26419 research outputs found
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Birdwatchers Across North America Tend to Survey Birds in the Morning for No Apparent Reason
No full textBird surveys are typically conducted in the early morning hours. This time of day is chosen since it presumably reflects the greatest activity of birds: many species are most active around sunrise, making them easily detected. While most bird research calls for strict and standardized protocols for when to survey for birds, such approaches are often labor-intensive and limited to very small spatial scales. A community-based approach (often termed “citizen science”) offers a data-intensive alternative to conventional data collection. This community approach involves gathering data from volunteers who submit observations of birds that they encounter at any point, along with information that describes their sampling effort. Such volunteers are not given any specific instructions as to how and when to collect bird data. In this study, I used observations submitted to eBird—a popular web-based platform where more than 800 thousand birdwatchers from Canada and the U.S. have contributed bird sightings between 2010 and 2023. I tested whether observers were biased on when they were birdwatching. I estimated a time-of-day bias as a deviation of estimated kernel density of solar time for \u3e4 million observations from \u3e30,000 locations across Canada and the U.S. relative to a simulated uniform timing distribution. I found a substantial time-of-day bias across observations wherein a large proportion were submitted immediately after local sunrise. Night observations, however, were scarce and represented only a negligible part of the dataset. In fact
Green Chelation Strategy for Deashing of Algal Biomass
Full text linkThis study investigated a green chelation strategy for deashing algal biomass using nitrilotriacetic acid (NTA) and deionized water (DI) to enhance its suitability for biofuel and bioproduct applications. Solid-state algal turf scrubber (SS ATS), green algal turf scrubber (ATS), and Scenedesmus were analyzed, with Scenedesmus selected for detailed evaluation due to its high ash removal efficiency. The objective was to optimize a purification process that minimizes ash and heavy metal content while preserving biochemical integrity. Algal biomass underwent sequential washing with DI, NTA, and NTA+DI under varying temperatures (90-130 °C). Analytical techniques including Fourier Transform Infrared (FTIR) spectroscopy, Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), and CHN (carbon, hydrogen, nitrogen) elemental analysis were employed to assess changes in ash content, metal ion concentration, elemental composition, and biochemical properties. The NTA+DI treatment was the most effective, lowering the ash content in Scenedesmus from 15.2% to 3.8% and decreasing lead (Pb) and copper (Cu) levels below detectable limits. Ash removal was greatly aided by temperature reaching a high efficiency of 83.07% at 130 °C owing to enhanced calcium, magnesium, and potassium solubilization and chelation. Successive NTA recycling led to a decline in efficiency by the third cycle, indicating chelating agent saturation. Despite ash reduction, the ultimate analysis showed minimal changes in CHN composition (C: 45.2%, H: 6.0%, and N: 7.1%), ensuring the retention of organic matter. These findings established NTA+DI treatment as an effective and sustainable method for deashing algal biomass while maintaining its biochemical integrity. This optimized process enhances the feasibility of Scenedesmus-derived biofuels improving combustion efficiency and reducing inorganic fouling in biorefineries and thermochemical conversion systems
01 - Design Of Ion-Sieve For Selective Adsorption Of Lithium From Geothermal Brine
Full text linkThe growing demand for lithium-ion batteries necessitates efficient and sustainable lithium extraction technologies. Geothermal brines provide an alternative lithium source; however, their complex ionic composition challenges selective lithium recovery. Hydrogen manganese oxides (H4Mn4.5O12) are widely studied as lithium-ion sieves due to their high selectivity for lithium ions. Still, its structural instability and manganese dissolution during adsorption-desorption cycles limit long-term performance. This study introduced zirconium (Zr) doping into the spinel-type Li4Mn5O12 precursor through a one-step calcination process (450 °C for 24 hours at 10 °C min-1), ensuring an energy-efficient synthesis process of Li4Mn4.5Zr0.5O12 (LMZO) matrix. The resulting spinel H4Mn4.5Zr0.5O12 (HMZO), after acid activation, underwent comprehensive characterization employing several analytical techniques, including Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) surface area, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS)The adsorption behavior of HMZO was systematically investigated under varying parameters, including solution pH, temperature, contact time, initial lithium concentration, and adsorbent dosage. Results revealed that optimal lithium uptake occurred at pH 11 and 70 °C, with a maximum adsorption capacity of 35 mg/g when 0.017 g of adsorbent was used in a 50 mL lithium chloride solution. The adsorption process conformed to the pseudo-second-order kinetic model, suggesting chemisorption involving ion exchange between Li⁺ and H⁺. The Freundlich isotherm model best described the equilibrium data, indicating heterogeneous surface adsorption and multilayer formation. Cycling experiments demonstrated good reusability, with an average lithium adsorption capacity of 25.89 mg/g after five adsorption-desorption cycles. These findings confirm the enhanced adsorption efficiency, operational resilience, and potential scalability of HMZO for lithium recovery from high-salinity environments. The study offers promising insights into advanced lithium-selective materials for sustainable resource extraction
09 - Critical Hit!: Using Virtual Reality to Promote Philosophy and Critical Thinking
No full textThe Critical Hit! project uses cutting-edge virtual reality technologies to introduce users philosophical thinking about ethical dilemmas. For this presentation, participants will play through a VR version of the trolley problem philosophical thought experiment, a life-and-death ethical dilemma. This experience is followed by a debriefing conversation with other participants and a facilitator. Reflecting on one’s own beliefs and interrogating those beliefs by engaging with diverse alternative viewpoints is at the foundation of much work in the humanities. Philosophers use thought experiments to aid in such reflection and interrogation. For example, thought experiments can reveal tensions in a person’s concept of “right” or “good” by introducing them to alternative ways of thinking. Once a tension has been revealed, a person is better positioned to adjust their thinking and engage in more constructive conversations across diverse belief sets going forward. This process of reflection and interrogation, however, can be arduous for the uninitiated. Presenting thought experiments in VR allows a general audience to explore philosophical ideas in fun, safe, and controlled environments in order to enjoy the benefits of self-discovery and critical thinking
TAMOS: Task-Aware Multi-Agent Orchestrator System
Full text linkLarge language models (LLMs) are increasingly at the core of multi-agent systems (MAS). However, the high resource demand, error propagation, and lack of adaptive evaluation mechanisms pose significant challenges in deploying these agentic solutions at scale. To address these concerns, this research proposes a Task-Aware Multi-Agent Orchestrator System designed to refine the agentic framework, categorizing tasks autonomously, assigning specialized evaluation datasets, and balancing token usage against functional effectiveness. This approach underscores robust data management, including AsyncHow, Mosaic AI, and Synthetic Preference Optimization (PO) corpora. Each dataset targets specific dimensions of agent performance, such as dynamic task decomposition and tool integration (AsyncHow), quality-cost-latency tradeoffs (Mosaic AI), and iterative preference refinement (PO). By classifying tasks using hierarchical clustering and LLM-driven intent detection, the framework automatically aligns each task with the most relevant evaluation dataset and metrics. An integrated evaluation pipeline leverages LLM judges for correctness and groundedness assessments, computing token-based cost-latency metrics and aggregating these results into multi-objective optimization models. By evaluating agents along Pareto frontiers of performance and cost, the framework enables casual decision-making, particularly in high-stakes applications where resource constraints and reliability must be balanced. In pursuit of continual refinement, feedback loops guide iterative improvements to agent configurations, leveraging meta-level techniques like Llama 3.2-3B for reasoning over performance outcomes. This multi-agent reinforcement learning (MARL) engine maximizes task success rates and proactively minimizes resource consumption, contributing to sustainable, real-world deployment scenarios. Thus, TAMOS enables alignment mechanisms and transparent reporting of cost-latency tradeoffs to address ethical concerns around bias, safety, and accountability
The Combined Impact of Alcohol Consumption and Smoking Habits on Blood Pressure Control Among Hypertensive Adults: An Analysis of NHANES 2021-2023 Data
No full textBackground: Hypertension, a leading risk factor for cardiovascular disease, affects nearly half of U.S. adults. While lifestyle factors such as alcohol consumption and smoking are independently linked to hypertension, their combined impact on blood pressure control remains underexplored. This study investigates the individual and interactive effects of alcohol use and smoking habits on hypertension management.
Method: A cross-sectional analysis using the data from the 2021-2023 National Health and Nutrition Examination Survey (NHANES) was conducted on 3,756 hypertensive adults, categorizing participants by smoking status (current, former, never smokers) and alcohol consumption patterns (never, occasional, heavy drinkers). Hypertension was classified into Stage I (130-139/80-89 mmHg) and Stage II (≥140/90 mmHg). Logistic regression models adjusted for demographic, socioeconomic, and health-related covariates were employed to assess associations.
