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Chemical Composition and Mixing State of Wintertime Aerosol from the European Arctic Site of Ny-Ålesund, Svalbard
No full textThe Arctic is rapidly warming, and aerosols play an increasingly important role by scattering and absorbing sunlight and by participating in cloud formation. Their optical and cloud-forming properties depend on the mixing state and chemical composition, but observations of these features remain limited. This study comprehensively characterizes 25,254 individual particles collected at Ny-Ålesund, Svalbard (November −December 2020), using microspectroscopy techniques to investigate their size, morphology, mixing state, and chemical composition. Fresh sea salt aerosols (SSA) were identified as the most abundant (∼85%), of the total observed aerosol population, with potential sources from sea spray and blowing snow. Air masses originating from the Arctic Ocean surrounding Svalbard likely contribute to increased concentrations of sub-micrometer “Fresh SSA” particles. “Aged SSA” particles (7.4%) are enriched in sulfur and nitrogen, compared to “Fresh SSA”. These elevated ratios may result from various atmospheric aging processes including the uptake of sulfuric and nitric acids. Our results suggest that aged SSA, with sizes larger than 300 nm, likely underwent chlorine depletion by sulfuric and nitric acids during transport. Additionally, elemental analysis reveals that both fresh and aged SSA can mix with dust particles, regardless of the SSA size (49.9% in sub-micrometer size and 50.1% in super-micrometer size, respectively). Dust particles are efficient ice-nucleating particles (INPs), and SSA is known to act as cloud condensation nuclei (CCN), and therefore, their mixtures may inherit both properties. The non-negligible number (4.4%) of SSA-dust mixtures underscores the importance of these particles as potential sources of CCN and INP in the Arctic atmosphere
Design of Future Scholarly Communication
No full textThis experience report documents the evolving practices of scholarly communication in the age of artificial intelligence while publishing an open-access peer-reviewed book. Drawing on experiences from 2023–2025, it explores open-access, print-on-demand publishing, collaborative authorship, and ethical integration of AI tools in academic workflows. Anchored by our work with the WAC Clearinghouse, we highlight how infrastructure, metadata, and peer review shape practices of scholarly production and assessment. We frame literacy not as a fixed competency but as an evolving set of practices shaped by technologies and platforms. Through a mix of analysis, publishing, and reflective design, we argue that scholarly communication is shifting from static to dynamic, from closed to open, from solitary to collaborative. We conclude with a call to senior faculty mentors, administrators, and review committees to recognize and support emerging publication venues—especially digital and community-based outlets. This is a pivot moment for academia: a time when modest advocacy can shape inclusive, sustainable futures for scholarly publishing
Influence of Polymerization and Restricted Dipole Motion on the Dielectric Constants of Ionic Liquids
No full textWe employ molecular dynamics simulations using the Stockmayer fluid (SF) model, in which ionic groups are treated as charged dipolar spheres, to study the dielectric constants of ionic liquids (ILs) and polymerized ionic liquids (PILs). The simulations model ethylammonium nitrate and poly(N-vinyl ethylimidazolium bromide) by implementing both a freely rotating dipolar PIL model and one with restricted dipolar motion for the side chains. We compare their dielectric constants and analyze their dipole orientational order and correlations. We find that there is a relationship between degrees of rotational freedom and the dielectric constants of PILs, and this relationship defines the change in the dielectric constant of PILs compared to their monomeric IL counterparts
Pioneering Insights into the Complexities of Salt-Sensitive Hypertension: Central Nervous System Mechanisms and Dietary Bioactive Compound Interventions
No full textSalt-sensitive hypertension (SSH) is an important and common subtype of hypertension, whose pathogenesis involves multi-level regulation, including the central nervous system (CNS), metabolic stress, and epigenetics. Dietary bioactive compounds have emerged as a research hotspot for SSH intervention due to their safety and multi-target effects. Although existing studies have focused on the CNS regulation of SSH or the role of individual dietary components, there is a lack of comprehensive analysis integrating multiple mechanisms, systematically summarizing multiple compounds, and incorporating a clinical translation perspective. This review first outlines the mechanisms of CNS pathways, endoplasmic reticulum (ER) stress, mitochondrial dysfunction, and epigenetic modifications in SSH. Then, it systematically reviews the mechanisms of action and preclinical and clinical research progress of bioactive compounds, including capsaicin, taurine, gamma-aminobutyric acid, tea, and anthocyanins in SSH. In summary, this review systematically clarifies the complex regulatory network of SSH and the intervention potential of dietary bioactive compounds from an integrated perspective, innovatively proposes a precise dietary intervention framework, and fills the research gaps in the integration of multiple mechanisms and systematic evaluation of compounds in existing studies. This framework not only provides a new integrated perspective for the basic research of SSH but also offers key references for clinical dietary guidance, functional food development, and the formulation of targeted intervention strategies
Outer scale effects on optical scintillation in the transition from focusing to saturation.
