Volume Of Convex Hull Research Articles

Measuring exploration: evaluation of modelling to generate alternatives methods in capacity expansion models

Abstract As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

Abstract PO2-02-11: Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment

Abstract Background: Neoadjuvant therapy (NAT) has become a standard-of-care (SOC) for patients diagnosed with locally advanced or early-stage breast cancer. A critical step towards optimizing treatment selection and coordinating surgical care is to estimate the tumor’s response to NAT. While pathological complete response (pCR) is commonly used to measure NAT effectiveness, patients who do not achieve pCR still benefit from NAT depending on the tumor’s response. A reduction in tumor extent could alter the surgical approach from mastectomy to breast conserving therapy and improve recurrence and overall survival. In this study we used pre-treatment imaging data to provide a comprehensive prediction of clinically relevant metrics including tumor volume, tumor convex hull volume, and spatially derived metrics; this research represents an important advancement in predicting NAT effectiveness. Methods: 262 breast cancer patients from 5 institutions who underwent NAT with pre- and post-NAT dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were included in this study. Each MRI was automatically segmented using a convolutional neural network with manual edits and verification. Radiomic features were extracted from the pre-NAT MRI and tumor segmentation. Significant radiomic features were selected based on their correlation with post-NAT volume, feature-to-feature correlation, and LASSO regression. Altogether, 12 and 17 radiomic features were selected for volume and convex hull volume prediction, respectively. The selected features were inputted into a Huber loss regression model to predict either the post-NAT tumor volume or the convex hull volume (surgical excision volume). To enable spatial and morphological comparison between the predicted and post-NAT tumor, an erosion model was developed in which the pre-NAT and post-NAT MRIs were co-registered, and the pre-NAT tumor was symmetrically eroded until its volume matched the predicted volume. Clinically relevant metrics such as the distance from the tumor to the nipple, skin, and chest were computed to assess the morphological and spatial prediction capabilities of the model. All models were tested on patients from cohort holdout sets (n=110) unseen during training. Results: The volume prediction analysis yielded promising results, with an R^2 value of 0.62 for post-NAT tumor volume prediction and an R^2 value of 0.56 for post-NAT tumor convex hull volume prediction. The mean volume and convex hull volume error was 1.1 cc and 1.3 cc, respectively. Table 1 provides an overview of the morphological and spatial comparisons between the predicted and post-NAT tumors. Overall, the predicted tumors demonstrated close agreement with their post-NAT counterparts. Conclusion: This study demonstrates a successful approach towards estimating post-NAT tumor volume, convex hull volume, and morphological and spatial characteristics. By providing reliable estimates of post-NAT tumor characteristics using SOC pre-NAT data, our predictive models enable personalized treatment planning and patient stratification to optimize patient care. Table 1. Predicted vs. post-NAT tumor comparison. Results demonstrate accurate predictions for clinically relevant distance metrics. Citation Format: Bradley Feiger, John Pfeiffer, John Cole, Anuja Antony, Joseph Peterson. Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-11.

Abstract PO2-02-10: Predicting the feasibility of breast conserving surgery using pre-treatment standard of care DCE-MRI: a novel clinical decision support tool for breast cancer surgical planning

