Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data De-Rived from sUAS Imagery

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of red-cockaded woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

Graphitisation of Waste Carbon Powder with Femtosecond Laser Annealing

Graphitisation of structural characteristics and improvement in electrical conductivity was reported onto waste carbon powder through femtosecond laser annealing. Raman spectroscopy on the carbon powder pre- and post-annealing showed a shift from amorphous-like carbon to graphitic-like carbon, which can be explained by the three-stage model. Electrical I-V probing of the samples revealed an increase in conductivity by up to 90%. An increase in incident laser power was found to be correlated to an increase in conductivity. An average incident laser power of 0.104 W or less showed little to no change in electrical characteristics, while an average incident laser power of greater than 1.626 W had a destructive effect on the carbon powder, shown through the reduction in powder. The most significant improvement in electrical conductivity has been observed at laser powers ranging from 0.526 to 1.286 W. To conclude, the graphitisation of waste carbon powder is possible using post-process femtosecond laser annealing to alter its electrical conductivity for future applications.

Revisiting silk: a lens-free optical physical unclonable function

For modern security, devices, individuals, and communications require unprecedentedly unique identifiers and cryptographic keys. One emerging method for guaranteeing digital security is to take advantage of a physical unclonable function. Surprisingly, native silk, which has been commonly utilized in everyday life as textiles, can be applied as a unique tag material, thereby removing the necessary apparatus for optical physical unclonable functions, such as an objective lens or a coherent light source. Randomly distributed fibers in silk generate spatially chaotic diffractions, forming self-focused spots on the millimeter scale. The silk-based physical unclonable function has a self-focusing, low-cost, and eco-friendly feature without relying on pre-/post-process for security tag creation. Using these properties, we implement a lens-free, optical, and portable physical unclonable function with silk identification cards and study its characteristics and reliability in a systemic manner. We further demonstrate the feasibility of the physical unclonable functions in two modes: authentication and data encryption.

Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1

Operational forecasting of volcanic ash and SO2 clouds is challenging due to the large uncertainties that typically exist on the eruption source term and the mass removal mechanisms occurring downwind. Current operational forecast systems build on single-run deterministic scenarios that do not account for model input uncertainties and their propagation in time during transport. An ensemble-based forecast strategy has been implemented in the FALL3D-8.1 atmospheric dispersal model to configure, execute, and post-process an arbitrary number of ensemble members in a parallel workflow. In addition to intra-member model domain decomposition, a set of inter-member communicators defines a higher level of code parallelism to enable future incorporation of model data assimilation cycles. Two types of standard products are automatically generated by the ensemble post-process task. On one hand, deterministic forecast products result from some combination of the ensemble members (e.g., ensemble mean, ensemble median, etc.) with an associated quantification of forecast uncertainty given by the ensemble spread. On the other hand, probabilistic products can also be built based on the percentage of members that verify a certain threshold condition. The novel aspect of FALL3D-8.1 is the automatisation of the ensemble-based workflow, including an eventual model validation. To this purpose, novel categorical forecast diagnostic metrics, originally defined in deterministic forecast contexts, are generalised here to probabilistic forecasts in order to have a unique set of skill scores valid to both deterministic and probabilistic forecast contexts. Ensemble-based deterministic and probabilistic approaches are compared using different types of observation datasets (satellite cloud detection and retrieval and deposit thickness observations) for the July 2018 Ambae eruption in the Vanuatu archipelago and the April 2015 Calbuco eruption in Chile. Both ensemble-based approaches outperform single-run simulations in all categorical metrics but no clear conclusion can be extracted on which is the best option between these two.

On the Recycling of Water Atomized Powder and the Effects on Properties of L-PBF Processed 4130 Low-Alloy Steel

The microstructure and mechanical properties of a 4130-grade steel processed by L-PBF using a feedstock of low-cost water atomized powder have been investigated considering the effects of powder recycling. Chemical analysis of the recycled powder showed a constant amount of alloying elements with a slight reduction in oxygen content. The as-built microstructure was mainly composed of a martensitic structure separated by a high fraction of low-angle grain boundaries, suggesting the application of a direct tempering treatment starting from the as-built condition as a cost-effective post-process thermal treatment rather than the conventional quench and tempering treatment. Moreover, the degree of anisotropy generated by L-PBF in as-built specimens could be reduced after performing either the direct tempering or the quench and tempering treatments. The possible degradation of powder properties on the steel performance was also investigated. After various powder recycling events, no significant deterioration in tensile properties was measured, indicating that the water atomized powder could be a sustainable feedstock candidate for L-PBF.