No full textRecent works in optical scintillation have shown heuristic continuations of weak turbulence theory and asymptotic theory do not match with results of experimental or simulation campaigns. One possible source of these discrepancies is a failure to account for outer scale effects in asymptotic models. Experiment and theory suggest that a smaller outer scale should affect only the rate at which scintillation approaches the asymptote at high turbulence levels. Here, I present the results of a wave optics simulation campaign for a spherical wave source with a varied outer scale and compare the results to Extended Rytov Theory. I find that in contrast to theory a finite, von Karman outer scale plays a significant role in suppressing peak scintillation in the focusing regime and that this result is predicted both by simulation and theory. Similar to other works I find that heuristic theory under predicts scintillation in the focusing regime compared to theory when a finite outer scale is present
ESTIMATION OF QUEUE LENGTH AND GREENLIGHT DURATION AT SIGNALIZED INTERSECTION USING COLLABORATIVE PERCEPTION AND MACHINE LEARNING METHODS IN V2X ENVIRONMENTS
No full textConnected and Autonomous Vehicles (CAVs) utilize ecodriving systems for efficient fuel consumption while traversing signalized intersections. Applications such as Green Light Optimal Speed Advisory(GLOSA), Eco Arrival and Departure, and other Eco- Driving support these objectives. Estimating the accurate signal phase timing durations with real-time evaluation of queue lengths, queue clearance timings, and other traffic flow parameters play a crucial role in enabling energy-efficient maneuvering through intersections. To enable the dynamic processing of traffic flow, this research focuses on utilizing collaborative perception through Vehicle-to-Everything (V2X) communication to estimate queue lengths and broadcast estimated green light durations through enhanced SPAT messages. In our approach, Road Side Units collaborate with CAVs through perception data sharing to calculate green light duration at signalized intersections, enabling efficient traffic flow through optimized broadcasting of timing information. The study features a random forest algorithm with geo-spatial and corridor enhancements. Our model is trained on corridor specific data from Metro Detroit area, capturing individual intersection geometry and signal timing plans. Results reveal our system efficiently determines duration of green light based on vehicle distribution along the lanes leading to the intersection, queue length and traffic flow parameters. This efficiency is maintained under different traffic volumes and queue lengths with improved performance
Experimental and Simulation Study of a Capacitance Void Fraction Sensor in a Rectangular Channel
No full textA Review of In Situ Manganese Bioleaching and Recovery Techniques
No full textThe rise in global consumption of manganese, largely due to its extensive use in the steel and battery industries, has increased the demand for sustainable manganese extraction methods. Conventional manganese extraction methods, such as open pit mining followed by pyrometallurgy and hydrometallurgy, are often costly and environmentally harmful due to their reliance on high energy and chemical reagents. The in situ bioleaching approach has the potential to replace conventional approaches, offering cost savings and environmental benefits. In situ bioleaching utilizes microorganisms and a source of nutrient media to directly dissolve manganese within ore bodies under natural environmental conditions. This review provides an in-depth discussion of the critical components of in situ bioleaching, including manganese dissolution mechanisms, manganese-reducing organisms, nutrient media for microbial growth, and manganese recovery options. This study also explores the challenges associated with scaling in situ bioleaching to a commercial level and provides insights into future directions to overcome challenges
Organic geochemical evidence for life in Archean rocks identified by pyrolysis–GC–MS and supervised machine learning
No full textThroughout Earth’s history, organic molecules from both abiogenic and biogenic sources have been buried in sedimentary rocks. Most of these organic molecules have been significantly altered by geologic processes through deep time. Nonetheless, the nature and distribution of those ancient fragmentary organic remains have the potential to reveal diagnostic biomolecular information after billions of years of burial. Here, we analyzed 406 fossil, modern biological, meteoritic, and synthetic samples using pyrolysis gas chromatography and mass spectrometry. We explored these analytical data via supervised machine-learning methods to discriminate samples of biogenic vs. abiogenic origin, plant vs. animal phylogenetic affinity, and photosynthetic vs. nonphotosynthetic physiology. Dividing 272 samples with known phylogenetic affinity and physiology into 9 categories, each further divided into 75% training and 25% testing sets, our random forest models accurately predict pairwise assignments of modern vs. fossil or meteoritic organics (100% correct assignments), fossil plant tissues vs. meteoritic organics (97%), modern vs. fossil plant tissues (98%), and modern plants vs. animal tissues (95%). Pairwise comparisons between fossil biogenic samples vs. abiogenic samples resulted in 93% correct classifications, while analysis of modern and ancient photosynthetic vs. nonphotosynthetic samples also resulted in 93% correct assignments. Our analyses demonstrate that molecular biosignatures can survive in ancient fossils and allow for the identification of organismal origins and traits. Consistent with previous morphological and isotopic inferences, we present evidence for biogenic molecular assemblages in Paleoarchean rocks (3.33 Ga) and for photoautotrophy in Neoarchean rocks (2.52 Ga)