Helios: A Scalable 3D Plant and Environmental Biophysical Modeling Framework.

This article presents an overview of Helios, a new three-dimensional (3D) plant and environmental modeling framework. Helios is a model coupling framework designed to provide maximum flexibility in integrating and running arbitrary 3D environmental system models. Users interact with Helios through a well-documented open-source C++ API. Version 1.0 comes with model plug-ins for radiation transport, the surface energy balance, stomatal conductance, photosynthesis, solar position, and procedural tree generation. Additional plug-ins are also available for visualizing model geometry and data and for processing and integrating LiDAR scanning data. Many of the plug-ins perform calculations on the graphics processing unit, which allows for efficient simulation of very large domains with high detail. An example modeling study is presented in which leaf-level heterogeneity in water usage and photosynthesis of an orchard is examined to understand how this leaf-scale variability contributes to whole-tree and -canopy fluxes

Semi-direct tree reconstruction using terrestrial LiDAR point cloud data

A new method was developed for reconstructing the geometric structure of large plants such as trees at the leaf-scale by utilizing terrestrial LiDAR data. The primary goal of the work was to develop a feasible means for accurately and rapidly reconstructing or “digitizing” entire trees in order to specify the position, orientation, and size of every leaf in digital tree models that provide geometric inputs for high-resolution biophysical models or analyses. As with any optical measurement technique, a primary challenge is accurately accounting for plant matter that is occluded from view of the sensor. The present method is termed “semi-direct” because it uses a triangulation procedure to approximately directly reconstruct as many leaves as possible that are in view of the scanner. For plant matter obstructed from view, a statistical backfilling procedure was used to add additional leaves such that the three-dimensional distribution of leaf area and orientation of the reconstructed plant matched that of the actual plant on average. In a best case scenario such as when leaf density is low, nearly all leaf area is directly reconstructed from the scan and the branch and clumping structure is preserved within the reconstruction. In the worst case scenario such as when the leaf density is very high and nearly all leaves are occluded from view of the scanner, only a small fraction of leaves can be directly reconstructed, but at a minimum the distribution of leaf area and the leaf angle distribution across the reconstructed plant will be consistent with that of the actual plant. Unlike many other approaches, the present method does not rely on the woody matter of the plant to provide a skeleton for reconstruction, and can be used in dense plants where little woody matter is visible from the scanner

Fusion-Driven Tree Reconstruction and Fruit Localization: Advancing Precision in Agriculture

Fruit distribution is pivotal in shaping the future of both agriculture and agricultural robotics, paving the way for a streamlined supply chain. This study introduces an innovative methodology that harnesses the synergy of RGB imagery, LiDAR, and IMU data, to achieve intricate tree reconstructions and the pinpoint localization of fruits. Such integration not only offers insights into the fruit distribution, which enhances the precision of guidance for agricultural robotics and automation systems, but also sets the stage for simulating synthetic fruit patterns across varied tree architectures. To validate this approach, experiments have been carried out in both a controlled environment and an actual peach orchard. The results underscore the robustness and efficacy of this fusion-driven methodology, highlighting its potential as a transformative tool for future agricultural robotics and precision farming.Comment: This work was presented at IEEE/RSI International Conference on Intelligent Robots and Systems (IROS) Worksho

The Influence of Citrus urantium and Caffeine Complex versus Placebo on the Cardiac Autonomic Response: A Double Blind Crossover Design

Background: The purpose of this study was to examine the resting cardiac autonomic nervous system’s response to the ingestion of a complex containing Citrus aurantium + Caffeine (CA + C) and its influence on recovery following a high-intensity anaerobic exercise bout in habitual caffeine users. Methods: Ten physically active males (25.1 ± 3.9 years; weight 78.71 ± 9.53 kg; height 177.2 ± 4.6 cm; body fat 15.5 ± 3.13%) participated in this study, which consisted of two exhaustive exercise protocols in a randomized crossover design. On each visit the participants consumed either a CA + C (100 mg of CA and 100 mg of C) or placebo (dextrose) capsule. After consumption, participants were monitored throughout a 45-min ingestion period, then completed a repeated Wingate protocol, and were then monitored throughout a 45-min recovery period. Cardiac autonomic function (Heart Rate (HR) and Heart Rate Variability (HRV)) and plasma epinephrine (E) and norepinephrine(NE) were taken at four different time points; Ingestion period: baseline (I1), post-ingestion period (I2); Recovery period: immediately post-exercise (R1), post-recovery period (R2). Heart rate variability was assessed in 5-min increments. Results: A repeated measures ANOVA revealed significant time-dependent increases in HR, sympathetic relatedmarkers of HRV, and plasma E and NE at I2 only in the CA + C trial (p\u3c 0.05); however, no meaningful changes in parasympathetic markers of HRV were observed. Participants recovered in a similar time-dependent manner in all markers of HRV and catecholamines following the PLA and CA + C trials. Conclusion: The consumption of CA + C results in an increase of sympathetic activity during resting conditions without influencing parasympathetic activity. CA + C provides no influence over cardiac autonomic recovery

DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots

In agricultural environments, viewpoint planning can be a critical functionality for a robot with visual sensors to obtain informative observations of objects of interest (e.g., fruits) from complex structures of plant with random occlusions. Although recent studies on active vision have shown some potential for agricultural tasks, each model has been designed and validated on a unique environment that would not easily be replicated for benchmarking novel methods being developed later. In this paper, hence, we introduce a dataset for more extensive research on Domain-inspired Active VISion in Agriculture (DAVIS-Ag). To be specific, we utilized our open-source "AgML" framework and the 3D plant simulator of "Helios" to produce 502K RGB images from 30K dense spatial locations in 632 realistically synthesized orchards of strawberries, tomatoes, and grapes. In addition, useful labels are provided for each image, including (1) bounding boxes and (2) pixel-wise instance segmentations for all identifiable fruits, and also (3) pointers to other images that are reachable by an execution of action so as to simulate the active selection of viewpoint at each time step. Using DAVIS-Ag, we show the motivating examples in which performance of fruit detection for the same plant can significantly vary depending on the position and orientation of camera view primarily due to occlusions by other components such as leaves. Furthermore, we develop several baseline models to showcase the "usage" of data with one of agricultural active vision tasks--fruit search optimization--providing evaluation results against which future studies could benchmark their methodologies. For encouraging relevant research, our dataset is released online to be freely available at: https://github.com/ctyeong/DAVIS-AgComment: 8 pages, 5 figures, 4 table

QES-Fire: A dynamically coupled fast-response wildfire model

A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model\u27s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire\u27s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data

The structure and function of complex networks

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.Comment: Review article, 58 pages, 16 figures, 3 tables, 429 references, published in SIAM Review (2003

Effects of Heat Exposure on Body Water Assessed using Single-Frequency Bioelectrical Impedance Analysis and Bioimpedance Spectroscopy

International Journal of Exercise Science 10(7): 1085-1093, 2017. The purpose of this study was to determine if heat exposure alters the measures of total body water (TBW), extracellular water (ECW), and intracellular water (ICW) in both single-frequency bioelectrical impedance analysis (BIA) and bioimpedance spectroscopy (BIS). Additionally, we sought to determine if any differences exist between the BIA and BIS techniques before and after brief exposure to heat. Body water was evaluated for twenty men (age=24±4 years) in a thermoneutral environment (22°C) before (PRE) and immediately after (POST) 15 min of passive heating (35°C) in an environmental chamber. The mean difference and 95% limits of agreement at PRE demonstrated that BIS yielded significantly higher body water values than BIA (all p0.05; 0.2±1.5kg). Additionally, the ES of the mean differences at POST were trivial to small and the r-values were high (r≥0.96). When analyzing the changes in body water before and after heat exposure, POST values for BIS were significantly higher than PRE (all

Alterations in Platelet Function and Cell-Derived Microvesicles in Recently Menopausal Women: Relationship to Metabolic Syndrome and Atherogenic Risk

A woman’s risk for metabolic syndrome (MS) increases at menopause, with an associated increase in risk for cardiovascular disease. We hypothesized that early menopause-related changes in platelet activity and concentrations of microvesicles derived from activated blood and vascular cells provide a mechanistic link to the early atherothrombotic process. Thus, platelet functions and cellular origin of blood-borne microvesicles in recently menopausal women (n = 118) enrolled in the Kronos Early Estrogen Prevention Study were correlated with components of MS and noninvasive measures of cardiovascular disease [carotid artery intima medial thickness (CIMT), coronary artery calcium (CAC) score, and endothelial reactive hyperemic index (RHI)]. Specific to individual components of the MS pentad, platelet number increased with increasing waist circumference, and platelet secretion of ATP and expression of P-selectin decreased with increasing blood glucose (p = 0.005) and blood pressure (p < 0.05), respectively. Waist circumference and systolic blood pressure were independently associated with monocyte- and endothelium-derived microvesicles (p < 0.05). Platelet-derived and total procoagulant phosphatidylserine-positive microvesicles, and systolic blood pressure correlated with CIMT (p < 0.05), but not with CAC or RHI. In summary, among recently menopausal women, specific platelet functions and concentrations of circulating activated cell membrane-derived procoagulant microvesicles change with individual components of MS. These cellular changes may explain in part how menopause contributes to MS and, eventually, to cardiovascular disease