths of discharge-produced xenon plasma. We present the transition probabilities of twenty-three emission lines originating from the 5p56p →5p56s transition array of neutral xenon covering the wavelength range from 450 to 1100 nm using a xenon-filled hollow cathode discharge lamp. The experimental absolute transition probabilities have been measured by combining the reported lifetimes of upper levels with the integrated spectral intensities of xenon transitions. The results agree well with the reported data in the NIST database, where available. The theoretical line strengths for all spectral transitions originating from the 5p56p→5p56s transition array have been calculated in the LS, LK, jj, and jck coupling schemes using the 3j, 6j, and 9j symbol relations. Furthermore, the normalized experimental line strengths have also been measured and compared with theoretical line strengths, which confirm that the jj and jck coupling scheme is more appropriate for the level designation of xenon.

A comprehensive study of ferroelectric, dielectric and optical properties of europium-doped SBD metal- organic framework

The synthesis of Europium-doped Strontium based benzene dicarboxylate dimethylformamide metal–organic framework- Eu([Sr(μ-BDC)(DMF)])- with short form as Eu:SBD@MOF was carried out using the solvothermal method. The investigation involved characterization techniques such as X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), CHN analysis, and energy-dispersive X-ray (EDX) elemental analysis. The results confirmed the formation of metal–organic crystals in the framework. The XRD characterization shows good alignment with the simulated pattern of SBD. The experimental absorption spectrum displays distinct transitions of the Eu (III) ion alongside transitions of the ligands. Calculations of line strengths for all observed transitions have been conducted. The Judd-Ofelt theory has been employed to determine intensity parameters, transition probabilities, branching ratios, and radiative lifetimes for the Eu: SBD@MOF. These JO parameters further aid in calculating various excited state properties. When excited by ultraviolet and visible photons, the complex exhibits emission in the red region respectively. The crystal’s dielectric loss and dielectric constant were investigated for their frequency dependence. It was observed that by virtue of DMF, a molecule linked to the crystal, a synthesized MOF exhibits a good dielectric characteristic and when the temperature is raised to 558 K, the dielectric constant drops sharply as DMF molecule could not stand after this temperature. Further ferroelectric studies reveal that the Eu: SBD@MOF has maximum polarization of 0.27 C/cm2 which is greater than Rochelle salt and other MOF’s. Exploring the broadband dielectric response of metal–organic frameworks (MOFs) open up new avenues in research, potentially leading to innovative applications in smart optoelectronics, terahertz sensors, high-speed telecommunications, and microelectronics.

Asynchronous Event-Triggered Control for Polynomial Fuzzy-Model-Based Markov Jump Systems With Complex Transition Probabilities

Asynchronous Event-Triggered Control for Polynomial Fuzzy-Model-Based Markov Jump Systems With Complex Transition Probabilities

Modeling the Effect of Sulfur Composition in Dispersed Systems Involving Organosulfur Compounds

Background:: Organosulfur compounds within petroleum have far-reaching consequences for the refining industry, combustion of petroleum products, and environmental quality. They induce corrosion in refining equipment, hamper the efficient burning of petroleum products, and contribute to environmental degradation. In high-density asphalt crudes, these compounds are predominantly concentrated within asphaltenes. However, crude oils with extremely high sulfur content, may be distributed across the four constituent families defined by the SARA analysis of crude oil composition. These compounds, characterized by differing polarities, can trigger the formation of a dispersed phase, whose destabilization results in tube clogging issues. Methods:: The research problem focuses on understanding how sulfur composition affects the formation of a dispersed phase in low-polarity organic dispersion media for sulfur-containing hydrocarbons. It is investigated because the presence of sulfur in crude oil significantly affects the behavior of dispersed phases, which can result in operational and environmental quality issues to comprehensively assess the impact of sulfur composition on the dynamics and stability of this dispersed phase, we introduce a mesoscopic model based on the master equation. This model considers the molecular structure of system components and their molecular properties, established through computational quantum chemistry and statistical thermodynamics tools Results:: While our research focuses on a two-phase system, our theoretical insights suggest that increased sulfur content escalates the likelihood of destabilizing the dispersed phase. This adverse effect can be mitigated by incorporating additives capable of reducing the polarizability of the dispersion medium. The novelty lies in the development of a stochastic model to predict the dynamics of dispersed phase formation in sulfur-containing hydrocarbons. This model considers molecular interactions and stochastic processes, offering insights into the influence of sulfur composition on phase behavior. Conclusion:: A stochastic model, based on molecular structure, predicts accelerated formation with increased sulfur concentration, reaching non-equilibrium steady states. Limitations include ad hoc transition probabilities and the exclusion of factors like density and viscosity. Real crudes, with complex compositions, may exhibit different behavior. The presence of sulfur in the dispersion medium enhances the stability of the dispersed system. Our work sheds light on the intricate interplay between sulfur content and the performance of petroleum systems, offering potential solutions to mitigate these issues. Quantitative results include accelerated dispersed phase formation with increased sulfur concentration. Qualitatively, molecular interactions and stochastic processes were explored, highlighting sulfur's impact on phase dynamics.

