Search for: All records

A strip of square stamps can be folded in many ways such that all of the stamps are stacked in a single pile in the folded state. The stamp folding problem asks for the number of such foldings and has previously been studied extensively. We consider this problem with the additional restriction of fixing the mountain-valley assignment of each crease in the stamp pattern. We provide a closed form for counting the number of legal foldings on specific patterns of mountain-valley assignments, including a surprising appearance of the Catalan numbers. We describe results on upper and lower bounds for the number of ways to fold a given mountain-valley assignment on the strip of stamps, provide experimental evidence suggesting more general results, and include conjectures and open problems. Journal version 

Abstract The blue loop stage of intermediate mass stars has been called a “magnifying glass”, where even seemingly small effects in prior stages of evolution, as well as assumptions about stellar composition, rotation, and convection, produce discernible changes. As such, blue loops, and especially the existence and properties of Cepheids, can serve as a laboratory where feebly connected Beyond Standard Model particles such as axions can be gainfully studied. We undertake a careful study of the effects of these putative particles on the blue loop, paying close attention to the evolution of the core potential and the hydrogen profile. Our simulations, performed withMESA, place bounds on the axion-photon coupling using the galactic Cepheid S Mus, with dynamically-determined mass of 6M_⊙, as a benchmark. The effects of varying convective overshoot on the core potential and hydrogen profile, and the ensuing changes in the axion constraints, are carefully studied. Along the way, we explore the “mirror principle” induced by the hydrogen burning shell and contrast our results with those existing in the literature. Less conservative (but more stringent) bounds on the axion-photon coupling are given for a 9M_⊙model, which is the heaviest that can be simulated if overshoot is incorporated, and tentative projections are given for a 12M_⊙model, which is approximately the heaviest tail of the mass distribution of galactic Cepheids determined by pulsation models using Gaia DR2. Our main message is that the reliable simulation and observation (ideally, through dynamical mass determination) of massive Cepheids constitutes an important frontier in axion searches, challenges in modeling uncertainties in the microphysics of the blue loop stage notwithstanding. 

Water quality monitoring is essential for identifying risks to environmental and human health. Nitrate monitoring is of particular importance, as its anthropogenic point and nonpoint sources are common globally and have deleterious effects on water quality and usability as well as aquatic ecosystem health. Standard methods for assessing nitrate concentrations in water generally involve laboratory techniques, as methods available for field testing face significant tradeoffs between cost, precision, and portability. Given its relatively ubiquitous nature and the widespread regulation of nitrate pollution, it is a prime target for sensor development. The growing field of nanomaterials (e.g., nanoparticles, nanotubes, and 2-dimensional materials) offers the potential to eliminate these tradeoffs through a new generation of field-ready nitrate sensors. However, transitioning nano-sensors from the lab to the field remains challenging. In this perspective we examine the challenges of lab-to-field transition of nano-sensors for nitrate, highlighting the importance of a user-centered design approach under the framework of FOCUS (form factor, operational robustness, cost, user interface, and sensitivity). 

Abstract Understanding how foundation species recover from disturbances is key for predicting the future of ecosystems in the Anthropocene. Coral reefs are dynamic ecosystems that can undergo rapid declines in coral abundance following disturbances. Understanding why some reefs recover quickly from these disturbances whereas others recover slowly (or not at all) gives insight into the drivers of community resilience. From 2006 to 2010 coral reefs on the fore reef of Moorea, French Polynesia, experienced severe disturbances that reduced coral cover from ~46% in 2005 to <1% in 2010. Following these disturbances, coral cover increased from 2010 to 2018. Although there was a rapid and widespread recovery of corals, reefs at 17 m depth recovered more slowly than reefs at 10 m depth. We investigated the drivers of different rates of coral recovery between depths from 2010 to 2018 using a combination of time‐series data on coral recruitment, density, growth, and mortality in addition to field experiments testing for the effects of predation. Propagule abundance did not influence recovery, as the density of coral recruits (spat <6 months old) did not differ between depths. However, mortality of juvenile corals (≤5 cm diameter) was higher at 17 m, leading to densities of juvenile corals 3.5 times higher at 10 m than at 17 m depth. Yet, there were no differences in the growth of corals between depths. These results point to an early life stage bottleneck after settlement, resulting in greater mortality at 17 m than at 10 m as the likely driver of differential coral recovery between depths. We used experiments and time‐series data to test mechanisms that could drive different rates of juvenile coral mortality across depths, including differences in predation, competition, and the availability of suitable substratum. The results of these experiments suggested that increased coral mortality at 17 m may have been influenced by higher intensity of fish predation, and higher mortality of corals attached to unfavorable substratum. In contrast, the abundance of macroalgae, a coral competitor, did not explain differences in coral survival. Our work suggests that top‐down processes and substratum quality can create bottlenecks in corals that can drive rates of coral recovery after disturbance. 

Abstract Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians. 

This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination.