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Recent Advances in Wildland Fire Smoke Dynamics Research in the United States
No full textSmoke plume dynamics involve various smoke processes and mechanics in the atmosphere and provide the scientific foundation for the development of tools to simulate and predict smoke and its environmental and human impacts. The increasing occurrence of wildfires and the demands for more extensive application of prescribed fires in the U.S. have posed great challenges and immediate actions for advancing smoke plume dynamics and improving smoke predictions and impact assessments to mitigate smoke impacts. Numerous efforts have been made recently to address these needs and challenges. This paper synthesizes advances in smoke plume dynamics research mainly conducted in the U.S. in the recent decade, identifies gaps, and suggests future research needs. The main advances include smoke data collections from comprehensive field campaigns, new satellite products, improved understanding of smoke plume properties and chemistry, structure and evolution, evaluation and improvement of smoke modeling and prediction systems, the development of coupled smoke models, and applications of machine-learning techniques. The major remaining gaps are the lack of comprehensive simultaneous measurements of smoke, fuels, fire, and atmospheric interactions during wildfires, high-resolution coupled modeling systems of these components, and real-time smoke prediction capacity. The findings from this synthesis study are expected to support smoke research and management to meet various challenges under increasing wildland fires and impacts
Towards Mitigation of Hallucination for LLM-Empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
No full textEmpowered by large language models (LLMs), intelligent agents have become a popular paradigm for interacting with open environments to facilitate AI deployment. However, hallucinations generated by LLMs - where outputs are inconsistent with facts - pose a significant challenge, undermining the credibility of intelligent agents. Only if hallucinations can be mitigated, the intelligent agents can be used in real-world without any catastrophic risk. Therefore, effective detection and mitigation of hallucinations are crucial to ensure the dependability of agents. Unfortunately, the related approaches either depend on white-box access to LLMs or fail to accurately identify hallucinations. To address the challenge posed by hallucinations of intelligent agents, we present HalMit, a novel black-box watchdog framework that models the generalization bound of LLM-empowered agents and thus detect hallucinations without requiring internal knowledge of the LLM\u27s architecture. Specifically, a probabilistic fractal sampling technique is proposed to generate a sufficient number of queries to trigger the incredible responses in parallel, efficiently identifying the generalization bound of the target agent. Experimental evaluations demonstrate that HalMit significantly outperforms existing approaches in hallucination monitoring. Its black-box nature and superior performance make HalMit a promising solution for enhancing the dependability of LLM-powered systems
Multiphase salt making at Nakuukuwidish/Holman Springs, a Caddo and settler salt-making site in Sevier County, Arkansas
No full textColonial/settler saltworks, particularly in rural or border settings, are less frequently studied archaeologically and historically than are larger-scale, industrialized saltworks. Using historical and archival information in conjunction with field observations at a small-scale saltworks in southwest Arkansas can draw out the importance of sites like Nakuukuwidish/Holman Springs (3SV29) for understanding the structure and development of consumption habits, economic arrangements, and supply chains for American settlers in the nineteenth century. The site has three phases of occupation spanning six centuries. In this report, we develop frameworks for continuing research on small-scale salt manufacturing in nineteenth-century America and its comparison with Indigenous practices
CO2 Conversion to Value-Added Products Through Electrochemical Reduction
No full textThe increasing concentration of carbon dioxide (CO[[inf]]2[[/inf]]) in the Earth’s atmosphere is a pressing environmental challenge, contributing to global warming and climate change. Addressing CO[[inf]]2[[/inf]] emissions through innovative technologies is critical for achieving sustainability and mitigating environmental impacts. Among various strategies, the electrochemical conversion of CO[[inf]]2[[/inf]] into valuable chemicals and fuels emerges as a promising approach. This method not only offers a pathway to reduce atmospheric CO[[inf]]2[[/inf]] levels but also provides a sustainable alternative to fossil fuel-based resources. This chapter delves into the principles, advancements, and challenges of electrochemical CO[[inf]]2[[/inf]] conversion, highlighting its significance and potential in addressing environmental concerns
The heart-brain axis: unraveling the interconnections between cardiovascular and Alzheimer’s diseases
No full textCardiovascular disease (CVD) and Alzheimer\u27s disease (AD) are leading causes of death and disability worldwide, and recent research has increasingly illuminated a complex, bidirectional relationship between the two. This review synthesizes epidemiological, mechanistic, imaging, and genetic evidence linking CVD and AD through the heart-brain axis—a network of interrelated physiological and demographic pathways. We detail how cerebral hypoperfusion, inflammation, blood-brain barrier dysfunction, imbalance of the autonomic nervous system, and systemic amyloidosis contribute to shared neurodegenerative and cardiovascular outcomes. Multi-organ imaging studies, including MRI and PET, reveal that dysfunction of the cardiovascular system correlates with brain atrophy, white matter lesions, glymphatic impairment, and accumulation of AD-related proteinopathies. Genetic analyses further support overlapping risk architectures, particularly involving APOE and loci associated with lipid metabolism, vascular integrity, and inflammation. Age and sex are critical modifiers, with midlife CVD exerting the strongest influence on later cognitive decline, and sex-specific physiological responses shaping disease susceptibility. Finally, we explore how modifiable lifestyle factors, pharmacologic interventions, and precision medicine approaches targeting inflammatory and vascular pathways can jointly reduce the burden of both CVD and AD. Multidisciplinary collaboration to understand the interconnected biology of the heart and brain is essential for advancing integrated prevention and treatment strategies in aging populations
Sample-Efficient Reinforcement Learning Controller for Deep Brain Stimulation in Parkinson\u27s Disease
No full textDeep brain stimulation (DBS) is an established intervention for Parkinson\u27s disease (PD), but conventional open-loop systems lack adaptability, are energy-inefficient due to continuous stimulation, and provide limited personalization to individual neural dynamics. Adaptive DBS (aDBS) offers a closed-loop alternative, using biomarkers such as beta-band oscillations to dynamically modulate stimulation. While reinforcement learning (RL) holds promise for personalized aDBS control, existing methods suffer from high sample complexity, unstable exploration in binary action spaces, and limited deployability on resourceconstrained hardware. We propose SEA-DBS, a sample-efficient actor-critic framework that addresses the core challenges of RL-based adaptive neurostimulation. SEA-DBS integrates a predictive reward model to reduce reliance on real-Time feedback and employs Gumbel-Softmax-based exploration for stable, differentiable policy updates in binary action spaces. Together, these components improve sample efficiency, exploration robustness, and compatibility with resource-constrained neuromodulatory hardware. We evaluate SEA-DBS on a biologically realistic simulation of Parkinsonian basal ganglia activity, demonstrating faster convergence, stronger suppression of pathological beta-band power, and resilience to post-Training FP16 quantization. Our results show that SEA-DBS offers a practical and effective RL-based aDBS framework for real-Time, resource-constrained neuromodulation
The regeneration of metal components from the cathode of spent lithium-ion batteries using food wastes: a review
No full textPurpose – The purpose of this study is to review the regeneration of metal components from the cathode of spent lithium-ion batteries (LIBs) using food wastes. Design/methodology/approach – This review critically explored the use of common food wastes like orange peels (OPs), waste tea, macadamia shells and grape seed waste in regenerating the metal components from the cathodes of expended LIBs that have been pretreated. Additionally, this study explores the economic viability of using food waste for LIB recycling and the outlook for this innovative approach. Findings – The reductive potentials of certain food wastes: OPs, waste tea, macadamia shells and grape seed, were evaluated for their abilities to leach metal components from the cathodes of spent LIBs. OP yielded 80%–99% leaching of the important metals: Li, Co, Ni and Mn. Waste tea yielded leaching efficiencies of almost 100% for Ni, Li and Mn and about 90% for Co. Macadamia shell yielded 93.4% leaching of lithium. Grape seed showed that the efficiencies of 99% and 92% could be achieved for Li and Co, respectively. Research limitations/implications – This research/method suffers from variabilities in food composition, with amount of extractable useful components differing greatly among different food wastes. Also, the seasonal availability of some foods is also a critical concern. With these limitations, there comes a challenge regarding the scalability of this method. Originality/value – This paper presents an original comprehensive review of the regeneration of metal components from the cathode of spent LIBs using food wastes
Artificial Morphotropic Phase Boundary Associated with Nano-Clustering in Lead Zirconate – Lead Titanate Composite Films