Transition times across the HIV care continuum in Spain from 2005 to 2022: a longitudinal cohort study

Ending AIDS by 2030 requires improvements across all stages of the HIV care continuum. We used a longitudinal approach to assess changes in the HIV care continuum in Spain and transition probabilities across different stages. We used data from the prospective Cohort of the Spanish HIV/AIDS Research Network to analyse the time from diagnosis to linkage to care, linkage to care to antiretroviral therapy (ART), and ART to viral suppression in five calendar periods defined by milestones in ART, from 2005 to 2022. We used the Kaplan-Meier method and Cox proportional hazard models to estimate cumulative probabilities of stage transition within 1, 3, 6, and 12 months of stage eligibility, by period. We included 18 529 participants. Comparing the initial (2005-09) and final (2020-22) periods, time to linkage to care decreased from a median of 6·0 weeks to 1·3 weeks, time to ART initiation from 15·9 weeks to 0·4 weeks, and time to viral suppression from 13·3 weeks to 7·1 weeks. Adjusted hazard ratios for the comparison between the last period and the initial period were 3·1 (95% CI 2·8-3·4) for linkage to care within 1 month, 11·4 (10·1-12·3) for ART initiation within 1 month, and 2·2 (1·2-2·4) for viral suppression within 3 months. The aggregate proportion of late diagnoses was 38·6%, increasing after 2012 to 46·4% in the 2020-22 period. Same-day ART initiation increased from 18% to 39% from 2005 to 2022. The overall incidence rate of virological failure was 1·05 failures per 1000 person-years and showed a non-significant decline throughout the study. The great improvement in transition times through the HIV care cascade might put Spain on the verge of achieving the UNAIDS targets for HIV elimination. However, late diagnosis remains a challenge that should be addressed. Instituto de Salud Carlos III and Spanish AIDS Research Network.

Immune checkpoint inhibitors as the second-line treatment for advanced esophageal squamous cell carcinoma: a cost-effectiveness analysis based on network meta-analysis

BackgroundImmune checkpoint inhibitors (ICIs) have demonstrated superior clinical efficacy in prolonging overall survival (OS) as the second-line treatment for advanced or metastatic esophageal squamous cell carcinoma (ESCC), and were recommended by the guidelines. However, it remains uncertain which ICI is the most cost-effective. This study assessed the cost-effectiveness of ICIs as the second-line treatment for ESCC based on the perspective of the Chinese healthcare system.MethodsA network meta-analysis (NMA) was performed to obtain the Hazard ratios (HRs) for indirect comparisons. A three-state Markov model with a 10-year time horizon was conducted to assess the cost-effectiveness. The state transition probabilities were calculated with Kaplan-Meier (KM) curves data from clinical trial and HRs from the NMA. Utilities and costs were derived from local charges or previously published studies. Univariate and probabilistic sensitivity analyses (PSA) were performed to examine model robustness. The results were assessed based on the total costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs).ResultsFive clinical trials (ATTRACTION-3, ESCORT, KEYNOTE-181, ORIENT-2, RATIONALE-302) with a total of 1797 patients were included in the NMA. The NMA showed that both camrelizumab and tislelizumab received relatively high rankings for progression-free survival (PFS) and OS. Compared with sintilimab, treatment with tislelizumab and camrelizumab gained 0.018 and 0.034 additional QALYs, resulting in incremental ICERs of $75,472.65/QALY and $175,681.9/QALY, respectively. Nivolumab and pembrolizumab produced lower QALYs and greater costs, suggesting that both were dominated in comparison to sintilimab. HRs and health state utilities were the most influential parameters in most univariate sensitivity analyses of paired comparisons. PSA results suggested that sintilimab had an 84.4% chance of being the most cost-effective treatment regimen at the WTP threshold of $38,223.34/QALY. In the scenario analysis, sintilimab would no longer be cost-effective, if the price of camrelizumab was assumed to decrease by 64.6% or the price of tislelizumab was assumed to decrease by 16.9%.Conclusions and relevanceAmong the five potential competing ICIs, sintilimab was likely to be the most cost-effective regimen as the second-line treatment for locally advanced or metastatic ESCC in China.

Rapid dynamics of electrophysiological connectome states are heritable

Abstract Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N = 928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60–500 ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of dynamic spatial features, specifically states’ Modularity and connectivity pattern. We conclude that genetic effects shape individuals’ connectome dynamics at rapid timescales, specifically states’ overall occurrence and sequencing.

Research on a global periodic climate prediction method based on improved BP neural network

Climate prediction has a wide social demand, but due to the complexity of climate system formation and the uniqueness of Chinas terrain, accurate prediction is extremely complex and difficult. Therefore, this article proposes a global periodic climate prediction method based on an improved BP neural network. Firstly, based on historical meteorological data, establish a climate model and summarize and analyze the characteristics of the patterns; Then, based on the BP neural network, considering the relationship between adjacent days, rolling sampling of historical data is carried out according to seasonal and meteorological factors in the sampling interval, establishing a multi-state transition probability matrix, and constructing an annual time series forecasting model; Finally, taking precipitation prediction as an example, an example was used to simulate the annual precipitation situation. The calculation results have verified the effectiveness of the proposed method, indicating that it can simulate climate conditions well and provide guidance for climate governance and engineering construction.

An information field theory approach to Bayesian state and parameter estimation in dynamical systems

Dynamical system state estimation and parameter calibration problems are ubiquitous across science and engineering. Bayesian approaches to the problem are the gold standard as they allow for the quantification of uncertainties and enable the seamless fusion of different experimental modalities. When the dynamics are discrete and stochastic, one may employ powerful techniques such as Kalman, particle, or variational filters. Practitioners commonly apply these methods to continuous-time, deterministic dynamical systems after discretizing the dynamics and introducing fictitious transition probabilities. However, approaches based on time-discretization suffer from the curse of dimensionality since the number of random variables grows linearly with the number of time-steps. Furthermore, the introduction of fictitious transition probabilities is an unsatisfactory solution because it increases the number of model parameters and may lead to inference bias. To address these drawbacks, the objective of this paper is to develop a scalable Bayesian approach to state and parameter estimation suitable for continuous-time, deterministic dynamical systems. Our methodology builds upon information field theory. Specifically, we parameterize the dynamical system state path using a linear basis. Then, we construct a physics-informed prior probability measure for the coefficients of the linear basis such that functions that satisfy the physics are more likely. This prior allows us to quantify model form errors. We connect the system's response to observations through a probabilistic model of the measurement process. The joint posterior over the system responses and all parameters is given by Bayes' rule. To approximate the intractable posterior, we develop a stochastic variational inference algorithm. In summary, the developed methodology offers a powerful framework for Bayesian estimation in dynamical systems.

An empirical examination of financial performance and distress profiles during COVID-19: the case of fishery and food production firms in Vietnam

Purpose Financial ratios are often used to classify firms into different clusters of financial performance. This study aims to classify firms using financial ratios with advanced techniques and identify the transition matrix of firms moving clusters during the COVID-19 period. Design/methodology/approach This study uses compositional data (CoDa) analysis based on existing clustering methods with transformed data by weighted logarithms of financial ratios. The data include 66 listed firms in Vietnam’s food and beverage and fishery sectors over a three-year period from 2019 to 2021, including the COVID-19 period. Findings These firms can be classified into three clusters of distinctive characteristics, which can serve as benchmarks for solvency and profitability. The results also show the migration from one cluster to another during the COVID-19 pandemic, allowing for the calculation of the transition probability or the transition matrix. Practical implications The findings indicate three distinct clusters (good, average and below-average firm performance) that can help financial analysts, accountants, investors and other strategic decision-makers in making informed choices. Originality/value Clustering firms with their financial ratios often suffer from various limitations, such as ratio choices, skewed distributions, outliers and redundancy. This study is motivated by a weighted CoDa approach that addresses these issues. This method can be extended to classify firms in multiple sectors or other emerging markets.

Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering

Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems. However, they often face challenges, such as omitting cluster information and the neglect of higher-order neighbor relationships within the data. To address these issues, a novel subspace clustering method named Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering is proposed. This method seamlessly embeds Markov transition probability information into the self-expression, leveraging a fine-grained neighbor matrix to uncover latent data structures. This matrix preserves crucial high-order local information and complementary details, ensuring a comprehensive understanding of the data. To effectively handle complex nonlinear relationships, the method learns the underlying manifold structure from a cross-order local neighbor graph. Additionally, connectivity constraints are applied to the affinity matrix, enhancing the group structure and further improving the clustering performance. Extensive experiments demonstrate the superiority of this novel method over baseline approaches, validating its effectiveness and practical utility.

D.2 Cost-effectiveness analysis of efgartigimod vs chronic IVIg for treatment of patients with generalized myasthenia gravis in Canada

Background: Efgartigimod is a human IgG1 antibody Fc fragment recently approved by Health Canada for patients with acetylcholine receptor antibody positive (AChR-Ab+) generalized myasthenia gravis (gMG). We assessed cost-effectiveness of efgartigimod vs chronic IVIg for adult patients with AChR-Ab+ gMG. Methods: A Markov model estimated costs (treatment and administration, disease monitoring, complications from chronic corticosteroid use, exacerbation and crisis management, adverse events, end-of-life care) and benefits (quality-adjusted life-years [QALYs]). The analysis was conducted from the Canadian healthcare system perspective. Health state transition probabilities were estimated using data from ADAPT, ADAPT+, and a network meta-analysis comparing efgartigimod with chronic IVIg. Utility values were obtained from MyRealWorld MG. Patients with MG-ADL ≥5 who did not die/discontinue were assumed to receive treatment every 4 weeks or every 3 weeks over the lifetime horizon. Results: Over the lifetime horizon, efgartigimod and chronic IVIg were predicted to have total discounted QALYs of 16.80 and 13.35, and total discounted costs of $1,913,294 and $2,170,315, respectively. Efgartigimod dominated chronic IVIg with incremental QALYs of 3.45 and cost savings of $257,020 over the lifetime horizon. Conclusions: Efgartigimod may provide greater benefit at lower costs than chronic IVIg for Canadian patients with AChR-Ab+ gMG, with substantial healthcare system savings over the lifetime horizon.