Results: Results revealed that heavy alcohol consumption was a robust predictor of both Stage I (OR = 1.481, 95% CI: 1.250–1.756) and Stage II hypertension (OR = 1.399, 95% CI: 1.126–1.737), even after adjustment for confounders. Smoking showed weaker associations, with current smokers exhibiting elevated odds of Stage I hypertension in unadjusted models (OR = 1.382, 95% CI: 1.121–1.704), which diminished after adjustment. The combined effects of heavy drinking and current smoking suggested a potential amplification of hypertension risk, though this interaction was not statistically significant. Notably, heavy drinkers and current smokers exhibited a higher prevalence of Stage II hypertension, emphasizing the compounded risk of these behaviors.
Conclusion: These findings underscore the critical role of heavy alcohol consumption in hypertension management and highlight the need for targeted interventions to reduce alcohol intake and promote smoking cessation. Clinicians should adopt a holistic approach to address multiple high-risk behaviors in hypertensive patients, as these behaviors may interact to exacerbate cardiovascular risk. Future research should explore longitudinal and mechanistic insights to better understand the interplay between smoking, alcohol use, and hypertension, particularly through biomarkers of inflammation and oxidative stress. Such insights are essential for developing tailored public health strategies and clinical guidelines to improve cardiovascular outcomes and enhance hypertension management in at-risk populations
Examining Throughput in Academic Medical Center Inpatient Obstetric Units, A Comparative Quality Improvement Project
No full textABSTRACT
Problem: An Academic Medical Center in the southeast is delivery records and growing. With increased patient volumes, there is no additional space to place patients and rooms quickly fill. Efficient patient throughput is vital to minimize waiting, enhance satisfaction, and uphold safety. Recently, there have been delays in transfers from Labor and Delivery (L&D) to the Mother and Infant Unit (MIU), as well as discharge delays from MIU. The actual throughput data and staff perceptions of throughput were unknown.
Purpose: The purpose of this quality improvement (QI) project was to collect and analyze the throughput data for inpatient OB units and determine staff perceptions.
EBP Question: Will analysis of the variables of time/day/shift, patient volume, and staff perception provide direction for improving patient throughput including discharges and transfers between inpatient obstetric units?
Methods: This QI project was non-experimental causal comparative, with descriptive questions. The variables were compared to determine the relationship with patient throughput. The project occurred on L&D and MIU. The individuals for the project are employees from L&D or MIU that voluntarily completed the anonymous survey excluding nurse leadership or floated staff, with a total of 66 participants. The throughput metric data from the EHR was declassified and included aggregate data from a convenience sample of patients admitted and transferred to MIU, excluding patients who did not transfer or were not postpartum, with a total of 198 records meeting criteria. The data metrics included times delivery, ready to transfer, arrival to MIU, and discharge order to discharge, room clean turn over time, patient transport times and daily census. The data were collected over a 30-day period and then analyzed.
Outcomes: Data was analyzed and compared to determine the relationships between the variables and to determine the barriers. An outcome of the project was strategic future improvement recommendations to enhance throughput or patient flow. Subsequent QI projects implementing the recommendations should be completed.
Significance: This project is important as it made recommendations for future quality improvement projects as well as a throughput framework in the EHR for the OB units. The future QI projects should increase efficiency of throughput to minimize risks, improve satisfaction, and accommodate the high patient volumes. Other organizations that have separate postpartum care units can also use the recommended findings to implement changes to enhance throughput, or the survey to determine the bedside expert’s perception, following the high reliability organization principles of sensitivity to operations and deference to expertise
The Impact of Implanting Cr+ Ion onto ALD PbTe Thin Films
No full textInherently the synthesis of semiconducting materials by Atomic Layer Deposition (ALD) produces only intrinsic undoped films which require the introduction of small amounts of impurities for doping to change them into extrinsic semiconductors. Apart from various in-situ diffusion doping techniques like delta doping during the ALD process, post deposition doping by ion implantation affords the best control of dose and doping profile. The present study investigates the impact of Cr+ ion implantation onto Lead Telluride (PbTe) thin films to improve their thermoelectric figure of merit. The implantation was accomplished with 180 keV Chromium ions at a given fluence to reach a desired 1% Cr doping level. These thermoelectric PbTe thin films have been synthesized on silicon substrates covered with native oxide by ALD with the growth temperature during ALD range from 130 centidegree to 170 centidegree. Several physical characterization techniques among them SEM and EDS have been employed to determine the ALD PbTe thin film characteristics before and after Chromium in implantation. X- ray diffraction analysis reveals that the films exhibit a polycrystalline structure with simple cubic crystallites. Atomic force microscopy analysis was employed to determine the surface properties of the films, including surface topology, root mean square (RMS) roughness, grain height, and average size. For the electrical characterization we report the effects of the ion implantation on the resistivity ρ(T) as a function of temperature, the electrical conductivity, the Hall mobility, and the Seebeck coefficient
Enhancing LLM Capability to Generate a Problem Statement in Mission Engineering Using RAG
No full textThe first phase of the Mission Engineering (ME) process, as outlined in the Mission Engineering Guide (MEG), is critical for defining the Mission Problem or Opportunity. This phase involves establishing the mission\u27s purpose, formulating investigative questions, and identifying decision needs to guide subsequent analysis and system integration(DoD MEG 2.0, 2023). However, traditional approaches to problem definition often rely heavily on manual processes, which can be time-intensive and prone to gaps in knowledge representation.
This research explores the application of Retrieval Augmented Generation (RAG) to enhance the capabilities of large language models (LLMs) in generating precise and actionable problem statements during this critical ME phase. By leveraging RAG\u27s ability to combine retrieval-based grounding with generative AI capabilities, I aim to dynamically incorporate real-time, domain-specific knowledge from curated databases and external sources into the problem-framing process. This approach ensures that problem statements are not only data-driven but also aligned with operational realities and decision-makers needs.
RAG is an advanced AI framework that combines the generative capabilities of LLMs with real-time retrieval of external, domain-specific, or up-to-date knowledge, enhancing the accuracy, relevance, and contextual understanding of AI-generated responses by grounding them in factual data. RAG integrates retrieval and generation by fetching relevant information from external sources such as databases, documents, or the web before generating a response, ensuring outputs are informed by accurate and current data rather than relying solely on the static knowledge embedded in the LLM. This approach minimizes hallucinations, where LLMs generate plausible but incorrect information by grounding responses in retrieved facts. Additionally, RAG enables AI systems to provide real-time and domain-specific knowledge by accessing updated external knowledge bases, allowing specialization in areas such as law or medicine without requiring extensive retraining (Lewis et al., 2020).
Human-Centered Design (HCD) is a design approach that prioritizes user needs and experiences, focusing on understanding and addressing core problems rather than symptoms (JND, 2019). It involves iterative testing and refinement to ensure solutions meet user requirements effectively (Gillingham et al., 2023). Key principles include empathy, problem-solving, and user collaboration throughout the design process (Univ. of Min./CCAPS, 2024).
The proposed methodology integrates RAG with Human-Centered Design (HCD) principles to ensure that end-user inputs and stakeholders\u27 collaboration are central to defining mission purpose and investigative questions. The expected outcomes include a structured framework for using RAG-enhanced LLMs in ME workflows, improved efficiency in generating problem statements, and enhanced alignment between operational requirements and system capabilities. This research contributes to advancing mission engineering practices by demonstrating how AI-driven tools can streamline early-phase processes while maintaining a focus on usability, adaptability, and mission success
Evaluating Normalization Methods for Seagrass Mapping with Supervised Classification of PlanetScope Imagery
No full textAccurate mapping of submerged aquatic vegetation (SAV) is crucial for monitoring coastal ecosystems. However, this process is complicated by the fact submerged objects exhibit low reflectance due to significant absorption and scattering of light within the water column, resulting in a dark appearance. In contrast, terrestrial surfaces generally have higher reflectance, as they are not subject to the attenuating effects of water and instead reflect a greater proportion of incident sunlight. This imbalance creates a limited dynamic range among water pixels, complicating classification of submerged targets. Normalization of reflectance values in each spectral band may mitigate these issues by increasing dynamic range across submerged targets and equalizing values across the time-series of imagery, improving classification accuracy. This study evaluates the effectiveness of two normalization methods in improving the supervised classification of Planet SuperDove 8-band imagery for SAV detection in Pocomoke Sound, a mesohaline region of the Chesapeake Bay. Analysis focuses on summer imagery from 2020 to 2024, aligning with the peak seagrass growing season. To assess the impact of normalization, the frequency distribution of pixel values was examined in each band before and after applying min-max normalization and 1st/99th percentile normalization, first masking out land and bright areas to enhance contrast, then normalizing the remaining dark pixels to improve classification performance. In addition to normalization, the study involved testing two indices—OSW (Optically Shallow Water Index based on the green, red, and near-infrared bands) and Hue (represents spectral variation as a single color value instead of three RGB channels) —to determine whether they enhance classification accuracy by better distinguishing SAV from surrounding water and other features. Effective normalization has the potential to enable trained classification models to be applied to future images of the same site without requiring additional training data. The findings from this study may provide valuable insights into preprocessing strategies for deep-learning-based SAV mapping, contributing to the development of automated and scalable methods for coastal ecosystem monitoring