Estimation of Over- and Under-Bark Volume of Scots Pine Timber Produced by Harvesters

Harvesters produce one third of timber in Czechia. The aim of this study was to analyze the over- and under-bark volume estimates of Scots pine (Pinus sylvestris L.) timber produced by a mid-performance harvester. The data were collected between March 2017 and June 2018. In total, 4661 stems cut into 29,834 logs were analyzed. For volume estimation, StanForD offers several price categories using various algorithms. Three of these price categories are relevant for Czech forestry—M3s, M3toDE, and M3miDE. The M3s price category is based on the estimation of partial volumes of 10 cm long sections, which are summed. Therefore, this price category represents the volume estimation closest to the true volume. By comparison, the M3toDE and M3miDE price categories use the same algorithm for volume estimation, which is based on the Huber formula using a midspan diameter rounded down to the nearest whole centimeter. The M3toDE price category underestimated the over-bark volume by 6.48% compared to the reference price category M3s. The mean log volume estimated through the M3s price category was significantly higher than the M3toDE volume both in individual grades and without grading. We found significant differences between under-bark volume estimates by the diameter band bark deduction method (DBM) and the parametric linear bark deduction method (PLM) used in harvester’s systems according to the Guidelines for Electronic Scaling of Timber for Harvesters in Czechia (GEH) for Scots pine butt logs with rough bark, and also for other logs with normal bark thickness. To obtain under-bark volume estimates of Scots pine timber that are comparable with the Guidelines for Timber Scaling in Czechia (GTS) using the parametric nonlinear bark deduction method (PNM), we recommend using the algorithm of the M3toDE price category, with double bark thickness determined by the diameter band bark deduction method.

The effect of depth context in the segmentation of the colon in MRI volumes

ABSTRACTColonic volume content measurements can provide important information about the digestive tract physiology. Development of automated analyses will accelerate the translation of these measurements into clinical practice. In this paper, we test the effect of data dimension on the success of deep learning approaches to segment colons from MRI data. Deep learning network models were developed which used either 2D slices, complete 3D volumes and 2.5D partial volumes. These represent variations in the trade-off between the size and complexity of a network and its training regime, and the limitation of only being able to use a small section of the data at a time: full 3D networks, for example, have more image context available for decision making but require more powerful hardware to implement. For the datasets utilised here, 3D data was found to outperform 2.5D data, which in turn performed better than 2D datasets. The maximum Dice scores achieved by the networks were 0.898, 0.834 and 0.794 respectively. We also considered the effect of ablating varying amounts of data on the ability of the networks to label images correctly. We achieve dice scores of 0.829, 0.827 and 0.389 for 3D single slices ablation, 3D multi-slice ablation and 2.5D middle slice ablation.In addition, we examined another practical consideration of deep learning, that of how well a network performs on data from another acquisition device. Networks trained on images from a Philips Achieva MRI system yielded Dice scores of up to 0.77 in the 3D case when tested on images captured from a GE Medical Systems HDxt (both 1.5 Tesla) without any retraining. We also considered the effect of single versus multimodal MRI data showing that single modality dice scores can be boosted from 0.825 to 0.898 when adding an extra modality.

Curved interface reconstruction for 2D compressible multi-material flows

In this paper, we describe an interface reconstruction method in two dimension. This method is an extension of DPIR [1], which reconstructs continuous interfaces and preserves partial volumes using dynamic programming. First we extend the method to curved interfaces. Then, we present tools to improve its robustness in order to apply it to unstructured grid. Finally, we describe an extension to three materials.

Dalton’s and Amagat’s laws fail in gas mixtures with shock propagation

A shock propagating through a gas mixture leads to pressure, temperature, and density increases across the shock front. Rankine-Hugoniot relations correlating pre- and post-shock quantities describe a calorically perfect gas but deliver a good approximation for real gases, provided the pre-shock conditions are well characterized with a thermodynamic mixing model. Two classic thermodynamic models of gas mixtures are Dalton’s law of partial pressures and Amagat’s law of partial volumes. We measure post-shock temperature and pressure in experiments with nonreacting binary mixtures of sulfur hexafluoride and helium (two dramatically disparate gases) and show that neither model can accurately predict the observed values, on time scales much longer than that of the shock front passage, due to the models’ implicit assumptions about mixture behavior on the molecular level. However, kinetic molecular theory can help account for the discrepancy. Our results provide starting points for future theoretical work, experiments, and code validation.