Quantum transitions, detailed balance, black holes, and nothingness

We consider vacuum transitions by bubble nucleation among 4D vacua with different values and signs of the cosmological constant Λ, including both up and down tunnelings. Following the Hamiltonian formalism, we explicitly compute the transition probabilities for all possible combinations of initial and final values of Λ and find that up tunneling is allowed starting not only from dS spacetime but also from AdS and Minkowski spacetimes. We trace the difference with the Euclidean approach, where these transitions are found to be forbidden, to the difference of treating the latter spacetimes as pure (vacuum) states rather than mixed states with correspondingly vanishing or infinite entropy. We point out that these transitions are best understood as limits of the corresponding transitions with black holes in the zero mass limit M→0. We find that detailed balance is satisfied provided we use the Hartle-Hawking sign of the wave function for nucleating spacetimes. In the formal limit Λ→−∞, the transition rates for anti–de Sitter (AdS) to dS agree with both the Hartle-Hawking and Vilenkin amplitudes for the creation of dS from nothing. This is consistent with a proposal of Brown and Dahlen to define “nothing” as AdS in this limit. For M≠0, the detailed balance is satisfied only in a range of mass values. We compute the bubble trajectory after nucleation and find that, contrary to the M=0 case, the trajectory does not correspond to the open universe slicing of dS. We briefly discuss the relevance of our results to the string landscape. Published by the American Physical Society 2024

The Role of EEG microstates in predicting oxcarbazepine treatment outcomes in patients with newly-diagnosed focal epilepsy

PurposeMicrostates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy. MethodsThis study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes. ResultsIn the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively. ConclusionThis study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.

Stress testing for IInd pillar life-cycle pension funds using hidden Markov model

AbstractThis paper presents a stress testing technique based on a hidden Markov regime switching model and scenario generations. Firstly, we assume that investor’s risk preferences and beliefs are primarily observed in main world indices and then transmitted to other markets such as IInd pillar life-cycle pension funds. This enables both regime (market state) identification and regime-switching detection. Second, we apply stress to the transition matrix by increasing probabilities of moving to a worse market state, i.e., we increase the transition probabilities on the upper-right side of the main diagonal and decrease the diagonal probabilities correspondingly. Third, future scenarios (evolution) of returns of pension funds are generated using stressed transition matrices and basic risk and performance measures of the final wealth are presented. Finally, three different strategies for pension managers are considered and compared to each other. The results show how the best strategy depends on the stress level.

Corruption-Robust Exploration in Episodic Reinforcement Learning

We initiate the study of episodic reinforcement learning (RL) under adversarial corruptions in both the rewards and the transition probabilities of the underlying system, extending recent results for the special case of multiarmed bandits. We provide a framework that modifies the aggressive exploration enjoyed by existing reinforcement learning approaches based on optimism in the face of uncertainty by complementing them with principles from action elimination. Importantly, our framework circumvents the major challenges posed by naively applying action elimination in the RL setting, as formalized by a lower bound we demonstrate. Our framework yields efficient algorithms that (a) attain near-optimal regret in the absence of corruptions and (b) adapt to unknown levels of corruption, enjoying regret guarantees that degrade gracefully in the total corruption encountered. To showcase the generality of our approach, we derive results for both tabular settings (where states and actions are finite) and linear Markov decision process settings (where the dynamics and rewards admit a linear underlying representation). Notably, our work provides the first sublinear regret guarantee that accommodates any deviation from purely independent and identically distributed transitions in the bandit-feedback model for episodic reinforcement learning. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2021.0202 .

Relativistic atomic structure calculations of Li-like ions used for plasma diagnostic studies

We have carried out atomic structure calculations using systematically enlarged multiconfiguration Dirac-Fock wavefunctions of Li-like ions of the most prominent plasma impurities (Ar, Ti, Fe, Ni, Kr and W) found in presently working tokamaks. Relativistic Breit interaction and quantum electrodynamic (QED) corrections such as vacuum polarization and self-energy corrections are also included in the calculations prior to the evaluation of low lying energy levels, transition probabilities, oscillator strengths and line strengths. Selective radiative data for electric dipole and magnetic quadrupole transitions are also reported. Special emphasis is given in the computations of fundamental quantities such as oscillator strengths as they are widely used in atomic data and analysis structure (ADAS) databases to evaluate quantities such as effective collision strengths. Present computed values are compared with existing available results on NIST database and few similar earlier computations and a good agreement has been found. We believe that the detailed atomic data with the relativistic and QED corrections will assist in spectroscopic studies such as accurate line identification and plasma modelling work in tokamak plasma, laser induced breakdown spectroscopy (LIBS), highly charged ions clocks and astrophysical observations.