Coventry Health Care, Inc. was a health insurer in the United States. It had 3.7 million medical members, 1.5 million Medicare Part D members, and 900,000 Medicaid members. In May 2013, the company was acquired by Aetna for $5.7 billion.7 billion.
We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to −0.28, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference.
BACKGROUND:Baroreflex activation therapy (BAT) using an implanted neurostimulator enhances functional status and quality of life in patients with symptomatic heart failure with reduced ejection fraction (HFrEF). Nonetheless, its influence on mortality, heart failure morbidity, and optimal patient subgroups requires further study. METHODS:BENEFIT-HF is a prospective, randomized, open-label trial recruiting patients with left ventricular ejection fraction (LVEF) <50% who remain symptomatic despite guideline-directed medical and device therapies. A total of 2500 eligible ambulatory patients with stable heart failure will be randomized 2:1 to BAT implantation (Barostim System, CVRx, Inc, Minneapolis, MN, USA) plus standard care (Treatment Group) or standard care alone (Control Group). The primary efficacy endpoint is a 24-month composite of all-cause mortality, left ventricular assist device implantation or heart transplantation, and recurrent heart failure events. Secondary endpoints encompass changes in health status via the Minnesota Living with Heart Failure Questionnaire, 6-minute hall walk distance, days lost due to death or hospitalization, N-terminal pro-B-type natriuretic peptide levels, and all-cause mortality. CONCLUSIONS:BENEFIT-HF represents the largest device-based trial evaluating BAT as a neurohormonal modulator in a diverse cohort of patients with HFrEF and heart failure with mildly reduced ejection fraction (HFmrEF), aimed at determining its effects on morbidity, mortality, health status, and functional capacity.
Globalization has made travel essential to economic and social life while accelerating the international spread of disease, increasing the importance of risk communication. This chapter examines how travel health risks are communicated at two interconnected levels: population-focused public health messaging and individualized clinical counseling. It traces the evolution of travel health communication in response to rising travel volumes, increasingly diverse traveler profiles, and digital real-time information environments. Public health risk communication frameworks are reviewed through comparative analysis of outbreaks including COVID-19, Zika, and Ebola, highlighting common challenges of uncertainty, misinformation, engagement, and trust. Ethical considerations of transparency, proportionality, equity, and infodemic management are explored. Finally, psychological models of risk perception explain how travelers interpret guidance, informing strategies for patient-centered counseling that translate population recommendations into feasible protective decisions.
With the rise of social media, employees have turned into brand ambassadors of companies as influential figures, creating and disseminating public opinion and organizational reputation. The present study tries to explore the degree to which certain in-house communication approaches by , transparency, openness, empowerment, and recognition, trigger employee advocacy in Indian organizations. Based on relationship management and communication theory, this study also investigates the mediating role of employee engagement and the moderating influence of social media usage in this relationship. The research approach is quantitative and based on survey data gathered from 420 samples from permanent Indian employees from various industries. This research seeks to identify how emotionally committed employees, helped by effective internal communications, will have a better chance of posting positive tweets regarding their business and hence promote its reputation. The research also examines whether the frequency and intensity of social media use confirm this advocacy-reputation linkage. This study adds to the literature on employee advocacy by synthesizing communication practice, engagement, and social media activity, providing practical recommendations for organizations wanting to develop reputational capital through employees in digitally networked spaces.
As autonomous vehicles (AVs) are increasingly piloted in urban environments, establishing appropriate levels of pedestrian trust is critical for ensuring both safety and traffic efficiency. This study examines the influence of external human-machine interfaces (eHMIs) on pedestrian trust during AV encounters at unsignalized crosswalks. A virtual reality (VR) experiment involving 50 participants was conducted to assess how variations in eHMI timing and vehicle behavior affect trust calibration. The experimental design included a between-subject factor comparing failure scenarios with immediate versus delayed eHMI display, and a within-subject factor encompassing seven vehicle behavior conditions. Trust ratings were collected after each scenario and analyzed using a linear mixed-effects model. Results show that the timing and consistency of eHMI signals significantly influence pedestrian trust. Notably, delayed eHMI messages led to more cautious trust responses in failure scenarios, while still maintaining high trust in successful yielding conditions. These findings suggest that strategically delaying eHMI communication may mitigate overtrust and support safer pedestrian-AV interactions. The study contributes preliminary evidence toward designing trust-calibrated AV communication systems.