Abstract Background: The decision-making process regarding breast cancer treatment options, specifically breast conserving surgery (BCS) versus mastectomy, is a complex and sensitive matter that takes into account the patient's preferences and their eligibility for BCS. A range of anatomical factors, such as tumor size, breast size, and proximity of the tumor to the skin, can influence the surgeon's recommendation regarding BCS or mastectomy. While mastectomy may be the only viable option for patients with large tumors, those with smaller tumors often have the opportunity to collaboratively decide with their surgeon on the most appropriate approach to ensure oncologic success and minimize potential cosmetic deformity. To facilitate this decision-making process, we developed a tool capable of providing informed recommendations on BCS or mastectomy based on standard of care imaging patient data. By integrating these recommendation capabilities, we aim to enhance the decision-making process and improve patient outcomes in breast cancer treatment. Methods: To assist surgeons and patients in determining a patient’s eligibility for breast conserving surgery (BCS), we developed the BCS Feasibility Score. The BCS Feasibility Score is generated from a binary classifier machine learning model and is representative of the probability that a given patient could receive BCS. The score is based solely on the output from TumorSight Viz, a software platform that uses only pre-treatment T1-weighted DCE MRI and applies a suite of deep learning algorithms to segment the patient’s disease and the surrounding breast tissues. From the multi-tissue segmentation generated using TumorSight Viz, we extracted 25 spatial morphology features to use in the model development process. The training and testing sets were a subsample of a publicly available dataset (Saha, et al, 2018). We selected patients that received either BCS or mastectomy and had a viable T1-weighted DCE MRI. Bi-lateral cases were excluded. The total training/testing set consisted of n = 749 cases, 363 of which received mastectomy and 386 who received BCS. Before model training occurred, we split 20% of the sample to use as the test set. We then trained a random forest classifier across a range of pre-set hyper-parameters, using a total of 25 features as inputs, and using 5x cross-validation in the training set. We then assessed model performance in the testing set only. Results: In the test set, 76 cases received BCS versus 74 that received mastectomy. We successfully predicted BCS in 56 out of 76 cases (74%), and mastectomy in 46 out of 74 cases (62%). Overall, we observed an AUC = 0.76 and an F1 score = 0.66, indicating moderate to strong model performance. The most important features in the model, as measured by SHAP values, included the axis aligned tumor longest dimension (mm), the closest distance between the tumor and the nipple (mm), the volume of the tumor convex hull (mL; dilated uni-centrically), and the ratio of tumor convex hull volume to breast volume. We tested the model’s performance in a fully independent holdout validation set of 579 cases. Within this validation set, 335 patients received BCS versus 244 received mastectomy. We successfully predicted BCS in 254 out of 335 cases (76%), and mastectomy in 149 out of 244 cases (61%). Overall, we observed an AUC = 0.75 and an F1 score = 0.63. Validation set performance mirrored results observed in the test set, indicating strong generalizability. In the validation set, comparing patients who underwent BCS to those who underwent mastectomy, we observed significant differences across all four of the most predictive features. Conclusion: TumorSight Viz and the BCS Feasibility Score help empower both patients and clinicians with information and tools to facilitate surgical planning decisions. Citation Format: John Pfeiffer, Bradley Feiger, Anuja Antony, Joseph Peterson. Predicting the feasibility of breast conserving surgery using pre-treatment standard of care DCE-MRI: a novel clinical decision support tool for breast cancer surgical planning [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-10.

Using ZY1-02D satellite hyperspectral remote sensing to monitor landscape diversity and its spatial scaling change in the Yellow River Estuary

Monitoring and assessing wetland diversity is crucial for its accurate preservation. Hyperspectral satellites have been proven effective for detailed investigations of plant diversity in many places. However, it's unclear whether spectral diversity invert landscape diversity, and whether the inversion accuracy varies with spatial scale. In this study, the ZY1-02D hyperspectral remote sensing images of the Yellow River Estuary were supervised and classified by the support vector machine. Then, the landscape diversity indices (i.e., community richness, Shannon-Wiener index, Simpson index, and Pielou index) and spectral diversity indices (i.e., coefficient of variation, convex hull volume, and eight vegetation indices) were calculated. A random forest model was used to predict landscape diversity by using spectral diversity. The spatial scale relationship between spectral diversity and landscape diversity were explored lastly. Our results showed that the overall accuracy of plant community classification in the Yellow River Estuary was 91.53 %, with a Kappa coefficient of 0.90. Spectral diversity had the best inversion accuracy on the Shannon-Wiener index (14 ∼ 57 %, average = 38 %), while the intermediate on Pielou index (3 ∼ 56 %, average = 30 %) and community richness (2 ∼ 48 %, average = 30 %), but the lowest on the Simpson index (2 ∼ 43 %, average = 16 %). The inversion accuracy of landscape diversity index increased first and then stabilized with the increase of scales, reaching stability at a sampling size of 2880 m × 2880 m. Our results indicated that ZY1-02D hyperspectral data can be used to monitor spatial changes of landscape diversity in wetland systems. However, its accuracy is affected by diversity index type and spatial scaling effects. Our findings provide a new perspective for the conservation and management of large-scale wetland landscape diversity.

Tree species diversity mapping from spaceborne optical images: The effects of spectral and spatial resolution

AbstractIncreasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests.

Genetics of canopy architecture dynamics in photoperiod‐sensitive and photoperiod‐insensitive sorghum

AbstractCanopy architecture traits are associated with productivity in sorghum [Sorghum bicolor (L.) Moench], and they are commonly measured at the time of flowering or harvest. Little is known about the dynamics of canopy architecture traits through the growing season. Utilizing the ground‐based high‐throughput phenotyping system Phenobot 1.0, we collected stereo images of a photoperiod‐sensitive and a photoperiod‐insensitive population over time to generate three‐dimensional (3D) representations of the canopy. Four descriptors were automatically extracted from the 3D point clouds: plot‐based plant height (PBPH), plot‐based plant width (PBPW), plant surface area (PSA), and convex hull volume (CHV). Additionally, genotypic growth rates were estimated for each canopy descriptor. Genome‐wide association analysis was performed on individual timepoints and the growth rates in both populations. We detected genotypic variation for each of the four canopy descriptors and their growth rates and discovered novel genomic regions associated with growth rates on chromosomes 1 (PBPH, CHV), 3 (PBPH), 4 (PBPH, PBPW), 5 (PBPH), 8 (PSA), and 9 (PBPW). These results provide new knowledge about the genetic control of canopy architecture, highlighting genomic regions that can be targeted in plant breeding programs.

An integrated method for phenotypic analysis of wheat based on multi-view image sequences: from seedling to grain filling stages.

Wheat exhibits complex characteristics during its growth, such as extensive tillering, slender and soft leaves, and severe organ cross-obscuration, posing a considerable challenge in full-cycle phenotypic monitoring. To address this, this study presents a synthesized method based on SFM-MVS (Structure-from-Motion, Multi-View Stereo) processing for handling and segmenting wheat point clouds, covering the entire growth cycle from seedling to grain filling stages. First, a multi-view image acquisition platform was constructed to capture image sequences of wheat plants, and dense point clouds were generated using SFM-MVS technology. High-quality dense point clouds were produced by implementing improved Euclidean clustering combined with centroids, color filtering, and statistical filtering methods. Subsequently, the segmentation of wheat plant stems and leaves was performed using the region growth segmentation algorithm. Although segmentation performance was suboptimal during the tillering, jointing, and booting stages due to the glut leaves and severe overlap, there was a salient improvement in wheat leaf segmentation efficiency over the entire growth cycle. Finally, phenotypic parameters were analyzed across different growth stages, comparing automated measurements of plant height, leaf length, and leaf width with actual measurements. The results demonstrated coefficients of determination ( ) of 0.9979, 0.9977, and 0.995; root mean square errors (RMSE) of 1.0773 cm, 0.2612 cm, and 0.0335 cm; and relative root mean square errors (RRMSE) of 2.1858%, 1.7483%, and 2.8462%, respectively. These results validate the reliability and accuracy of our proposed workflow in processing wheat point clouds and automatically extracting plant height, leaf length, and leaf width, indicating that our 3D reconstructed wheat model achieves high precision and can quickly, accurately, and non-destructively extract phenotypic parameters. Additionally, plant height, convex hull volume, plant surface area, and Crown area were extracted, providing a detailed analysis of dynamic changes in wheat throughout its growth cycle. ANOVA was conducted across different cultivars, accurately revealing significant differences at various growth stages. This study proposes a convenient, rapid, and quantitative analysis method, offering crucial technical support for wheat plant phenotypic analysis and growth dynamics monitoring, applicable for precise full-cycle phenotypic monitoring of wheat.

Hyperbolic regularization-PointNet++: Automated three-dimensional soil particle roundness classification

Particle roundness significantly affects the macro-mechanics of granular soils. Nevertheless, current three-dimensional(3D) computational geometry (CG) -based methods for soil particle roundness classification exhibit inefficiency and struggle with defective particles. To overcome these limitations, this paper proposed a deep learning-based approach for intelligent soil particle classification to automatically assess roundness. We labeled 2400 soil particle point clouds into six classes: well rounded, rounded, subrounded, subangular, angular, and very angular. Using the labeled dataset, we trained the PointNet++ model with a novel hyperbolic regularizer, extracted hierarchical roundness features, and built a roundness classifier. The achieved automatic classification accuracy is 92.19%. Moreover, the classification results are similar to the 3D CG-Manual Charting method in terms of quantity, volume, surface area, and convex hull volume. Notably, our method overcomes the limitation of 3D CG's inability to classify soil particles with void defects. Moreover, it shows a classification speed of approximately 77 times faster than 3D CG.

A comparison of dissection and 3D approaches to estimate muscle physiological cross-sectional area, validated by in vivo bite forces.

Performance traits such as bite forces are crucial to fitness and relate to the niche and adaptation of species. However, for many insects it is not possible to directly measure bite forces because they are too small. Biomechanical models of bite forces are therefore relevant to test hypotheses of adaptation in insects and other small organisms. Although such models are based on classical mechanics, combining forces, material properties and laws of levers, it is currently unknown how various models relate to bite forces measured in vivo. One critical component of these models is the physiological cross-sectional area (PCSA) of muscles, which relates to the maximum amount of force they can produce. Here, using the grasshopper Schistocerca gregaria, we compare various ways to obtain PCSA values and use in vivo measurements of bite forces to validate the biomechanical models. We show that most approaches used to derive PCSA (dissection, 3D muscle convex hull volume, muscle attachment area) are consistent with the expected relationships between PCSA and bite force, as well as with the muscle stress values known for insects. The only exception to this are PCSA values estimated by direct 3D muscle volume computation, which could be explained by noisy variation produced by shrinkage. This method therefore produces PCSA values which are uncorrelated to in vivo bite forces. Furthermore, despite the fact that all other methods do not significantly differ from expectations, their derived PCSA values vary widely, suggesting a lack of comparability between studies relying on different methods.

Seedscreener: A novel integrated wheat germplasm phenotyping platform based on NIR-feature detection and 3D-reconstruction

Crop seed phenomics provides a new operational basis for breeders. However, a scarcity of seed phenotypic characterization, especially of the single seed kernels, severely restricts their use in plant breeding. In this study, we focus on designing and deploying a prototype for automatic and high throughput seed phenotyping. The novel setup, Seedscreener, allows simultaneous RGB imaging and NIR spectrum analysis pipeline to evaluate single kernel 3D morphologies and internal biochemicals. With the assistance of a data acquisition device centered on a near-infrared spectrometer and an industrial camera, as well as an automated acquisition software under the QT development framework, Seedscreener has the ability to acquire both endophenotype and exophenotype traits of single wheat kernels simultaneously. The developed biochemical endophenotype traits prediction model can forecast the protein content (PC), starch content (SC) and gluten content (GC) of wheat kernel by the full-band absorbance spectra. The developed morphological exophenotype traits extraction model based on Marching Cubes (MC) algorithm can calculate the wheat grain length (GL), grain width (GW), grain thickness (GT), grain surface area (GSA) and convex hull volume (CHV) using the three-dimensional visual hull constructed by the lateral profile information of the wheat grain. The results show that the platform could reach 94% success rate of single wheat phenotypic data collection, the average accuracy of endophenotype traits prediction is 0.85, the average accuracy of exophenotype traits extraction is 0.93, and the working flux of the platform is about 90 grains/h. Compared with existing individual wheat kernels phenotyping equipment, Seedscreener could obtain both endophenotype and exophenotype characteristics of wheat seeds with high throughput and precision.

Dynamical Analyses Show That Professional Archers Exhibit Tighter, Finer and More Fluid Dynamical Control Than Neophytes.

Quantifying the dynamical features of discrete tasks is essential to understanding athletic performance for many sports that are not repetitive or cyclical. We compared three dynamical features of the (i) bow hand, (ii) drawing hand, and (iii) center of mass during a single bow-draw movement between professional and neophyte archers: dispersion (convex hull volume of their phase portraits), persistence (tendency to continue a trend as per Hurst exponents), and regularity (sample entropy). Although differences in the two groups are expected due to their differences in skill, our results demonstrate we can quantify these differences. The center of mass of professional athletes exhibits tighter movements compared to neophyte archers (6.3 < 11.2 convex hull volume), which are nevertheless less persistent (0.82 < 0.86 Hurst exponent) and less regular (0.035 > 0.025 sample entropy). In particular, the movements of the bow hand and center of mass differed more between groups in Hurst exponent analysis, and the drawing hand and center of mass were more different in sample entropy analysis. This suggests tighter neuromuscular control over the more fluid dynamics of the movement that exhibits more active corrections that are more individualized. Our work, therefore, provides proof of principle of how well-established dynamical analysis techniques can be used to quantify the nature and features of neuromuscular expertise for discrete movements in elite athletes.

The Effect of Time Pressure on Visual Search Eye Tracking Metrics in Virtual Reality

The study of visual search has focused on various guiding factors, but less attention has been given to how environmental factors affect visual search in virtual reality (VR). The visual search literature has primarily been based on 2D laboratory tasks, which lack the complexity of real-life search tasks. Thus, this study studies the effect of time pressure on visual search in a naturalistic environment. To do that, participants were immersed in a virtual living room using VR and tasked with finding objects under a time constraint. Eye gaze data was collected, and convex hull volumes and scanning rates were calculated and analyzed. The results show that time pressure reduced convex hull volume and increased scanning rate, indicating faster search speed and a gaze tunneling effect. Understanding how time pressure affects visual search can help improve training strategies and design better user interfaces for visual search critical domains.

Variation in Pheidole nodus (Hymenoptera: Formicidae) functional morphology across urban parks.

Habitat fragmentation and consequent population isolation in urban areas can impose significant selection pressures on individuals and species confined to urban islands, such as parks. Despite many comparative studies on the diversity and structure of ant community living in urban areas, studies on ants' responses to these highly variable ecosystems are often based on assemblage composition and interspecific mean trait values, which ignore the potential for high intraspecific functional trait variation among individuals. Here, we examined differences in functional traits among populations of the generalist ant Pheidole nodus fragmented between urban parks. We used pitfall trapping, which is more random and objective than sampling colonies directly, despite a trade-off against sample size. We then tested whether trait-filtering could explain phenotypic differences among urban park ant populations, and whether ant populations in different parks exhibited different phenotypic optima, leading to positional shifts in anatomical morphospace through the regional ant meta-population. Intraspecific morphological differentiation was evident across this urban region. Populations had different convex hull volumes, positioned differently over the morphospace. Fragmentation and habitat degradation reduced phenotypic diversity and, ultimately, changed the morphological optima of populations in this urban landscape. Considering ants' broad taxonomic and functional diversity and their important role in ecosystems, further work over a variety of ant taxa is necessary to ascertain those varied morphological response pathways operating in response to population segregation in urban environments.

Forest conversion into pasture selects dung beetle traits at different biological scales depending on species pool composition.

The conversion of forests into open areas has large effects on the diversity and structure of native communities. The intensity of these effects may vary between regions, depending on the existence of native species adapted to open habitats in the regional pool or the time since habitat change.We assess the differences in species richness and functional diversity of dung beetle communities (Coleoptera: Scarabaeinae) between native forests and novel pasturelands of the Atlantic Forest and the Cerrado, two biomes with contrasting histories of human occupation in Brazil. We conducted standardized surveys in seven forest fragments and adjacent pastures in each region and measured 14 traits in individuals collected in each type of habitat at each particular site. We calculated functional richness, functional evenness, functional divergence, and community-weighted mean of traits for each area, and analyzed individual variation through nested variance decomposition and Trait Statistics.Communities were richer and more numerous at the Cerrado. We did not find any consistent relationship between functional diversity and forest conversion beyond the changes in species diversity. Although landscape changes were more recent at the Cerrado, the colonization of the new habitat by native species already adapted to open habitats lessens the functional loss in this biome. This indicates that habitat change's effects on trait diversity depend on the regional species pool rather than on time since land conversion.Forest conversion effects were primarily due to internal filtering. The effects of external filtering only appear at the intraspecific variance level, with contrasting differences between the Cerrado, where traits related to relocation behavior and size are selected, and the Atlantic Forest, where selection operates for traits related to relocation behavior and flight. These results evidence the importance of considering individual variance to address the responses of dung beetle communities to forest conversion.

A novel method to estimate adult age from the lumbar vertebral body using 3D PMCT images in Japanese

This study evaluated the age-related changes in the vertebral body using 3D Postmortem CT (PMCT) images and proposed an alternative age estimation formula. The PMCT images of 200 deceased individuals aged 25 to 99 years (126 males, 74 females) were retrospectively reviewed and included in the study. Using the open-source software ITK-SNAP and MeshLab, a 3D surface mesh of the fourth lumbar vertebral body (L4) and its convex hull models were created from the PMCT data. Using their inbuilt tools, volumes (in mm3) of the L4 surface mesh and convex hull models were subsequently computed. We derived VD, defined as the difference in volumes between the convex hull and L4 surface mesh normalized by L4 mesh volume, and VR, defined as the ratio of L4 mesh volume to convex hull volume based on individual L4. Correlation and regression analyses were performed between VD, VR, and chronological age. A statistically significant positive correlation (P < 0.001) between chronological age and VD, (rs = 0.764, males; rs = 0.725, females), and a significant negative correlation between chronological age and VR (rs = -0.764, males; rs = -0.725, females) was obtained in both sexes. The lowest standard error of the estimate was demonstrated by the VR at 11.9 years and 12.5 years for males and females, respectively. As such, their regression models to estimate adult age were Age = 248.9–2.5VR years, males; Age = 258.1–2.5VR years, females. These regression equations may be useful for estimating age in Japanese adults in forensic settings.

