California State University, Monterey Bay,California State University System

Abstract Heterogeneous reactions occurring in atmospheric aerosols are crucial components of multiple chemical reaction cycles. Sulfate species exert substantial influence on many heterogeneous reaction mechanisms due to their abundance and speciation. Despite the importance of sulfate species on heterogeneous reactions, quantitative descriptions of interfacial affinity and sulfate species partitioning at interfaces remain incomplete. Here, we quantify the interfacial properties of HSO4–, using cetyltrimethylammonium hydrogen sulfate (CTAHS) reverse micelles (RMs), as model marine aerosol particles. Infrared spectra (IR) of CTAHS RMs were collected and analyzed using multivariate curve resolution-alternating least-squares (MCR-ALS). MCR-ALS analysis reveals that four distinct species contribute to the observed IR spectra, which we assign as CTA+-HSO4– contact pairs (CPs), interfacial HSO4–, core HSO4–, and core SO42–. Computational studies reveal that the CTA+-HSO4– CPs exist in two distinct orientations at the interface. Spectral contributions from each chemical species are used to determine the quantitative interfacial affinity (χInt.) of HSO4–. Our observations indicate that the cationic charge of the interface alters the ionic distribution of sulfate species compared to air–water interfaces, placing H3O+ below HSO4–.

BACKGROUND:Due to minimal effects, new directions for behavioral (non-pharmacologic/non-surgical) obesity interventions are required. Previous research suggests that physical activity and exercise (PA/exercise) might have considerable merit for weight reduction via impacts on mood and other psychosocial correlates of controlled eating. Although as few as 3 bouts/week of low-moderate intensity PA/exercise are associated with improved mood-with no dose-response effect beyond that frequency-it is unknown if mood improvements may be leveraged when participants already complete ⩾3 bouts/week by treatment start. OBJECTIVES:The present research addressed gaps in the available PA/exercise-psychosocial change-weight management research. DESIGN:This study combined group contrasts with mediation analyses. METHODS:Women participating in a community-based cognitive-behavioral obesity treatment emphasizing the increase of PA/exercise and exercise-related self-regulation and self-efficacy (N = 99) were divided for separate analyses into groupings of <3 bouts/week, and ⩾3 bouts/week, of light (e.g., easy walking) and moderate (e.g., fast walking) PA/exercise regularly completed prior to treatment start. The participants were assessed on psychosocial and PA/exercise changes from baseline to month 3. RESULTS:Reductions in total mood disturbance (TMD), depression, and anxiety scores were overall significant, with no significant difference by exercise-frequency grouping. PA/exercise increases were significantly greater in the <3 bouts/week groupings. Change in self-regulatory skills usage mediated relations between groupings and changes in TMD, depression, and anxiety. In subsequent serial mediation models, significant paths from grouping → self-regulation change → self-efficacy change → TMD and depression changes were found. CONCLUSION:Whether or not ⩾3 bouts/week of PA/exercise are completed at intervention start, TMD, depression, and anxiety may be reduced through treatment foci on increasing PA/exercise, self-regulation, and self-efficacy. Findings might be useful for informing scalable obesity treatments.

The standard in Earth-observation tasks today is having near real-time access to surface images in response to changing conditions. For instance, as urban environments interface more with wildlands and wildfires become less predictable, their tracking with satellite resources becomes essential. This requires the coordination of increasingly large constellations of satellites, giving rise to challenging computational problems. With wildfire detection and tracking as a backdrop, we investigate the power of special purpose and novel computing paradigms to tackle the ensuing satellite scheduling problems, making a compelling case for quantum algorithms. We bring quantum scheduling algorithms closer to implementation by examining both the emerging iterative quantum algorithm framework, which comes with analytic guarantees compared to some classical algorithms, and distributed quantum computing methods whose relevance is on the rise as utility-scale problems begin to get solved with quantum computers. Drawing strength from several computing fronts, we develop a distributed/parallelization scheme in conjunction with the quantum algorithm design and apply these techniques to real-world datasets for wildfire detection. While our quantum subprocesses are currently too small to see significant quantum advantage, our results validate the utility of these techniques, and continue forging the path toward distributed quantum computing.

OBJECTIVES:This study explores how older South Asian migrants in the United States navigate mobile health (mHealth) tools for type 2 diabetes self-management. The goal is to identify multilevel facilitators and barriers to digital engagement within culturally and structurally embedded contexts. METHODS:We conducted a qualitative descriptive study using semi-structured interviews with 21 South Asian adults aged 55 and older who had migrated to the U.S. later in life and had recent experience using a diabetes management app. Thematic analysis was guided by the Social-Ecological Model (SEM) and the Consolidated Criteria for Reporting Qualitative Research (COREQ). RESULTS:Participants' engagement with mHealth tools was shaped by personal confidence, emotional response, and adaptive strategies at the individual level; family and peer dynamics at the interpersonal level; lack of provider support and culturally misaligned features at the organizational level; culturally rooted norms and informal peer networks at the community level; and broader systemic exclusions related to language access, insurance coverage, and technology infrastructure at the policy level. While participants demonstrated motivation and resilience, emotional fatigue, app complexity, and cultural mismatches often limited sustained use. CONCLUSIONS:Older South Asian migrants are not disinterested in digital health; rather, they are systemically excluded. For mHealth to meaningfully support diabetes self-management, tools and systems must be designed with linguistic access, cultural alignment, and policy-level support in mind. Findings underscore the need for culturally tailored, relationally supported, and digitally inclusive interventions that affirm the everyday realities of aging immigrant populations.

Ecological modeling captures natural phenomena and processes, aiding in our understanding of ecology, conservation biogeography, and hydrology. The integration of ecological modeling into adaptive management frameworks is, however, underutilized. Adaptive modeling can enhance adaptive management by integrating new information and responding to changing environmental conditions, which facilitates dynamic and effective decision-making. This paper addresses challenges in implementing adaptive modeling, such as the need for collaborative model development, clear communication of model outcomes and limitations, and data and code accessibility. We propose a suite of potential actions to enhance communication, collaboration, and model utility. Focusing on water resource management, we demonstrate how adaptive modeling can inform management decisions and improve conservation outcomes. We highlight the importance of integrating adaptive modeling into future adaptive management plans to ensure models are relevant, reliable, and effectively utilized in resource management and conservation.