No full textMany ferroelectrics, such as lead zirconate titanate, are widely known to exhibit excellent ferroelectric and piezoelectric properties near the morphotropic phase boundary (MPB), where two phases coexist in a solid solution form. Nano-clustering by aerosol deposition (AD) enables multiphase films to be prepared from distinct phases at room temperature. Herein, nano-clustered (NC) antiferroelectric xPbZrO3 (PZ) – ferroelectric (1-x)PbTiO3 (PT) films are fabricated to assess the possibility for artificial MPB (aMPB) behavior. This is done by comparing the composition-dependent ferroelectric and piezoelectric properties with those of solid solution (SS) Pb(Zrx,Ti1-x)O3 (PZT) films. The origin of the aMPB behavior in the NC PZ-PT film is verified through phase field modeling to be the internal local depolarization field generated in AFE. Both the SS PZT film and the NC PZ-PT film exhibited the largest dielectric constants (ɛr) of 820 and 650, and large signal piezoelectric constant (d33,f*) of 86 and 82 pm V−1, respectively, at a near-MPB composition (x = 0.5) compared to those of the Ti-rich (x = 0.1) and Zr-rich (x = 0.9) compositions, indicating aMPB behavior. The aMPB behavior induced by the nano-clustering is expected to be expanded to combinations of various material compositions that do not form solid solutions
FINDING ANTIPATTERNS ACROSS LANGUAGES WITH ABSTRACT SYNTAX TREES
No full textFinding antipatterns in student code is a difficult task that is useful for helping beginner programmers. Antipatterns are common mistakes that students make while writing code. Code critiquers are tools that find antipatterns and provide rich, immediate feedback to students, even when professors aren’t available. WebTA is a code critiquer that finds antipatterns using regular expressions (regex), error messages, and language-specific abstract syntax trees (ASTs). Each of these tools has obstacles to antipattern searching that are difficult to overcome. Regex is without context, limiting the patterns it can recognize. Additionally, even experienced users have difficulty reading and debugging regex. Error messages and language-specific abstract syntax trees must be implemented for each language. Error messages are also restricted to finding syntax and other error-related antipatterns. Finally, language-specific abstract syntax trees require rebuilding and redeploying in the current design of WebTA for each new or modified antipattern query. This thesis presents two tools and a collection of antipatterns to solve these issues. The first tool, C-Slam, is a multi-language code critic that uses a combination of AST searching and Python scripts to easily find antipatterns. The second, Universal Abstract Syntax Trees (UASTs), creates a rule-based system for converting ASTs into UASTs. This makes antipatterns that are identical between two languages need only one query to be matched. Finally, this collection of antipatterns is used to show the usefulness of UASTs and provide a starting point for the implementation of future code critiquers
Built Simulacra: Digital Media and Cultural Representation of Tourist Imaginaries in Online Reviews of Hotels and Casinos in Las Vegas
No full textDigital media, particularly user-generated online review platforms, play a central role in shaping how tourists imagine and experience cultural heritage in themed environments. Scholars have argued that twenty-first-century tourism emphasizes consumerist appeal over cultural or educational value. My inquiry focuses on the Luxor Hotel and Casino in Las Vegas, a resort modeled after ancient Egypt, as a paradigmatic case of hyperreality in postmodern tourism. I interrogate how this tourist destination deploys visual, narrative, and architectural cues to evoke a fantasy version of history, and how tourists, through online reviews, reproduce or contest these representations. I employ Baudrillard’s simulacra and hyperreality, Stuart Hall’s concept of cultural representation, and Edward Said’s critique of Orientalism to analyze how history is aestheticized, distorted, and commodified in the global tourism industry.
I employ critical discourse analysis and content analysis to identify the language and power relations embedded in TripAdvisor reviews, and also to show the thematic patterns in tourists’ narratives. I complement this with visual semiotic analysis of Luxor’s promotional imagery and incorporate autoethnographic reflections from my own visit. This allows for the interrogation of both textual and visual dimensions of the Luxor’s representation to capture the multifaceted nature of tourist meaning-making. While inter-rater reliability testing is applied in coding, computational visualization tools are used to map thematic patterns in the data.
The study foregrounds the interplay of spectacle, commodification, and cultural simulation in shaping tourist imaginaries. It finds that Luxor’s hyperreal spectacle commodifies cultural heritage, effectively transforming ancient Egyptian symbols into an idealized, consumable simulation. The resort’s symbolic architecture and digital narratives construct a simulacrum, an experience that detaches Egyptian icons from their original contexts and repackages them as an exotic spectacle for tourist consumption. To emphasize this dynamic, I introduced the Stages of Simulated Fulfillment, which traces how guests shift from recognizing artificiality to emotional investment; and the Orientalism and Cultural Flattening Matrix, which categorizes reactions from exotic enthusiasm to critical detachment. The study demonstrates how TripAdvisor user reviews and digital interfaces reinforce hyperreal expectations while revealing tensions between imagined fantasy and lived experience. Tourists’ narratives simultaneously celebrate Luxor’s grandeur and expose disillusionment when authenticity falters. These highlight the gap between tourist imaginaries and offer broader insights into the nexus of spectacle, media, and cultural representation in modern tourism