Three-dimensional evaluation of changes in upper airway volume in growing skeletal Class II patients following mandibular advancement treatment with functional orthopedic appliances

ABSTRACT Objectives: The aim of this study was to assess three-dimensionally the upper airway changes following functional appliance treatment in growing Class II patients. Materials and Methods: Pre-and post-treatment Cone beam computed tomography scans of 20 patients (age range: 9 to 12; mean: 11.4 ± 1.0 years) were retrieved from the list of patients previously treated with functional appliances in the Postgraduate Clinic at the Section of Orthodontics, Aarhus University, Denmark. Total and partial volumes of the upper airway (ie, lower nasopharynx, velopharynx, and oropharynx) were calculated. To rule out the effect of growth, the changes in the functional appliance group were compared to an age-matched Class I group of 18 patients (age range: 8 to 14; mean: 11.8 ± 1.4 years). Results: In the functional appliance group, all the partial and total volumes were significantly larger at the end of treatment when compared to the start of treatment (P < .003). On the other hand, when comparing the changes for the total and partial volumes of the upper airway in the functional appliance group with the Class I group, a statistical difference was seen only for the oropharynx (P = .022) and total volume (P = .025), with the functional appliance group showing a larger volume increment. Conclusions: An increase in the upper airway volume was found after treatment with functional appliances. This difference was mainly related to the changes at the oropharynx level, which differed significantly from what was observed in the Class I group.

Numerical Simulation of an Injection Microscale Calorimeter to Identify Significant Thermal Processes and Verify Data Reduction Procedures

A numerical solution based on a varying node volume for the mass diffusion, thermal response during a dilution experiment of a reacting mixture was performed. The unique feature of the solution was its use of partial volumes and incorporating the changes in heat transfer area and internal node volume changes. The limiting behavior of these transient responses was in agreement with expected analytical results. The resulting temperature and concentration responses were used with a typical EOT calibration relation to estimate its response during an experiment. The calculated EOT and temperature time response exhibit the same trends as that observed experimentally.

Functional morphometry demonstrates extraocular muscle compartmental contraction during vertical gaze changes

Anatomical studies demonstrate selective compartmental innervation of most human extraocular muscles (EOMs), suggesting the potential for differential compartmental control. This was supported by magnetic resonance imaging (MRI) demonstrating differential lateral rectus (LR) compartmental contraction during ocular counterrolling, differential medial rectus (MR) compartmental contraction during asymmetric convergence, and differential LR, inferior rectus (IR), and superior oblique (SO) compartmental contraction during vertical vergence. To ascertain possible differential compartmental EOM contraction during vertical ductions, surface coil MRI was performed over a range of target-controlled vertical gaze positions in 25 orbits of 13 normal volunteers. Cross-sectional areas and partial volumes of EOMs were analyzed in contiguous, quasi-coronal 2-mm image planes spanning origins to globe equator to determine morphometric features correlating best with contractility. Confirming and extending prior findings for horizontal EOMs during horizontal ductions, the percent change in posterior partial volume (PPV) of vertical EOMs from 8 to 14 mm posterior to the globe correlated best with vertical duction. EOMs were then divided into equal transverse compartments to evaluate the effect of vertical gaze on changes in PPV. Differential contractile changes were detected in the two compartments of the same EOM during infraduction for the IR medial vs. lateral (+4.4%, P = 0.03), LR inferior vs. superior (+4.0%, P = 0.0002), MR superior vs. inferior (−6.0%, P = 0.001), and SO lateral vs. medial (+9.7%, P = 0.007) compartments, with no differential contractile changes in the superior rectus. These findings suggest that differential compartmental activity occurs during normal vertical ductions. Thus all EOMs may contribute to cyclovertical actions.