Root system architecture and environmental flux analysis in mature crops using 3D root mesocosms.

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft3 (1.27 m3), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.

The tightest knot is not necessarily the smallest

In this paper, we attempt to find counterexamples to the conjecture that the ideal form of a knot, that which minimizes its contour length while respecting a no-overlap constraint, also minimizes the volume of the knot, as determined by its convex hull. We measure the convex hull volume of knots during the length annealing process, identifying local minima in the hull volume that arise due to buckling and symmetry breaking. We use [Formula: see text] torus knots as an illustrative example of a family of knots whose locally minimal-length embeddings are not necessarily ordered by volume. We identify several knots whose central curve has a convex hull volume that is not minimized in the ideal configuration, and find that [Formula: see text] has a non-ideal global minimum in its convex hull volume even when the thickness of its tube is taken into account.

Split-based sequential sampling for realtime security assessment

Machine learning (ML) for real-time security assessment requires a diverse training database to be accurate for scenarios beyond historical records. Generating diverse operating conditions is highly relevant for the uncertain future of emerging power systems that are completely different to historical power systems. In response, for the first time, this work proposes a novel split-based sequential sampling approach based on optimisation that generates more diverse operation scenarios for training ML models than state-of-the-art approaches. This work also proposes a volume-based coverage metric, the convex hull volume (CHV), to quantify the quality of samplers based on the coverage of generated data. This metric accounts for the distribution of samples across multidimensional space to measure coverage within the physical network limits. Studies on IEEE test cases with 6, 68 and 118 buses demonstrate the efficiency of the approach. Samples generated using the proposed split-based sampling cover 37.5% more volume than random sampling in the IEEE 68-bus system. The proposed CHV metric can assess the quality of generated samples (standard deviation of 0.74) better than a distance-based coverage metric which outputs the same value (standard deviation of <0.001) for very different data distributions in the IEEE 68-bus system. As we demonstrate, the proposed split-based sampling is relevant as a pre-step for training ML models for critical tasks such as security assessment.

Remotely sensed carbon content: The role of tree composition and tree diversity

Optical remote sensing permits modeling of variables related to forest biomass, which is a critical determinant of carbon (C) stocks and fluxes. Plant functional characteristics can be captured by (hyper)spectral data, but it remains unclear whether the links between spectral information and C content are driven largely by tree composition, tree diversity, or by forest attributes not commonly measured in field inventories (e.g., physical canopy structure). Here, we examine the relationship between hyperspectral reflectance and aboveground C content in forests, testing the relative importance of tree composition and diversity in mediating this relationship. We use hyperspectral imaging data from an airborne survey with precisely geo-located field data on canopy trees in two forests in southern Québec, Canada. Spectral data covering visible (VIS), near infrared (NIR) and short-wave infrared (SWIR) wavelengths were extracted for 2626 tree crowns within 64 field plots distributed along an elevational gradient. We applied a continuum removal to the spectra and subsequently performed a principal component analysis to reduce the spectral dimensionality. From the spectral principal components, for each plot we quantified (i) spectral composition (based on average reflectance per plot), and (ii) spectral diversity using the convex hull volume. From field data, we calculated variables characterizing the taxonomic, functional and phylogenetic composition and diversity of canopy trees. Carbon content was calculated using allometric equations based on tree size. We applied a structural equation model based on partial-least squares to test both indirect effects of spectral composition and diversity on C storage of trees (via on-the-ground tree composition or diversity), and also direct effects (reflecting forest characteristics unmeasured in field surveys). We found that spectral composition, particularly from the VIS, is related to C content largely indirectly, via changes in tree composition along the elevational gradient (a transition from deciduous to coniferous species with increasing elevation). Though spectral diversity was significantly related to tree species diversity, no direct or indirect effects on C content were detected. Overall, our findings support (i) the importance of tree composition (but not diversity) in mediating the link between hyperspectral data and forest C content, and (ii) the use of hyperspectral remote sensing as an effective surrogate of taxonomic, functional, and phylogenetic composition of tree communities with strong links to C storage.