Search for: All records

The existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of affairs, although its empirical geographic distribution of demand virtually always exhibits positive SA. This latent redundant attribute information alludes to other tools that may well help to solve such spatial optimization problems in an improved, if not better than, heuristic way. Within a proof-of-concept perspective, this paper articulates connections between extensions of the renowned Majority Theorem of the minisum problem and especially the local indices of SA (LISA). The relationship articulation outlined here extends to the p = 2 setting linkages already established for the p = 1 spatial median problem. In addition, this paper presents the foundation for a novel extremely efficient p = 2 algorithm whose formulation demonstratively exploits spatial autocorrelation. 

Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget and human resources for traffic safety, delineating geographic areas optimally for police patrol areas is an important agenda item. This paper considers two p-median location models using segments on a street network as observational units on which traffic issues such as vehicle crashes occur. It also uses two weight sets to construct an enhanced delineation of police patrol areas in the City of Plano, Texas. The first model for the standard p-median formulation gives attention to the cumulative number of motor vehicle crashes from 2011 to 2021 on the major transportation networks in Plano. The second model, an extension of this first p-median one, uses balancing constraints to achieve balanced spatial coverage across patrol areas. These two models are also solved with network kernel density count estimates (NKDCE) instead of crash counts. These smoothed densities on a network enable consideration of uncertainty affiliated with this aggregation. The analysis results of this paper suggest that the p-median models provide effective specifications, including their capability to define patrol areas that encompass the entire study region while minimizing distance costs. The inclusion of balancing constraints ensures a more equitable distribution of workloads among patrol areas, improving overall efficiency. Additionally, the model with NKDCE results in an improved workload balance among delineated areas for police patrolling activities, thus supporting more informed spatial decision-making processes for public safety. 

The formation of brown carbon (BrC) in aqueous atmospheric aerosols is well-documented and often attributed to aldehyde-ammonia reactions. However, many studies have focused on individual aldehyde precursors, overlooking the complex composition of organic aerosols, which comprise a diverse mix of organic and inorganic compounds. To address this, a complex BrC system was investigated by generating aqueous atmospheric aerosol mimics containing glyoxal (Gly), glycolaldehyde (GAld), and ammonium sulfate. Structural analysis using supercritical fluid chromatography−mass spectrometry (SFC-MS) showed that adjusting the Gly:GAld mole ratio leads to variations in the composition and abundance of BrC products formed. Notably, aromatic heterocycles (e.g., imidazoles and pyrazines) as well as acyclic carbonyl oligomers were identified to form at different concentrations depending on the Gly:GAld mole ratio. UV−visible spectroscopy analysis demonstrated that light absorption in these mixed Gly + GAld + AS systems cannot be modeled as a simple weighted average of the Gly:GAld mole ratio; observed changes in light absorbance can be explained by compositional changes in solution. These observations indicate that cross-reactions are occurring between the Gly and GAld in solution, potentially leading to changes in the physical properties of the aerosol. Given the thousands of reactive compounds found in atmospheric aerosol, these findings could have important implications for our understanding of organic reactions within the aerosol. 

Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C₃or C₄), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjustedR² = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjustedR² = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjustedR² = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies (R² = 0.65 to <0.01), butR²was >0.80 for certain lineages and sites. The relationship between Vc_max, a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients. 

In this article, the use of additive-manufactured thermoplastics, specifically polylactic acid (PLA), to fabricate segments of wind turbine blades with core sandwich composites was verified through their compressive bucking performance, demonstrating their costeffectivness in manufacturing and transportation. A small wind blade was constructed by joining these segments to demonstrate their application potential in renewable energy technologies. The study’s focus was on the compressive buckling behavior of these fusion joined blades, particularly on the heterogeneity at the resistance welding bond line. An approach was adopted to integrate a hybrid of solid and cohesive elements within the cohesive zone modeling (CZM) framework using the Abaqus–Riks method. This allowed us to insert a thin layer of solid–cohesive elements at the bond line, enhancing the fidelity of our simulations. The validity of our numerical results was examined by comparing them with the surface strain field measured by digital image correlation (DIC) and assessing the compressive response. Furthermore, the applicability of classical Euler and Johnson formulas was evaluated in predicting buckling loads and modes. The Euler formula was found adequate for the first flexural buckling mode in beams with high slenderness ratios (≥12). Our findings demonstrate that the hybrid CZM approach effectively models the buckling behavior of fusion-joined beams, accommodating a range of slenderness ratios (6 to 18) and various buckling modes. This study provides insights into the structural analysis of fusion-joined components for potential applications of additive manufacturing in wind energy. 

Abstract Sequence-specific activation by transcription factors is essential for gene regulation^1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors^3,4and recruit co-activators to initiate transcription⁵. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient^6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500K_ds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g.enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, andin vitroaffinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation. 

Abstract Biased understanding of savanna biogeographyGrasslands and savannas exist across a wide range of climates. Mesic savannas, with highly variable tree densities, are particularly misunderstood and understudied in comparison to arid and semi‐arid savannas. North America contains historically extensive mesic savannas dominated by longleaf pine. Longleaf pine savannas may have once been the largest savanna type on North America, yet these ecosystems have been overlooked in global syntheses. Excluding these “Forgotten Ecosystems” from global syntheses biases our understanding of savanna biogeography and distribution. Evolutionary history and distinct climate of longleaf savannasWe assessed the evolutionary history and biogeography of longleaf pine savannas. We then harmonize plot data from longleaf savannas with plot data from valuable existing global synthesis of savannas on other continents. We show that longleaf pine savannas occur in a strikingly distinct climate space compared to savannas on Africa, Australia, and South America, and are unique in having wide ranging tree basal areas. Future directionsGrass‐dominated ecosystems are increasingly recognized as being ancient and biologically diverse, yet threatened and undervalued. A new synthesis of savanna ecosystems considering their full range of distributions is needed to understand their ecology and conservation status. Interestingly, the closest analogues to North American savannas and their relatives in Mesoamerica and the Caribbean may be Asian savannas, which also contain mesic fire‐driven pine savannas and have been similarly neglected in existing global syntheses. 

Abstract Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep-learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence–ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes.