University of Memphis

Tennessee ranks sixth in stroke mortality in the United States. Yet the patterns of stroke mortality vary significantly across counties and over time. This study aims to examine spatiotemporal disparities of stroke mortality at the county level in Tennessee from 1969 to 2020. A population-based study using the national vital statistics system of stroke mortality data through the Surveillance, Epidemiology, and End Results and National Center for Health Statistics (SEER-NCHS) database. Patients older than 35 years who died from stroke in Tennessee from 1969 to 2020. Data from the SEER-NCHS were aggregated into 4 periods (1969-1980, 1981-1992, 1993-2004, and 2005-2020), and age-adjusted stroke mortality rates were calculated by county and by race and sex for each time period. The stroke mortality rates in Tennessee declined by 35.6%, 28.3%, 7.1%, and 24.4% in 1969-1980, 1981-1992, 1993-2004, 2005-2020, respectively. The degree of decline varied by race and sex groups. In the first 2 periods, the largest decline in stroke mortality was observed among Black women (43.2% and 31.5%). During 1993-2004, the largest decline was observed among Black men (22.4%), while the largest decline was observed among white women during 2005-2020 at 25.5%. There were urban-rural disparities in stroke mortality across counties and over the 4 periods. In general, urban and rural mortality rates were similar from 1969 to 1992; however, a substantial decline (24.1%) was observed in urban counties during 1993-2004, while a larger decline (34.6%) occurred later in rural counties during 2005-2020. County-level variations in stroke mortality were also evident across the 4 periods. Substantial disparities in stroke mortality by counties and race-sex subgroups persisted over the past 5 decades. The disease burden was clustered in a few counties and disproportionately higher among vulnerable populations.

ABSTRACT Background Postpartum weight retention is a key concern due to the risk of escalating overweight/obesity, as well as the risk of diabetes, hypertension, and cardiovascular disease. Self‐weighing may be a helpful strategy for reducing weight retention; however, the ideal frequency of self‐weighing is not known. Methods The objective of this secondary analysis ( N = 263) examined the role of self‐weighing during the 6‐month postpartum period, with a focus on substantial postpartum weight retention (i.e., ≥ 5 kg). All participants received e‐scales at randomization, which captured daily self‐weighing data over the postpartum period (6 weeks postpartum to 6 months postpartum). Multivariable logistic and linear regression models were applied to determine the association between self‐weighing frequency and postpartum weight retention while controlling for covariates. Results Most participants had overweight or obesity (68.7%), were nulliparous (47.5%), and identified as White (67.3%). The mean self‐weighing frequency was 2.45 ± 1.86 days per week. Each additional day of self‐weighing per week was associated with 34% lower odds of substantial postpartum weight retention, relative to < 0.5 kg weight retention (OR: 0.66, 95th CI: 0.54,0.84, p = 0.001). Self‐weighing ≥ 4 days per week was associated with 3.3 kg lower weight retention, compared to participants who weighed less frequently ( β = −2.89, 95th CI: −4.51,−1.27, p = 0.001). Conclusions Thus, self‐weighing ≥ 4 days per week could be tested as a goal for postpartum weight management. These findings may inform future postpartum weight management programs, as self‐weighing is an easily disseminated strategy. Trial Registration The trial is registered on clinicaltrials.gov (NCT 03057808)

Sustainable materials are increasingly being incorporated into high-strength concrete (HSC) to reduce environmental impact while maintaining structural performance. This study experimentally investigates the combined use of steel scale waste (SSW) as a replacement for natural fine aggregates and graphite nano/micro platelets (GNMPs) as a nano-modifying additive in HSC. Natural sand was replaced with SSW at levels of 0%, 50%, and 100%, while GNMPs were incorporated at dosages of 0%, 0.1%, 0.3%, and 0.5% by weight of cement. The results indicate that partial replacement of sand with SSW significantly improves concrete density and mechanical performance due to enhanced particle packing and the high specific gravity of steel scale particles. At the nanoscale, GNMPs contribute to pore refinement, improved nucleation of hydration products, and crack-bridging within the cement matrix, thereby strengthening the interfacial transition zone and delaying crack propagation. The combined effect of these mechanisms produces a synergistic enhancement in concrete performance. The optimum mixture containing 50% SSW and 0.3% GNMPs achieved a compressive strength of 68.2 MPa and splitting tensile strength of 7.6 MPa, representing improvements of approximately 54% and 52%, respectively, compared with the control mix. Durability-related properties such as water absorption and sorptivity were also significantly improved due to matrix densification and pore structure refinement. Although the incorporation of SSW and GNMPs reduced workability, all mixtures remained within a practical range for casting. The developed concrete is particularly suitable for structural applications requiring high strength and durability, such as high-rise building components, bridge elements, and precast structural members. The findings demonstrate that the combined use of industrial steel waste and nano-reinforcement offers a promising pathway toward sustainable and high-performance concrete.

In this paper we review different conceptions of rationality as they apply to decision making and problem solving. We defend the notion of enactive problem solving (EPS) and we consider how it applies to the use of therapeutic chatbots. We start by recounting the development of different models of rationality, from expected value maximization, to subjective expected utility theory, and of bounded rationality from prospect theory, to simple adaptive heuristics. We then focus on problem solving. We show how the centre of gravity of problem solving shifts from the model of computational and cognitive processing, to an action-oriented pragmatic one in EPS, that is, to the possible actions that the body-environment interaction allows. We highlight a notion of the agent’s relational autonomy as part of EPS and show that this can be a problem in some chatbot designs in the therapeutic context. EPS suggests that simple early versions of therapeutic chatbots like ELIZA may be more beneficial than more recent sophisticated models involving “Belief-Desire-Intention” (BDI) architecture characterized by predetermined trajectories. The system formed between the agent plus the chatbot tool facilitates a form of enactive problem solving, reducing rigidity on the side of the agent.