Post-Processing Ensemble Weather Forecasts for Introducing Multisite and Multivariable Correlations using Rank Shuffle and Copula Theory

Statistical methods have been widely used to post-process ensemble weather forecasts for hydrological predictions. However, most of the statistical post-processing methods apply to a single weather variable at a single location, thus neglecting the inter-site and inter-variable dependence structures of forecast variables. This study synthesized a multisite and multivariate (MSMV) post-processing framework that extends the univariate method to the MSMV version by directly rearranging the post-processed ensemble members (post-reordering strategy) or by rearranging the latent variables used in univariate method (pre-reordering strategy). Based on the univariate Generator-based Post-Processing (GPP) method, the two reordering strategies and three dependence reconstruction methods (Rank shuffle (RS), Gaussian Copula (GC), and Empirical Copula (EC)) totaling 6 MSMV methods (RS-Pre, GC-Pre, EC-Pre, RS-Post, GC-Post, and EC-Post) were evaluated in post-processing ensemble precipitation and temperature forecasts for the Xiangjiang Basin in China using the 11-member ensemble forecasts from the Global Ensemble Forecasting System (GEFS). The results showed that raw GEFS forecasts tend to be biased for both the forecast ensembles and the inter-site and inter-variable dependencies. Univariate method can improve the univariate performance of ensemble mean and spread but misrepresent the inter-site and inter-variable dependence among the forecast variables. The MSMV framework can well utilize the advantages of the univariate method and also reconstruct the inter-site and inter-variable dependencies. Among the six methods, RS-Pre, RS-Post, GC-Post, and EC-Post perform better than the others with respect to reproducing the univariate statistics and multivariable dependences. The post-reordering strategy is recommended to combine the univariate method (i.e. GPP) and reconstruction methods.

Multimodality as a Communicative Tool for Social Engagement and Reform

This article acknowledges the viability of multimodal projects in first-year college-level writing courses in accordance with the evolution of composition pedagogy over the past forty years. Since the 1982 publication of Hairston’s article “The Winds of Change” forecasting the end of the then-ubiquitous current-traditional approach, composition pedagogy has undergone paradigm shifts from process to post-process theory and from textual to digital modes of composition. Inspired by Goodwin’s (2020) research on students’ multimodal responses to local community issues, I developed a public media project for my first-year writing course for which students created media texts addressing local, regional, national, and global issues of their choosing. The project synthesizes the public and interpretative dimensions of writing identified by post-process scholars with elements of multimodality and civic engagement to help students understand how public media texts raise social awareness of current issues and mobilize community efforts toward unified resolution of such issues.

In-process range-resolved interferometric (RRI) 3d layer height measurements for wire + arc additive manufacturing (WAAM)

In this work a range resolved interferometry (RRI) instrument for absolute distance measurements is integrated into a wire + arc additive manufacturing (WAAM) system to provide in-process monitoring of layer height, and prospects for volume and profile monitoring are discussed. Interferometry as a coherent optical technique offers a straightforward in-process measurement even in the harsh welding environment, as compared to non-coherent techniques based either on laser profiling or camera vision systems. RRI can be accomplished at significantly lower cost, and with higher depth of field (up to 10s of cm) than existing optical coherence tomography based weld monitoring. In this experiment titanium feedstock was used to create a 150mm long, 13.5mm high weld-wall comprised of 11 welded layers. The RRI in-process measurements are in very good agreement with both mid-process, on-machine micrometer measurements taken by hand after each weld, and post-process laser scanning measurements of the completed wall. The high depth of field allows direct referencing of the layer height measurements to the build plate making the measurement independent of the motion system and build plate bending, considerably lowering uncertainties. This, together with the capability for cost-effective in-process measurements in harsh environments, should make the proposed approach very interesting for routine use in WAAM systems.

Influence of LPBF-Surface Characteristics on Fatigue Properties of Scalmalloy®

Laser powder bed fusion (LPBF) has indisputable advantages when designing new components with complex geometries due to toolless manufacturing and the ability to manufacture components with undercuts. However, fatigue properties rely heavily on the surface condition. In this work, in-process surface parameters (three differing contour parameter sets) and post-process surface treatments, namely turning and shot peening, are varied to investigate the influence of each treatment on the resulting fatigue properties of LPBF-manufactured specimens of the aluminium–magnesium–scandium alloy Scalmalloy®. Therefore, metallographic analysis and surface roughness measurements, as well as residual stress measurements, computer tomography measurements, SEM-analyses, tensile and fatigue tests, along with fracture surface analysis, were performed. Despite the fact that newly developed in-process contour parameters are able to reduce the surface roughness significantly, only a minor improvement in fatigue properties could be observed: Crack initiation is caused by sharp, microscopic notches at the surface in combination with high tensile residual stresses at the surface, which are present on all in-process contour parameter specimens. Specimens using contour parameters with high line energy show keyhole pores localized in the subsurface area, which have no effect on crack initiation. Contours with low line energy have a slightly positive effect on fatigue strength because less pores can be found at the surface and subsurface area, which even more greatly promotes an early crack initiation. The post-process parameter sets, turning and shot peening, both improve fatigue behaviour significantly: Turned specimens show lowest surface roughness, while, for shot peened specimens, the tensile residual stresses of the surface radially shifted from the surface towards the centre of the specimens, which counteracts the crack initiation at the surface.