ABSTRACT AI‐based recommender systems shape many consumer decisions, but users often have limited information about why a recommendation is presented. This paper examines how explainable AI (XAI) design influences consumers' follow‐through, and when these effects are stronger. We conceptualize XAI design along two dimensions: explanation level (high vs. low diagnostic detail) and explanation type (process‐oriented vs. outcome‐oriented), and we examine boundary conditions across recommendation context (product vs. content). Four scenario‐based, between‐subjects experiments were conducted with Prolific participants who reported familiarity with recommendation systems (total N = 1080). Study 1 establishes the baseline effect: high (vs. low) explanation level increases intention to follow the recommendation, and the effect is robust under divided attention. Study 2 shows that the benefit of higher explanation level is context‐dependent, with stronger effects in content recommendations than in product recommendations. Study 3 shows that explanation type also shapes the effect of explanation level on follow‐through, with process‐oriented explanations producing a larger advantage for high (vs. low) explanation level than outcome‐oriented explanations. Study 4 tests the proposed mechanisms in a 2 × 2 × 2 design and finds that explanation level affects follow‐through primarily through trust and psychological ownership, with these indirect effects stronger in content (vs. product) contexts and under process‐oriented (vs. outcome‐oriented) explanations. Together, the findings specify how explanation level, explanation type, and context jointly determine when XAI increases follow‐through, and they identify trust and psychological ownership as mechanisms through which explanation design translates into consumer action.

ABSTRACT Categorical perception (CP) reflects the human auditory system’s ability to map continuous acoustic signals onto discrete categories. Understanding the relationship between neural dynamics and perceptual decisions is central to speech–language processing. Here, we implemented a data-driven approach using Bayesian nonparametrics and machine learning to characterize the relationship between auditory cortical responses and speech categorization behaviors. By applying a hierarchical Dirichlet process hidden Markov Model (HDP-HMM) to source-reconstructed event-related potential (ERP) data, we identified temporally distinct neural microstates that capture the evolving stages of speech categorization without imposing predefined temporal windows. Machine learning classifiers, including extreme gradient boosting (XGBoost), support vector machines, and random forests, were applied to decode prototypical (Tk1/5) versus ambiguous (Tk3) speech sound tokens from the microstate data. Using whole-brain activity, the XGBoost classifier achieved the highest decoding accuracy of 94.1% with an area under the curve (AUC) 94.1% within a specific encoding microstate occurring approximately 200-250 ms after stimulus onset. A reduced set of 15 informative brain regions identified via Shapley additive explanations (SHAP) yielded comparable classification performance (90.3% accuracy; AUC 90.0%), with many regions localized to frontal, temporal, and parietal regions in the left hemisphere. Furthermore, neural activity within these regions robustly predicted listeners’ behavioral identification slopes ( R 2 = 0.92, p < 0.00001), linking microstate-specific cortical dynamics to individual differences in perceptual gradiency of speech perception. These findings demonstrate that speech categorization emerges within temporally discrete neural microstates during early sensory–perceptual encoding and is supported by a selective, distributed cortical network with clear behavioral relevance.

Standard brief alcohol and drug use interventions focus on motivating reductions in use and associated harms and have shown reliable reductions in these outcomes. However, effect sizes are small, and dissemination is limited by low rates of help seeking. Incorporating intervention elements focused on enhancing wellness, mood, and goal pursuit might enhance efficacy and appeal. The substance-free activity session (SFAS) integrates behavioral economic and motivational interviewing elements to enhance goal-directed and mood-enhancing activity patterns that might provide alternatives to alcohol- and drug-related reward. We conducted a narrative review of 15 empirical studies that investigated the efficacy of the SFAS and evaluated mechanisms of behavior change. We then provide an overview of how to conduct the SFAS, including a session vignette. There is support for the efficacy of the SFAS when integrated with standard brief alcohol interventions for college students. There is also evidence that the SFAS reduces negative affective symptoms and increases self-regulation and proportionate reinforcement from substance-free activities. Evidence for cannabis use reductions is less robust. The SFAS has been successfully implemented as an adjunct to intensive alcohol and drug treatment for adults, but not in the context of adequately powered efficacy trials. The clinical vignette and intervention manual provides a model for how to motivate an individual to pursue reductions in substance use supported by increased engagement in goal-directed and enjoyable substance-free activities. The SFAS shows promise as a brief intervention that supports substance use reductions via motivating patterns of substance-free activities. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Organizations usually rely on stringent access control mechanisms where access policies are an important asset. Their storage or transmission in plaintext can compromise sensitive access rules. It is important in dynamic environments where access decisions are made in real time such as Zero Trust (ZT). Existing ZT approaches were found to oversee the aspect of securing these policies. This investigation presents a Multi-layer Access Policy Encryption System for ZT systems (MAPE-ZT). The first stage uses the trapdoor index to generate a secure index to find the applicable access policies. Advanced Encryption Standard-256 is used in counter mode for the encryption of the policies. They are re-encrypted using the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to allow decryption based on a matching set of attributes. Various experiments using quantitative metrics, including comparison with baseline access control systems simulation, scalability evaluation, storage overhead, etc., highlight the efficacy of the MAPE-ZT and establish new benchmarks. The result count entropy for the policies ranged 3.84–4.21 for different scales of policies. The evaluation in different scales of systems shows that the MAPE-ZT reduces various observable patterns even if the deployment size grows. Its unique design of securing policies makes this approach scalable for multi-domain integration.